literature reviews on social media

Social Media and Higher Education: A Literature Review

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• Yuanqiong Wang 14 &
• Gabriele Meiselwitz 14  

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This paper presents a literature review of empirical research related to the use and effects of social media in higher education settings. The adoption of social media has been steadily increasing. However, a majority of the research reported focuses on students’ perception on the effects of social media in learning. The research on the effects of social media on student learning and faculty perspectives are still limited. This literature review focused on the empirical studies that involved the use of social media in higher education in the computing field. Recommendations for future research directions were presented as the result of this literature review.

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1 introduction.

The popularity of social media sites has been steadily increasing over the last few years, and over 70 % of online adults are now using a social networking site of some kind. Many users of social networking sites have more than one account, and check these accounts several times daily [ 6 ]. But even as social media has been widely adopted by many users, its use for higher education has also been questioned by educators. Although faculty in higher education often utilizes social networking sites in a professional context, many are reluctant to use social networking sites for teaching and learning. Moreover, even though computing faculty members may have more experience with the technology, their adoption of social media for teaching purpose has been at a lower rate comparing to faculty in other fields such as Humanities and Arts, Professions and Applied Sciences, and Social Sciences [ 6 , 20 ].

Web 2.0 (often referred to as the “social web”), with its many benefits such as social networking and user-generated content, has drawn much attention for teaching and learning [ 2 ]. Learning paradigms have shifted over the last decades from a traditional classroom setting to include online learning, e-learning, collaborative learning, and many hybrid forms. This shift indicates a move from instructor-led and instructor-centered learning environments to learner-centered environments, which focus on knowledge creation and building rather than knowledge transmission [ 3 , 5 ]. At first glance, Web 2.0 applications such as social networks, wikis, blogging, and micro blogging seem to be well suited for learner-centered environments, but a closer look reveals that the adoption of Web 2.0 technologies and applications in higher education learning is lagging behind the adoption of Web 2.0 technologies overall. Although roughly 90 % of young adults (18-29 years old) use some social network site, many faculty members also see limitations and potential problems with the use of online and interactive technologies in higher education [ 6 , 20 ]. In a survey, 56 % of faculty members stated that they see online and mobile technologies as more distracting than helpful to students for academic work [ 20 ].

Several studies have investigated the use of social media in higher education, many concentrating on the use of Facebook in their courses. Facebook still dominates the social media landscape, and is popular across a diverse mix of demographic profiles, but other sites have gained popularity and many users now participate in multiple networks [ 20 ]. However, the popularity of Facebook has prompted many educators to integrate some elements into their learning environments.

Some studies point out that it is an obligation to prepare students for what they will encounter once they graduate from college and enter the workplace [ 1 , 5 ]. Other studies examine the connection between social networking and informal and formal learning. Learning in a constructivist environment focuses on the individual learner and the situational context in which learning occurs, and the variety of options and tools that are available through social networking could support this type of situational learning. Students with different backgrounds, learning styles, and preferences can choose which tools they prefer for their individual learning process [ 19 ]. In addition, these technologies may create a higher level of student engagement that will build and support a community of scholars [ 9 , 12 , 23 ].

The majority of studies are experimental studies investigating specific social networking tools (e.g. MySpace, Facebook, Twitter) in specific settings (Business education, communication, medical school), and several studies focusing on pedagogy, learning outcomes, or teaching styles are emerging [ 19 ]. There is little discussion to date about some practical concerns for educators when integrating this technology into the higher education learning process. The fast pace in which technology changes, privacy and security concerns, intellectual property, accessibility for students with disabilities, or the increased workload for instructors have not received much attention [ 19 , 20 ]. Many educators are concerned about the short lifespan of certain applications. MySpace, for example, once the top site for young adults, is practically non-existent in the list of social networks used by this age group [ 6 ]. Moreover, it recently resorted to mass-mailing its former users to convince them to reactivate their still existing accounts [ 25 ]. Many young adults also have moved on from Facebook to other social networking sites, are participating in several sites, and check only their preferred site frequently [ 6 ].

The purpose of this paper is to review existing literature related to the use of social media in computing education at higher education level, the effects of social media on learning, and the concerns of adopting social media in learning. Empirical studies that focused on the use of social media for computer education in colleges, the effects of social media on student learning, and potential barriers of the social media adoption are presented in this paper. This literature review attempts to answer the following research questions:

Does social media lead to any improvement in higher education for learning computing related subjects?

What are the general objective benefits associated to the use of social media in higher education for learning computing related subjects?

What are the perceived benefits associated to the use of social media in higher education for learning computing related subjects?

What are the barriers or concerns that computer faculties have toward the use of social media in higher education for learning computing related subjects?

Literature focusing on the use of social media in higher education for computing subjects was collected and reviewed. The following online databases were utilized for the literature search: EBSCO, IEEE, ACM digital library.

The focus of the search was to gather full-text articles presenting empirical studies which involve the use of social media in higher education setting, especially the ones used for computing related subjects. To manage the scope and the comprehensiveness of the study, the following criteria were used to determine the inclusion of the paper for the review:

The study involved social media tools.

The study investigated the effects of the social media to students’ learning performance and behavior, the effects of the social media to students’ perception of learning process, the perception of the faculty members related to the use of social media.

The study focused on higher education preferably in computing related field. Therefore, studies conducted at K-12 level were excluded from this review.

The study must include a clear discussion on the research method utilized.

The study must be published between 2010 and 2014.

The paper must be written in English.

This section discusses the findings of this literature research. The findings are organized based on the key perspectives of the study.

3.1 Student Perspectives

Majority of the studies reviewed are focused on students’ perspectives of the social media use for instructional purpose, using various social media tools, such as Facebook, Blog, Wiki, and in-house social network tools, etc. Facebook has been the most frequently used site for the studies. This is consistent with the findings reported by Pearson’s social media for teaching and learning survey [ 20 ]. Based on a survey to 191 students in the use of Facebook for a closed group discussion, Gonzalez-Ramirez, Gasco, and Taverner [ 7 ] reported that students’ perceived weaknesses of Facebook in teaching included privacy issues, time required, and technological deficit; while the potential strengths that students predict include performance, communication, participation, and motivation. Students’ perceived usefulness of the tool and students’ learning achievements were the most frequently studied factors.

Student Perceived Learning Experience. In order to study the impact of social media in higher education setting, many researchers conducted explorative studies to investigate the students’ perceived learning experience [ 4 , 7 , 12 , 17 , 21 – 23 ]. Veletsianos and Navarrete [ 24 ] conducted a case study utilizing Elgg as the online social network in an online course, and investigated students’ perceived learning experience. The students reported to have overall enjoyed the experience. When being asked to compare the experience of using social network site (SNS) for class purpose to their previous experience of using traditional learning management systems (LMS), most students preferred SNS over LMS. However, when investigating further in terms of how students were using the tool, they noticed that there was limited participation to course related and graded activities, and little use for social networking and sharing purpose. In addition, students requested more support in managing the amount of information offered in SNS showing the potential of information overload by using SNS. While SNS provide more ways of communication and have the potential of accessing more resources, some students reported to have lacked the ability to effectively find and categorize content for future retrieval. However, all the findings from their study was based on students’ self-reported usage and perception. No investigation in terms of students’ actual usage of the site was done.

Li, Ganeshan and Xu [ 12 ] conducted an online survey to 300 students and a follow-up interview with nine of the respondents to investigate students’ preference of the communication tools and social networking sites. They reported that the students preferred Facebook in general. However, when it is time to discuss course related topics, Facebook is much less preferred than email. Some of the factors that affected the use of SNS for learning purpose included network speed, security and privacy.

Ozmen and Atici [ 17 ] conducted semi-structured interviews with 15 students in the use of LMS supported by SNS. They’ve incorporated Ning to support a Blackboard site. When being asked the overall perception of their learning experience, students responded that although Ning may have the potential to enhance the communication by using the chat tool, the overuse of chat actually lead to more distraction than to help them learn. Therefore, it is suggested that more pedagogical considerations need to be taken when incorporating SNS to the class environment. Finding the appropriate level of integration with the existing LMS and identify the appropriate activities may be the key to improve perceived learning experience.

In addition to general learning experience and preferences, several studies focused on the specific elements that may have the potential of affecting students learning by incorporating SNS to their classes. One of the common studied effect was the social support provided by SNS. For example, DeAndrea, Ellison, LaRose, Steinfield, and Fiore [ 4 ] presented an experiment they conducted that involved first year college students utilizing SpartanConnect, a social media site they designed, to study the effect of SNS in enhancing students’ perceptions of social support. Students were asked to create an account on the site before the semester started. The researchers then distributed pre-test survey after the first two weeks of classes to all first year students, and a post-test survey after using the site for the semester. Out of 1616 first year students who completed post-test survey, 265 students filled out both surveys. Higher level of perceived social support was reported after the use of the site.

Thoms, Eryilmaz and Gerbino [ 22 ] conducted a quasi-experiment in which students were asked to use an in-house online social network (OSN) to receive peer support recommendation. They also found that the use of OSN has improved students’ perceived level of course interaction and peer support, which in turn may lead to better learning.

Taylor [ 21 ] conducted a case study that investigated the possible effect of SNS on students’ retention rate in lower level computing class. The author reported to have seen an increased retention rate after the use of Facebook in CS1 class.

Unfortunately, although much previous research [ 21 , 22 ] reported improvements in students’ perceived learning experience and social support, there are also negative impacts reported. For example, Junco [ 9 ] conducted a survey to investigate the relationship between the use of Facebook and students’ engagement in learning. A negative relationship was reported between the self-reported frequency of Facebook use and students’ engagement. Based on the self-reported data, it shows a negative relationship between the frequency of engaging in Facebook chat and time spent preparing for class as well. This finding seems to be consistent with the study reported by Ozmen and Atici [ 17 ] that overuse of chat can become a distraction for learning since chat takes away the time that initially should have been allocated for study.

Student Learning Achievements. Unlike the studies of the impact on students’ perceived learning experience, the actual learning achievements were not investigated as heavily. Laru, Naykki, Jarvela [ 11 ] conducted a case study that involved 21 students work in groups of four to five for 12 weeks to complete a wiki project. A number of social media tools were introduced to the students, such as ShoZu, Flickr, Google Reader Mobile, Wordpress.com, Wikispaces, FeedBlendr, and FeedBurner RSS. Data was captured by using video recordings, social software usage activity and pre-and post-tests of students’ conceptual understanding of the materials. The comparison between the pre- and post- conceptual knowledge test showed an improvement in test scores received. Looking into more detail in terms of the relationship between the actual activities and the learning outcome, the researchers reported that the higher level of wiki-related activities was an indicator for determining the students improved scores.

Hernandez et al. [ 8 ] conducted an experiment to investigate the impact of different tools supporting students’ learning and perception of interaction. The students were assigned into groups that used Facebook with wiki-style document creation and wall/comment feature, Google Docs, or LMS discussion forum. After comparing the level of activities in each group setting and their final product, it was reported that the number of messages posted was higher in SNS compared to those using traditional LMS forum, time between messages posted was shorter in Facebook compared to other groups, and groups using Facebook also reported higher level of perceived interaction. However, the final result was the same among all groups. No effect in the learning outcome was reported when comparing groups using different tools for group communication.

Instead of an objective measure of the students’ learning achievement while SNS was used, many studies either overlooked the impact of SNS for learning outcomes or used only students’ self-reported data for this purpose [ 9 , 11 ]. More research is needed to look into the impact of SNS on student learning in addition to the impact on student learning experience.

Student SNS Usage Pattern. In addition to the potential impact of SNS for learning, the patterns of the posts in different tools were also investigated. Maleko et al. [ 13 ] reported their findings based on a case study conducted that compares Facebook and Blackboard usage by the students. They reported that posts in Facebook were unique in that they were concentrated in the expression of dissatisfaction, course admin, encouragement, discussions outside programming, and general advice; while the posts in Blackboard tend to be more for the purpose of community building and question to the lecturer. In addition, students reported to have preferred the use of Facebook for learning support where no authoritative figure was present.

Kear et al. [ 10 ] conducted a survey after students were asked to use an in-house wiki to co-edit a document after completing an online tutorial of how the wiki can be used. They collected data about students’ use of wiki over time. The authors reported decreased use overtime although students learned how to use wiki. After further investigation through the survey instrument, they found that the students were unhappy about editing other students’ work. When being asked to compare wiki and traditional discussion forum for this kind of collaborative activities, students preferred forum over wiki.

3.2 Faculty Perspectives

Unlike the studies focused on students’ perspectives, only a handful of studies that we investigated looked into the SNS use from faculty members’ perspectives.

Faculty Perception. Faculty perception of the SNS use for learning purpose has been mixed comparing to students perception. The survey conducted by Pearson [ 20 ] indicated that one of the reasons for faculty members not incorporating SNS in their teaching was because they consider the use of SNS as a distraction. Roblyer et al. [ 18 ] reported different perceptions of the faculty members compared to students. They found that students are more open to use Facebook when comparing to email for the communication purpose. Faculty members were more prone to traditional technologies, such as email. Brown also did a survey and a follow-up more in depth interview with the faculty members regarding the use of Web 2.0 technologies for learning purposes. The responses received from the faculty members indicated that promoting active student participation, enhancing distribution of and access to tutor-selected or generated learning content are the potential benefits indicated.

Faculty Concerns. The hesitance of faculty members regarding the use of SNS in classrooms may be explained by the concerns identified by previous studies. For example, Brown [ 2 ] reported that misalignments between the increasing amount of collaborative group work expected and continuing individual assessment, no “added value” to teaching, and too many constraints (because of the university policy) are the major concerns from faculty members. Kear et al. [ 10 ] reported that, in addition to the above mentioned concerns, performance of the SNS used, the difficulties in marking and monitoring students’ work, workload issues are also among the concerns raised by the teaching staff members.

4 Discussion

The literature review has shown some empirical findings that this paper attempted to investigate. In terms of the question regarding whether social media lead to any improvement in higher education for learning computing related subjects, the literature shows some evidence of improvement. Although the empirical study is extremely limited in gathering objective performance data to showcase the improvement of learning, the student self-reported data shows promising potential of the effectiveness of SNS use in higher education. In addition, literature indicated that careful pedagogical consideration needs to be made in order to ensure the effective use of SNS. However, more investigation is definitely needed.

The general objective benefits associated to the use of social media in higher education for learning computing related subjects has been identified by case studies and survey data. The identified benefits included improved social support, improved retention rate through peer support, and improved perceived interaction. However, the empirical study also showed that there can be negative impact of the use of SNS to the students’ engagement in learning.

When answering the perceived benefits associated with the use of social media in higher education for learning computing related subjects, the answers from students and faculty members are similar. Students tend to enjoy the activities using SNS considering it to help improve the interaction, and motivation to learning. In addition to the benefits identified by students, faculty members value the possibility of enhancing distribution of and access to tutor-selected or generated learning content through the use of SNS.

The specific concerns from computer faculty members were not identified from the literature review. However, the investigation of the literature shows that there is a list of potential concerns that are common for most of the faculty members. In general, faculty members share similar concerns as the students which include security, privacy and performance of the site/tool being used. In addition, faculty members are also concerned about the work load issue, the difficulty of performance evaluation and monitoring, and need for careful pedagogical design when it comes to the use of SNS for learning. Faculty members in computing field may be more concerned about the potential distraction of SNS and its security issues because of their familiarity of the technology. However, this was not identified in the literature and further investigation is definitely needed.

5 Recommendations and Future Research

Even though the literature review shows potential of social media usage for learning purpose, the use of the technology is still limited and not many controlled evaluations and in-depth studies in higher education settings have been conducted. First, more empirical study is needed to investigate the actual “added” benefits of SNS comparing to the use of traditional LMS. One of the major limitations of current literature is that most of the studies focused on self-report data to study the effect of the technology. Therefore, the actual usage and learning outcome should be addressed and investigated in more depth.

Although computing faculty members may know the technology better than faculty members in other field, their adoption of SNS is lagging behind. Is there any specific reason? Is it because of the nature of the topic that is sometimes hard to describe in texts? Is it because of the higher security concern from the faculty members? More investigation is needed to address this issue.

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Wang, Y., Meiselwitz, G. (2015). Social Media and Higher Education: A Literature Review. In: Meiselwitz, G. (eds) Social Computing and Social Media. SCSM 2015. Lecture Notes in Computer Science(), vol 9182. Springer, Cham. https://doi.org/10.1007/978-3-319-20367-6_11

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Research Article

Social impact in social media: A new method to evaluate the social impact of research

Roles Investigation, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Journalism and Communication Studies, Universitat Autonoma de Barcelona, Barcelona, Spain

Affiliation Department of Psychology and Sociology, Universidad de Zaragoza, Zaragoza, Spain

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing

Affiliation Department of Sociology, Universitat Autonoma de Barcelona, Barcelona, Spain

Affiliation Department of Sociology, Universitat de Barcelona (UB), Barcelona, Spain

• Cristina M. Pulido, 
• Gisela Redondo-Sama, 
• Teresa Sordé-Martí, 
• Ramon Flecha

• Published: August 29, 2018
• https://doi.org/10.1371/journal.pone.0203117
• Reader Comments

The social impact of research has usually been analysed through the scientific outcomes produced under the auspices of the research. The growth of scholarly content in social media and the use of altmetrics by researchers to track their work facilitate the advancement in evaluating the impact of research. However, there is a gap in the identification of evidence of the social impact in terms of what citizens are sharing on their social media platforms. This article applies a social impact in social media methodology (SISM) to identify quantitative and qualitative evidence of the potential or real social impact of research shared on social media, specifically on Twitter and Facebook. We define the social impact coverage ratio (SICOR) to identify the percentage of tweets and Facebook posts providing information about potential or actual social impact in relation to the total amount of social media data found related to specific research projects. We selected 10 projects in different fields of knowledge to calculate the SICOR, and the results indicate that 0.43% of the tweets and Facebook posts collected provide linkages with information about social impact. However, our analysis indicates that some projects have a high percentage (4.98%) and others have no evidence of social impact shared in social media. Examples of quantitative and qualitative evidence of social impact are provided to illustrate these results. A general finding is that novel evidences of social impact of research can be found in social media, becoming relevant platforms for scientists to spread quantitative and qualitative evidence of social impact in social media to capture the interest of citizens. Thus, social media users are showed to be intermediaries making visible and assessing evidence of social impact.

Citation: Pulido CM, Redondo-Sama G, Sordé-Martí T, Flecha R (2018) Social impact in social media: A new method to evaluate the social impact of research. PLoS ONE 13(8): e0203117. https://doi.org/10.1371/journal.pone.0203117

Editor: Sergi Lozano, Institut Català de Paleoecologia Humana i Evolució Social (IPHES), SPAIN

Received: November 8, 2017; Accepted: August 15, 2018; Published: August 29, 2018

Copyright: © 2018 Pulido et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The research leading to these results has received funding from the 7th Framework Programme of the European Commission under the Grant Agreement n° 613202 P.I. Ramon Flecha, https://ec.europa.eu/research/fp7/index_en.cfm . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The social impact of research is at the core of some of the debates influencing how scientists develop their studies and how useful results for citizens and societies may be obtained. Concrete strategies to achieve social impact in particular research projects are related to a broader understanding of the role of science in contemporary society. There is a need to explore dialogues between science and society not only to communicate and disseminate science but also to achieve social improvements generated by science. Thus, the social impact of research emerges as an increasing concern within the scientific community [ 1 ]. As Bornmann [ 2 ] said, the assessment of this type of impact is badly needed and is more difficult than the measurement of scientific impact; for this reason, it is urgent to advance in the methodologies and approaches to measuring the social impact of research.

Several authors have approached the conceptualization of social impact, observing a lack of generally accepted conceptual and instrumental frameworks [ 3 ]. It is common to find a wide range of topics included in the contributions about social impact. In their analysis of the policies affecting land use, Hemling et al. [ 4 ] considered various domains in social impact, for instance, agricultural employment or health risk. Moving to the field of flora and fauna, Wilder and Walpole [ 5 ] studied the social impact of conservation projects, focusing on qualitative stories that provided information about changes in attitudes, behaviour, wellbeing and livelihoods. In an extensive study by Godin and Dore [ 6 ], the authors provided an overview and framework for the assessment of the contribution of science to society. They identified indicators of the impact of science, mentioning some of the most relevant weaknesses and developing a typology of impact that includes eleven dimensions, with one of them being the impact on society. The subdimensions of the impact of science on society focus on individuals (wellbeing and quality of life, social implication and practices) and organizations (speeches, interventions and actions). For the authors, social impact “refers to the impact knowledge has on welfare, and on the behaviours, practices and activities of people and groups” (p. 7).

In addition, the terms “social impact” and “societal impact” are sometimes used interchangeably. For instance, Bornmann [ 2 ] said that due to the difficulty of distinguishing social benefits from the superior term of societal benefits, “in much literature the term ‘social impact’ is used instead of ‘societal impact’”(p. 218). However, in other cases, the distinction is made [ 3 ], as in the present research. Similar to the definition used by the European Commission [ 7 ], social impact is used to refer to economic impact, societal impact, environmental impact and, additionally, human rights impact. Therefore, we use the term social impact as the broader concept that includes social improvements in all the above mentioned areas obtained from the transference of research results and representing positive steps towards the fulfilment of those officially defined social goals, including the UN Sustainable Development Goals, the EU 2020 Agenda, or similar official targets. For instance, the Europe 2020 strategy defines five priority targets with concrete indicators (employment, research and development, climate change and energy, education and poverty and social exclusion) [ 8 ], and we consider the targets addressed by objectives defined in the specific call that funds the research project.

This understanding of the social impact of research is connected to the creation of the Social Impact Open Repository (SIOR), which constitutes the first open repository worldwide that displays, cites and stores the social impact of research results [ 9 ]. The SIOR has linked to ORCID and Wikipedia to allow the synergies of spreading information about the social impact of research through diverse channels and audiences. It is relevant to mention that currently, SIOR includes evidence of real social impact, which implies that the research results have led to actual improvements in society. However, it is common to find evidence of potential social impact in research projects. The potential social impact implies that in the development of the research, there has been some evidence of the effectiveness of the research results in terms of social impact, but the results have not yet been transferred.

Additionally, a common confusion is found among the uses of dissemination, transference (policy impact) and social impact. While dissemination means to disseminate the knowledge created by research to citizens, companies and institutions, transference refers to the use of this knowledge by these different actors (or others), and finally, as already mentioned, social impact refers to the actual improvements resulting from the use of this knowledge in relation to the goals motivating the research project (such as the United Nations Sustainable Development Goals). In the present research [ 3 ], it is argued that “social impact can be understood as the culmination of the prior three stages of the research” (p.3). Therefore, this study builds on previous contributions measuring the dissemination and transference of research and goes beyond to propose a novel methodological approach to track social impact evidences.

In fact, the contribution that we develop in this article is based on the creation of a new method to evaluate the evidence of social impact shared in social media. The evaluation proposed is to measure the social impact coverage ratio (SICOR), focusing on the presence of evidence of social impact shared in social media. Then, the article first presents some of the contributions from the literature review focused on the research on social media as a source for obtaining key data for monitoring or evaluating different research purposes. Second, the SISM (social impact through social media) methodology[ 10 ] developed is introduced in detail. This methodology identifies quantitative and qualitative evidence of the social impact of the research shared on social media, specifically on Twitter and Facebook, and defines the SICOR, the social impact coverage ratio. Next, the results are discussed, and lastly, the main conclusions and further steps are presented.

Literature review

Social media research includes the analysis of citizens’ voices on a wide range of topics [ 11 ]. According to quantitative data from April 2017 published by Statista [ 12 ], Twitter and Facebook are included in the top ten leading social networks worldwide, as ranked by the number of active users. Facebook is at the top of the list, with 1,968 million active users, and Twitter ranks 10 th , with 319 million active users. Between them are the following social networks: WhatsApp, YouTube, Facebook Messenger, WeChat, QQ, Instagram,Qzone and Tumblr. If we look at altmetrics, the tracking of social networks for mentions of research outputs includes Facebook, Twitter, Google+,LinkedIn, Sina Weibo and Pinterest. The social networks common to both sources are Facebook and Twitter. These are also popular platforms that have a relevant coverage of scientific content and easy access to data, and therefore, the research projects selected here for application of the SISM methodology were chosen on these platforms.

Chew and Eysenbach [ 13 ] studied the presence of selected keywords in Twitter related to public health issues, particularly during the 2009 H1N1 pandemic, identifying the potential for health authorities to use social media to respond to the concerns and needs of society. Crooks et al.[ 14 ] investigated Twitter activity in the context of a 5.8 magnitude earthquake in 2011 on the East Coast of the United States, concluding that social media content can be useful for event monitoring and can complement other sources of data to improve the understanding of people’s responses to such events. Conversations among young Canadians posted on Facebook and analysed by Martinello and Donelle [ 15 ] revealed housing and transportation as main environmental concerns, and the project FoodRisc examined the role of social media to illustrate consumers’ quick responses during food crisis situations [ 16 ]. These types of contributions illustrate that social media research implies the understanding of citizens’ concerns in different fields, including in relation to science.

Research on the synergies between science and citizens has increased over the years, according to Fresco [ 17 ], and there is a growing interest among researchers and funding agencies in how to facilitate communication channels to spread scientific results. For instance, in 1998, Lubchenco [ 18 ] advocated for a social contract that “represents a commitment on the part of all scientists to devote their energies and talents to the most pressing problems of the day, in proportion to their importance, in exchange for public funding”(p.491).

In this framework, the recent debates on how to increase the impact of research have acquired relevance in all fields of knowledge, and major developments address the methods for measuring it. As highlighted by Feng Xia et al. [ 19 ], social media constitute an emerging approach to evaluating the impact of scholarly publications, and it is relevant to consider the influence of the journal, discipline, publication year and user type. The authors revealed that people’s concerns differ by discipline and observed more interest in papers related to everyday life, biology, and earth and environmental sciences. In the field of biomedical sciences, Haustein et al. [ 20 ] analysed the dissemination of journal articles on Twitter to explore the correlations between tweets and citations and proposed a framework to evaluate social media-based metrics. In fact, different studies address the relationship between the presence of articles on social networks and citations [ 21 ]. Bornmann [ 22 ] conducted a case study using a sample of 1,082 PLOS journal articles recommended in F1000 to explore the usefulness of altmetrics for measuring the broader impact of research. The author presents evidence about Facebook and Twitter as social networks that may indicate which papers in the biomedical sciences can be of interest to broader audiences, not just to specialists in the area. One aspect of particular interest resulting from this contribution is the potential to use altmetrics to measure the broader impacts of research, including the societal impact. However, most of the studies investigating social or societal impact lack a conceptualization underlying its measurement.

To the best of our knowledge, the assessment of social impact in social media (SISM) has developed according to this gap. At the core of this study, we present and discuss the results obtained through the application of the SICOR (social impact coverage ratio) with examples of evidence of social impact shared in social media, particularly on Twitter and Facebook, and the implications for further research.

Following these previous contributions, our research questions were as follows: Is there evidence of social impact of research shared by citizens in social media? If so, is there quantitative or qualitative evidence? How can social media contribute to identifying the social impact of research?

Methods and data presentation

A group of new methodologies related to the analysis of online data has recently emerged. One of these emerging methodologies is social media analytics [ 23 ], which was initially used most in the marketing research field but also came to be used in other domains due to the multiple possibilities opened up by the availability and richness of the data for different research purposes. Likewise, the concern of how to evaluate the social impact of research as well as the development of methodologies for addressing this concern has occupied central attention. The development of SISM (Social Impact in Social Media) and the application of the SICOR (Social Impact Coverage Ratio) is a contribution to advancement in the evaluation of the social impact of research through the analysis of the social media selected (in this case, Twitter and Facebook). Thus, SISM is novel in both social media analytics and among the methodologies used to evaluate the social impact of research. This development has been made under IMPACT-EV, a research project funded under the Framework Program FP7 of the Directorate-General for Research and Innovation of the European Commission. The main difference from other methodologies for measuring the social impact of research is the disentanglement between dissemination and social impact. While altmetrics is aimed at measuring research results disseminated beyond academic and specialized spheres, SISM contribute to advancing this measurement by shedding light on to what extent evidence of the social impact of research is found in social media data. This involves the need to differentiate between tweets or Facebook posts (Fb/posts) used to disseminate research findings from those used to share the social impact of research. We focus on the latter, investigating whether there is evidence of social impact, including both potential and real social impact. In fact, the question is whether research contributes and/or has the potential to contribute to improve the society or living conditions considering one of these goals defined. What is the evidence? Next, we detail the application of the methodology.

Data collection

To develop this study, the first step was to select research projects with social media data to be analysed. The selection of research projects for application of the SISM methodology was performed according to three criteria.

Criteria 1. Selection of success projects in FP7. The projects were success stories of the 7 th Framework Programme (FP7) highlighted by the European Commission [ 24 ] in the fields of knowledge of medicine, public health, biology and genomics. The FP7 published calls for project proposals from 2007 to 2013. This implies that most of the projects funded in the last period of the FP7 (2012 and 2013) are finalized or in the last phase of implementation.

Criteria 2. Period of implementation. We selected projects in the 2012–2013 period because they combine recent research results with higher possibilities of having Twitter and Facebook accounts compared with projects of previous years, as the presence of social accounts in research increased over this period.

Criteria 3. Twitter and Facebook accounts. It was crucial that the selected projects had active Twitter and Facebook accounts.

Table 1 summarizes the criteria and the final number of projects identified. As shown, 10 projects met the defined criteria. Projects in medical research and public health had higher presence.

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https://doi.org/10.1371/journal.pone.0203117.t001

After the selection of projects, we defined the timeframe of social media data extraction on Twitter and Facebook from the starting date of the project until the day of the search, as presented in Table 2 .

https://doi.org/10.1371/journal.pone.0203117.t002

The second step was to define the search strategies for extracting social media data related to the research projects selected. In this line, we defined three search strategies.

Strategy 1. To extract messages published on the Twitter account and the Facebook page of the selected projects. We listed the Twitter accounts and Facebook pages related to each project in order to look at the available information. In this case, it is important to clarify that the tweets published under the corresponding Twitter project account are original tweets or retweets made from this account. It is relevant to mention that in one case, the Twitter account and Facebook page were linked to the website of the research group leading the project. In this case, we selected tweets and Facebook posts related to the project. For instance, in the case of the Twitter account, the research group created a specific hashtag to publish messages related to the project; therefore, we selected only the tweets published under this hashtag. In the analysis, we prioritized the analysis of the tweets and Facebook posts that received some type of interaction (likes, retweets or shares) because such interaction is a proxy for citizens’ interest. In doing so, we used the R program and NVivoto extract the data and proceed with the analysis. Once we obtained the data from Twitter and Facebook, we were able to have an overview of the information to be further analysed, as shown in Table 3 .

https://doi.org/10.1371/journal.pone.0203117.t003

We focused the second and third strategies on Twitter data. In both strategies, we extracted Twitter data directly from the Twitter Advanced Search tool, as the API connected to NVivo and the R program covers only a specific period of time limited to 7/9 days. Therefore, the use of the Twitter Advanced Search tool made it possible to obtain historic data without a period limitation. We downloaded the results in PDF and then uploaded them to NVivo.

Strategy 2. To use the project acronym combined with other keywords, such as FP7 or EU. This strategy made it possible to obtain tweets mentioning the project. Table 4 presents the number of tweets obtained with this strategy.

https://doi.org/10.1371/journal.pone.0203117.t004

Strategy 3. To use searchable research results of projects to obtain Twitter data. We defined a list of research results, one for each project, and converted them into keywords. We selected one searchable keyword for each project from its website or other relevant sources, for instance, the brief presentations prepared by the European Commission and published in CORDIS. Once we had the searchable research results, we used the Twitter Advanced Search tool to obtain tweets, as presented in Table 5 .

https://doi.org/10.1371/journal.pone.0203117.t005

The sum of the data obtained from these three strategies allowed us to obtain a total of 3,425 tweets and 1,925 posts on public Facebook pages. Table 6 presents a summary of the results.

https://doi.org/10.1371/journal.pone.0203117.t006

We imported the data obtained from the three search strategies into NVivo to analyse. Next, we select tweets and Facebook posts providing linkages with quantitative or qualitative evidence of social impact, and we complied with the terms of service for the social media from which the data were collected. By quantitative and qualitative evidence, we mean data or information that shows how the implementation of research results has led to improvements towards the fulfilment of the objectives defined in the EU2020 strategy of the European Commission or other official targets. For instance, in the case of quantitative evidence, we searched tweets and Facebook posts providing linkages with quantitative information about improvements obtained through the implementation of the research results of the project. In relation to qualitative evidence, for example, we searched for testimonies that show a positive evaluation of the improvement due to the implementation of research results. In relation to this step, it is important to highlight that social media users are intermediaries making visible evidence of social impact. Users often share evidence, sometimes sharing a link to an external resource (e.g., a video, an official report, a scientific article, news published on media). We identified evidence of social impact in these sources.

Data analysis

γ i is the total number of messages obtained about project i with evidence of social impact on social media platforms (Twitter, Facebook, Instagram, etc.);

T i is the total number of messages from project i on social media platforms (Twitter, Facebook, Instagram, etc.); and

n is the number of projects selected.

Analytical categories and codebook

The researchers who carried out the analysis of the social media data collected are specialists in the social impact of research and research on social media. Before conducting the full analysis, two aspects were guaranteed. First, how to identify evidence of social impact relating to the targets defined by the EU2020 strategy or to specific goals defined by the call addressed was clarified. Second, we held a pilot to test the methodology with one research project that we know has led to considerable social impact, which allowed us to clarify whether or not it was possible to detect evidence of social impact shared in social media. Once the pilot showed positive results, the next step was to extend the analysis to another set of projects and finally to the whole sample. The construction of the analytical categories was defined a priori, revised accordingly and lastly applied to the full sample.

Different observations should be made. First, in this previous analysis, we found that the tweets and Facebook users play a key role as “intermediaries,” serving as bridges between the larger public and the evidence of social impact. Social media users usually share a quote or paragraph introducing evidence of social impact and/or link to an external resource, for instance, a video, official report, scientific article, news story published on media, etc., where evidence of the social impact is available. This fact has implications for our study, as our unit of analysis is all the information included in the tweets or Facebook posts. This means that our analysis reaches the external resources linked to find evidence of social impact, and for this reason, we defined tweets or Facebook posts providing linkages with information about social impact.

Second, the other important aspect is the analysis of the users’ profile descriptions, which requires much more development in future research given the existing limitations. For instance, some profiles are users’ restricted due to privacy reasons, so the information is not available; other accounts have only the name of the user with no description of their profile available. Therefore, we gave priority to the identification of evidence of social impact including whether a post obtained interaction (retweets, likes or shares) or was published on accounts other than that of the research project itself. In the case of the profile analysis, we added only an exploratory preliminary result because this requires further development. Considering all these previous details, the codebook (see Table 7 ) that we present as follows is a result of this previous research.

https://doi.org/10.1371/journal.pone.0203117.t007

How to analyse Twitter and Facebook data

To illustrate how we analysed data from Twitter and Facebook, we provide one example of each type of evidence of social impact defined, considering both real and potential social impact, with the type of interaction obtained and the profiles of those who have interacted.

QUANESISM. Tweet by ZeroHunger Challenge @ZeroHunger published on 3 May 2016. Text: How re-using food waste for animal feed cuts carbon emissions.-NOSHAN project hubs.ly/H02SmrP0. 7 retweets and 5 likes.

The unit of analysis is all the content of the tweet, including the external link. If we limited our analysis to the tweet itself, it would not be evidence. Examining the external link is necessary to find whether there is evidence of social impact. The aim of this project was to investigate the process and technologies needed to use food waste for feed production at low cost, with low energy consumption and with a maximal evaluation of the starting wastes. This tweet provides a link to news published in the PHYS.org portal [ 25 ], which specializes in science news. The news story includes an interview with the main researcher that provides the following quotation with quantitative evidence:

'Our results demonstrated that with a NOSHAN 10 percent mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions were reduced by 0.3 kg compared to a non-food waste diet,' explains Montse Jorba, NOSHAN project coordinator. 'If 1 percent of total chicken broiler feed in Europe was switched to the 10 percent NOSHAN mix diet, the total amount of CO2 emissions avoided would be 0.62 million tons each year.'[ 25 ]

This quantitative evidence “a NOSHAN 10 percent mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions carbon dioxide emissions were reduced by 0.3 kg to a non-food waste diet” is linked directly with the Europe 2020 target of Climate Change & Energy, specifically with the target of reducing greenhouse gas emissions by 20% compared to the levels in 1990 [ 8 ]. The illustrative extrapolation the coordinator mentioned in the news is also an example of quantitative evidence, although is an extrapolation based on the specific research result.

This tweet was captured by the Acronym search strategy. It is a message tweeted by an account that is not related to the research project. The twitter account is that of the Zero Hunger Challenge movement, which supports the goals of the UN. The interaction obtained is 7 retweets and 5 likes. Regarding the profiles of those who retweeted and clicked “like”, there were activists, a journalist, an eco-friendly citizen, a global news service, restricted profiles (no information is available on those who have retweeted) and one account with no information in its profile.

The following example illustrates the analysis of QUALESISM: Tweet by @eurofitFP7 published on4 October 2016. Text: See our great new EuroFIT video on youtube! https://t.co/TocQwMiW3c 9 retweets and 5 likes.

The aim of this project is to improve health through the implementation of two novel technologies to achieve a healthier lifestyle. The tweet provides a link to a video on YouTube on the project’s results. In this video, we found qualitative evidence from people who tested the EuroFit programme; there are quotes from men who said that they have experienced improved health results using this method and that they are more aware of how to manage their health:

One end-user said: I have really amazing results from the start, because I managed to change a lot of things in my life. And other one: I was more conscious of what I ate, I was more conscious of taking more steps throughout the day and also standing up a little more. [ 26 ]

The research applies the well researched scientific evidence to the management of health issues in daily life. The video presents the research but also includes a section where end-users talk about the health improvements they experienced. The quotes extracted are some examples of the testimonies collected. All agree that they have improved their health and learned healthy habits for their daily lives. These are examples of qualitative evidence linked with the target of the call HEALTH.2013.3.3–1—Social innovation for health promotion [ 27 ] that has the objectives of reducing sedentary habits in the population and promoting healthy habits. This research contributes to this target, as we see in the video testimonies. Regarding the interaction obtained, this tweet achieved 9 retweets and 5 likes. In this case, the profiles of the interacting citizens show involvement in sport issues, including sport trainers, sport enthusiasts and some researchers.

To summarize the analysis, in Table 8 below, we provide a summary with examples illustrating the evidence found.

https://doi.org/10.1371/journal.pone.0203117.t008

Quantitative evidence of social impact in social media

There is a greater presence of tweets/Fb posts with quantitative evidence (14) than with qualitative evidence (9) in the total number of tweets/Fb posts identified with evidence of social impact. Most of the tweets/Fb posts with quantitative evidence of social impact are from scientific articles published in peer-reviewed international journals and show potential social impact. In Table 8 , we introduce 3 examples of this type of tweets/Fb posts with quantitative evidence:

The first tweet with quantitative social impact selected is from project 7. The aim of this project was to provide high-quality scientific evidence for preventing vitamin D deficiency in European citizens. The tweet highlighted the main contribution of the published study, that is, “Weekly consumption of 7 vitamin D-enhanced eggs has an important impact on winter vitamin D status in adults” [ 28 ]. The quantitative evidence shared in social media was extracted from a news publication in a blog on health news. This blog collects scientific articles of research results. In this case, the blog disseminated the research result focused on how vitamin D-enhanced eggs improve vitamin D deficiency in wintertime, with the published results obtained by the research team of the project selected. The quantitative evidence illustrates that the group of adults who consumed vitamin D-enhanced eggs did not suffer from vitamin D deficiency, as opposed to the control group, which showed a significant decrease in vitamin D over the winter. The specific evidence is the following extracted from the article [ 28 ]:

With the use of a within-group analysis, it was shown that, although serum 25(OH) D in the control group significantly decreased over winter (mean ± SD: -6.4 ± 6.7 nmol/L; P = 0.001), there was no change in the 2 groups who consumed vitamin D-enhanced eggs (P>0.1 for both. (p. 629)

This evidence contributes to achievement of the target defined in the call addressed that is KBBE.2013.2.2–03—Food-based solutions for the eradication of vitamin D deficiency and health promotion throughout the life cycle [ 29 ]. The quantitative evidence shows how the consumption of vitamin D-enhanced eggs reduces vitamin D deficiency.

The second example of this table corresponds to the example of quantitative evidence of social impact provided in the previous section.

The third example is a Facebook post from project 3 that is also tweeted. Therefore, this evidence was published in both social media sources analysed. The aim of this project was to measure a range of chemical and physical environmental hazards in food, consumer products, water, air, noise, and the built environment in the pre- and postnatal early-life periods. This Facebook post and tweet links directly to a scientific article [ 30 ] that shows the precision of the spectroscopic platform:

Using 1H NMR spectroscopy we characterized short-term variability in urinary metabolites measured from 20 children aged 8–9 years old. Daily spot morning, night-time and pooled (50:50 morning and night-time) urine samples across six days (18 samples per child) were analysed, and 44 metabolites quantified. Intraclass correlation coefficients (ICC) and mixed effect models were applied to assess the reproducibility and biological variance of metabolic phenotypes. Excellent analytical reproducibility and precision was demonstrated for the 1H NMR spectroscopic platform (median CV 7.2%) . (p.1)

This evidence is linked to the target defined in the call “ENV.2012.6.4–3—Integrating environmental and health data to advance knowledge of the role of environment in human health and well-being in support of a European exposome initiative” [ 31 ]. The evidence provided shows how the project’s results have contributed to building technology for improving the data collection to advance in the knowledge of the role of the environment in human health, especially in early life. The interaction obtained is one retweet from a citizen from Nigeria interested in health issues, according to the information available in his profile.

Qualitative evidence of social impact in social media

We found qualitative evidence of the social impact of different projects, as shown in Table 9 . Similarly to the quantitative evidence, the qualitative cases also demonstrate potential social impact. The three examples provided have in common that they are tweets or Facebook posts that link to videos where the end users of the research project explain their improvements once they have implemented the research results.

https://doi.org/10.1371/journal.pone.0203117.t009

The first tweet with qualitative evidence selected is from project 4. The aim of this project is to produce a system that helps in the prevention of obesity and eating disorders, targeting young people and adults [ 32 ]. The twitter account that published this tweet is that of the Future and Emerging Technologies Programme of the European Commission, and a link to a Euronews video is provided. This video shows how the patients using the technology developed in the research achieved control of their eating disorders, through the testimonies of patients commenting on the positive results they have obtained. These testimonies are included in the news article that complements the video. An example of these testimonies is as follows:

Pierre Vial has lost 43 kilos over the past nine and a half months. He and other patients at the eating disorder clinic explain the effects obesity and anorexia have had on their lives. Another patient, Karin Borell, still has some months to go at the clinic but, after decades of battling anorexia, is beginning to be able to visualise life without the illness: “On a good day I see myself living a normal life without an eating disorder, without problems with food. That’s really all I wish right now”.[ 32 ]

This qualitative evidence shows how the research results contribute to the achievement of the target goals of the call addressed:“ICT-2013.5.1—Personalised health, active ageing, and independent living”. [ 33 ] In this case, the results are robust, particularly for people suffering chronic diseases and desiring to improve their health; people who have applied the research findings are improving their eating disorders and better managing their health. The value of this evidence is the inclusion of the patients’ voices stating the impact of the research results on their health.

The second example is a Facebook post from project 9, which provides a link to a Euronews video. The aim of this project is to bring some tools from the lab to the farm in order to guarantee a better management of the farm and animal welfare. In this video [ 34 ], there are quotes from farmers using the new system developed through the research results of the project. These quotes show how use of the new system is improving the management of the farm and the health of the animals; some examples are provided:

Cameras and microphones help me detect in real time when the animals are stressed for whatever reason,” explained farmer Twan Colberts. “So I can find solutions faster and in more efficient ways, without me being constantly here, checking each animal.”

This evidence shows how the research results contribute to addressing the objectives specified in the call “KBBE.2012.1.1–02—Animal and farm-centric approach to precision livestock farming in Europe” [ 29 ], particularly, to improve the precision of livestock farming in Europe. The interaction obtained is composed of6 likes and 1 share. The profiles are diverse, but some of them do not disclose personal information; others have not added a profile description, and only their name and photo are available.

Interrater reliability (kappa)

The analysis of tweets and Facebook posts providing linkages with information about social impact was conducted following a content analysis method in which reliability was based on a peer review process. This sample is composed of 3,425 tweets and 1,925 Fb/posts. Each tweet and Facebook post was analysed to identify whether or not it contains evidence of social impact. Each researcher has the codebook a priori. We used interrater reliability in examining the agreement between the two raters on the assignment of the categories defined through Cohen’s kappa. We used SPSS to calculate this coefficient. We exported an excel sheet with the sample coded by the two researchers being 1 (is evidence of social impact, either potential or real) and 0 (is not evidence of social impact) to SPSS. The cases where agreement was not achieved were not considered as containing evidence of social impact. The result obtained is 0.979; considering the interpretation of this number according to Landis & Koch [ 35 ], our level of agreement is almost perfect, and thus, our analysis is reliable. To sum up the data analysis, the description of the steps followed is explained:

Step 1. Data analysis I. We included all data collected in an excel sheet to proceed with the analysis. Prior to the analysis, researchers read the codebook to keep in mind the information that should be identified.

Step 2. Each researcher involved reviewed case by case the tweets and Facebook posts to identify whether they provide links with evidence of social impact or not. If the researcher considers there to be evidence of social impact, he or she introduces the value of 1into the column, and if not, the value of 0.

Step 3. Once all the researchers have finished this step, the next step is to export the excel sheet to SPSS to extract the kappa coefficient.

Step 4. Data Analysis II. The following step was to analyse case by case the tweets and Facebook posts identified as providing linkages with information of social impact and classify them as quantitative or qualitative evidence of social impact.

Step 5. The interaction received was analysed because this determines to which extent this evidence of social impact has captured the attention of citizens (in the form of how many likes, shares, or retweets the post has).

Step 6. Finally, if available, the profile descriptions of the citizens interacting through retweeting or sharing the Facebook post were considered.

Step 7. SICOR was calculated. It could be applied to the complete sample (all data projects) or to each project, as we will see in the next section.

The total number of tweets and Fb/posts collected from the 10 projects is 5,350. After the content analysis, we identified 23 tweets and Facebook posts providing linkages to information about social impact. To respond to the research question, which considered whether there is evidence of social impact shared by citizens in social media, the answer was affirmative, although the coverage ratio is low. Both Twitter and Facebook users retweeted or shared evidence of social impact, and therefore, these two social media networks are valid sources for expanding knowledge on the assessment of social impact. Table 10 shows the social impact coverage ratio in relation to the total number of messages analysed.

https://doi.org/10.1371/journal.pone.0203117.t010

The analysis of each of the projects selected revealed some results to consider. Of the 10 projects, 7 had evidence, but those projects did not necessarily have more Tweets and Facebook posts. In fact, some projects with fewer than 70 tweets and 50 Facebook posts have more evidence of social impact than other projects with more than 400 tweets and 400 Facebook posts. This result indicates that the number of tweets and Facebook posts does not determine the existence of evidence of social impact in social media. For example, project 2 has 403 tweets and 423 Facebooks posts, but it has no evidence of social impact on social media. In contrast, project 9 has 62 tweets, 43 Facebook posts, and 2 pieces of evidence of social impact in social media, as shown in Table 11 .

https://doi.org/10.1371/journal.pone.0203117.t011

The ratio of tweets/Fb posts to evidence is 0.43%, and it differs depending on the project, as shown below in Table 12 . There is one project (P7) with a ratio of 4.98%, which is a social impact coverage ratio higher than that of the other projects. Next, a group of projects (P3, P9, P10) has a social impact coverage ratio between 1.41% and 2,99%.The next slot has three projects (P1, P4, P5), with a ratio between 0.13% and 0.46%. Finally, there are three projects (P2, P6, P8) without any tweets/Fb posts evidence of social impact.

https://doi.org/10.1371/journal.pone.0203117.t012

Considering the three strategies for obtaining data, each is related differently to the evidence of social impact. In terms of the social impact coverage ratio, as shown in Table 13 , the most successful strategy is number 3 (searchable research results), as it has a relation of 17.86%, which is much higher than the ratios for the other 2 strategies. The second strategy (acronym search) is more effective than the first (profile accounts),with 1.77% for the former as opposed to 0.27% for the latter.

https://doi.org/10.1371/journal.pone.0203117.t013

Once tweets and Facebook posts providing linkages with information about social impact(ESISM)were identified, we classified them in terms of quantitative (QUANESISM) or qualitative evidence (QUALESISM)to determine which type of evidence was shared in social media. Table 14 indicates the amount of quantitative and qualitative evidence identified for each search strategy.

https://doi.org/10.1371/journal.pone.0203117.t014

First, the results obtained indicated that the SISM methodology aids in calculating the social impact coverage ratio of the research projects selected and evaluating whether the social impact of the corresponding research is shared by citizens in social media. The social impact coverage ratio applied to the sample selected is low, but when we analyse the SICOR of each project separately, we can observe that some projects have a higher social impact coverage ratio than others. Complementary to altmetrics measuring the extent to which research results reach out society, the SICOR considers the question whether this process includes evidence of potential or real social impact. In this sense, the overall methodology of SISM contributes to advancement in the evaluation of the social impact of research by providing a more precise approach to what we are evaluating.

This contribution complements current evaluation methodologies of social impact that consider which improvements are shared by citizens in social media. Exploring the results in more depth, it is relevant to highlight that of the ten projects selected, there is one research project with a social impact coverage ratio higher than those of the others, which include projects without any tweets or Facebook posts with evidence of social impact. This project has a higher ratio of evidence than the others because evidence of its social impact is shared more than is that of other projects. This also means that the researchers produced evidence of social impact and shared it during the project. Another relevant result is that the quantity of tweets and Fb/posts collected did not determine the number of tweets and Fb/posts found with evidence of social impact. Moreover, the analysis of the research projects selected showed that there are projects with less social media interaction but with more tweets and Fb/posts containing evidence of social media impact. Thus, the number of tweets and Fb/posts with evidence of social impact is not determined by the number of publication messages collected; it is determined by the type of messages published and shared, that is, whether they contain evidence of social impact or not.

The second main finding is related to the effectiveness of the search strategies defined. Related to the strategies carried out under this methodology, one of the results found is that the most effective search strategy is the searchable research results, which reveals a higher percentage of evidence of social impact than the own account and acronym search strategies. However, the use of these three search strategies is highly recommended because the combination of all of them makes it possible to identify more tweets and Facebook posts with evidence of social impact.

Another result is related to the type of evidence of social impact found. There is both quantitative and qualitative evidence. Both types are useful for understanding the type of social impact achieved by the corresponding research project. In this sense, quantitative evidence allows us to understand the improvements obtained by the implementation of the research results and capture their impact. In contrast, qualitative evidence allows us to deeply understand how the resultant improvements obtained from the implementation of the research results are evaluated by the end users by capturing their corresponding direct quotes. The social impact includes the identification of both real and potential social impact.

Conclusions

After discussing the main results obtained, we conclude with the following points. Our study indicates that there is incipient evidence of social impact, both potential and real, in social media. This demonstrates that researchers from different fields, in the present case involved in medical research, public health, animal welfare and genomics, are sharing the improvements generated by their research and opening up new venues for citizens to interact with their work. This would imply that scientists are promoting not only the dissemination of their research results but also the evidence on how their results may lead to the improvement of societies. Considering the increasing relevance and presence of the dissemination of research, the results indicate that scientists still need to include in their dissemination and communication strategies the aim of sharing the social impact of their results. This implies the publication of concrete qualitative or quantitative evidence of the social impact obtained. Because of the inclusion of this strategy, citizens will pay more attention to the content published in social media because they are interested in knowing how science can contribute to improving their living conditions and in accessing crucial information. Sharing social impact in social media facilitates access to citizens of different ages, genders, cultural backgrounds and education levels. However, what is most relevant for our argument here is how citizens should also be able to participate in the evaluation of the social impact of research, with social media a great source to reinforce this democratization process. This contributes not only to greatly improving the social impact assessment, as in addition to experts, policy makers and scientific publications, citizens through social media contribute to making this assessment much more accurate. Thus, citizens’ contribution to the dissemination of evidence of the social impact of research yields access to more diverse sectors of society and information that might be unknown by the research or political community. Two future steps are opened here. On the one hand, it is necessary to further examine the profiles of users who interact with this evidence of social impact considering the limitations of the privacy and availability of profile information. A second future task is to advance in the articulation of the role played by citizens’ participation in social impact assessment, as citizens can contribute to current worldwide efforts by shedding new light on this process of social impact assessment and contributing to making science more relevant and useful for the most urgent and poignant social needs.

Supporting information

S1 file. interrater reliability (kappa) result..

This file contains the SPSS file with the result of the calculation of Cohen’s Kappa regards the interrater reliability. The word document exported with the obtained result is also included.

https://doi.org/10.1371/journal.pone.0203117.s001

S2 File. Data collected and SICOR calculation.

This excel contains four sheets, the first one titled “data collected” contains the number of tweets and Facebook posts collected through the three defined search strategies; the second sheet titled “sample” contains the sample classified by project indicating the ID of the message or code assigned, the type of message (tweet or Facebook post) and the codification done by researchers being 1 (is evidence of social impact, either potential or real) and 0 (is not evidence of social impact); the third sheet titled “evidence found” contains the number of type of evidences of social impact founded by project (ESISM-QUANESIM or ESISM-QUALESIM), search strategy and type of message (tweet or Facebook posts); and the last sheet titled “SICOR” contains the Social Impact Coverage Ratio calculation by projects in one table and type of search strategy done in another one.

https://doi.org/10.1371/journal.pone.0203117.s002

Acknowledgments

The research leading to these results received funding from the 7 th Framework Programme of the European Commission under Grant Agreement n° 613202. The extraction of available data using the list of searchable keywords on Twitter and Facebook followed the ethical guidelines for social media research supported by the Economic and Social Research Council (UK) [ 36 ] and the University of Aberdeen [ 37 ]. Furthermore, the research results have already been published and made public, and hence, there are no ethical issues.

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Five of the best books about social media

From online courtroom to information manipulation, social media has radically changed communication. Here are five books to help navigate it

F rom Covid conspiracy theories to recent speculations about Catherine, Princess of Wales, social media is at the heart of how we share information, and misinformation, with one another in the 21st century. For those who want to have a better understanding of social media and how it affects us, here are a selection of titles that explore how we consume, share, and manipulate information on social media platforms.

So You’ve Been Publicly Shamed by Jon Ronson

Journalist and author Jon Ronson argues we live in “a great renaissance of public shaming”, and this book tracks down some of the many victims of online shaming to understand what happened to them as a result. In the process, we learn about Ronson’s own values, question our own, and figure out how we’ve reached a time where an online feed can become a social courtroom.

Doppelganger by Naomi Klein

After getting repeatedly mistaken for feminist-turned-conspiracy-theorist Naomi Wolf online, and then in real life, Naomi Klein penned Doppelganger as an earnest and introspective look at herself. The book explores how conspiracy theories and lies spread quickly through the internet, and how the social and political climate of the physical world manipulates the way we experience online platforms. While not exclusively about social media, the story behind Doppelganger is a perfect case of the ways our digital lives and identities intersect with what we experience in reality – and how dangerous the repercussions of spreading online lies can be.

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Irresistible by Adam Alter

Have you ever wondered why you can’t stop scrolling on your TikTok “for you” page, or obsessing over how many likes you got on a recent Facebook post? You’re not alone, and Adam Alter’s book explores why we get sucked into the digital world. He answers what makes an online addiction, whether it be to emails, Instagram, or Netflix, different to other forms of addiction – and warns us of the dangers this could cause long-term. As well as introspection, he gives practical solutions to how digital addiction can be controlled for good.

Extremely Online by Taylor Lorenz

Journalist Taylor Lorenz calls this book “a social history of social media”; she uses real-life case studies of mothers, teenagers, politicians and influencers to assess how social media touches all demographics. Extremely Online explores topics from the digital economy and influencer culture, to what makes moments go viral on Twitter and how this is all influencing the way we socialise and understand the world. At its core, this book explores the idea of what it means to connect – and how social media as an innovation has warped communication.

TikTok Boom by Chris Stokel-Walker

TikTok is arguably one of the most significant advancements in social media in the past two decades. This book by journalist and writer Chris Stokel-Walker explores how the app is changing the way users interact with content . It moves away from the social-commentary style of the other books mentioned here, instead using business and technology analysis as a means to describe wider socio-political repercussions of the app. Stokel-Walker bridges the gap between the digital and the physical, showing the feedback loop that exists between what happens online on platforms such as TikTok and the real world.

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• Published: 20 September 2023

Older adults’ experiences during the COVID-19 pandemic: a qualitative systematic literature review

• Elfriede Derrer-Merk   ORCID: orcid.org/0000-0001-7241-0808 1 ,
• Maria-Fernanda Reyes-Rodriguez   ORCID: orcid.org/0000-0002-2645-5092 2 ,
• Laura K. Soulsby   ORCID: orcid.org/0000-0001-9071-8654 1 ,
• Louise Roper   ORCID: orcid.org/0000-0002-2918-7628 3 &
• Kate M. Bennett   ORCID: orcid.org/0000-0003-3164-6894 1  

BMC Geriatrics volume  23 , Article number:  580 ( 2023 ) Cite this article

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Relatively little is known about the lived experiences of older adults during the COVID-19 pandemic. We systematically review the international literature to understand the lived experiences of older adult’s experiences during the pandemic.

Design and methodology

This study uses a meta-ethnographical approach to investigate the included studies. The analyses were undertaken with constructivist grounded theory.

Thirty-two studies met the inclusion criteria and only five papers were of low quality. Most, but not all studies, were from the global north. We identified three themes: desired and challenged wellbeing; coping and adaptation; and discrimination and intersectionality.

Overall, the studies’ findings were varied and reflected different times during the pandemic. Studies reported the impact of mass media messaging and its mostly negative impact on older adults. Many studies highlighted the impact of the COVID-19 pandemic on participants' social connectivity and well-being including missing the proximity of loved ones and in consequence experienced an increase in anxiety, feeling of depression, or loneliness. However, many studies reported how participants adapted to the change of lifestyle including new ways of communication, and social distancing. Some studies focused on discrimination and the experiences of sexual and gender minority and ethnic minority participants. Studies found that the pandemic impacted the participants’ well-being including suicidal risk behaviour, friendship loss, and increased mental health issues.

The COVID-19 pandemic disrupted and impacted older adults’ well-being worldwide. Despite the cultural and socio-economic differences many commonalities were found. Studies described the impact of mass media reporting, social connectivity, impact of confinement on well-being, coping, and on discrimination. The authors suggest that these findings need to be acknowledged for future pandemic strategies. Additionally, policy-making processes need to include older adults to address their needs. PROSPERO record [CRD42022331714], (Derrer-Merk et al., Older adults’ lived experiences during the COVID-19 pandemic: a systematic review, 2022).

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Introduction

In March 2020 the World Health Organisation declared a pandemic caused by the virus SARS-CoV2 (COVID-19) [ 1 ]. At this time 118,000 cases in 114 countries were identified and 4,291 people had already lost their lives [ 2 ]. By July 2022, there were over 5.7 million active cases and over 6.4 million deaths [ 2 ]. Despite the effort to combat and eliminate the virus globally, new variants of the virus are still a concern. At the start of the pandemic, little was known about who would be most at risk, but emerging data suggested that both people with underlying health conditions and older people had a higher risk of becoming seriously ill [ 3 ]. Thus, countries worldwide imposed health and safety measures aimed at reducing viral transmission and protecting people at higher risk of contracting the virus [ 4 ]. These measures included: national lockdowns with different lengths and frequencies; targeted shopping times for older people; hygiene procedures (wearing masks, washing hands regularly, disinfecting hands); restricting or prohibiting social gatherings; working from home, school closure, and home-schooling.

Research suggests that lockdowns and protective measures impacted on people’s lives, and had a particular impact on older people. They were at higher risk from COVID-19, with greater disease severity and higher mortality compared to younger people [ 5 ]. Older adults were identified as at higher risk as they are more likely to have pre-existing conditions including heart disease, diabetes, and severe respiratory conditions [ 5 ]. Additionally, recent research highlights that COVID-19 and its safety measures led to increased mental health problems, including increased feelings of depression, anxiety, social isolation, and loneliness, potentially cognitive decline [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. Other studies reported the consequences of only age-based protective health measures including self-isolation for people older people (e.g. feeling old, losing out the time with family) [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ].

Over the past decade, the World Health Organisation (WHO) has recognised the importance of risk communication within public health emergency preparedness and response, especially in the context of epidemics and pandemics. Risk communication is defined as “the real-time exchange of information, advice and opinions between experts or officials, and people who face a threat (hazard) to their survival, health or economic or social well-being” ([ 31 ], p5). This includes reporting the risk and health protection measurements through media and governmental bodies. Constructing awareness and building trust in society are essential components of risk communication [ 32 ]. In the context of the pandemic, the WHO noted that individual risk perception helped to prompt problem-solving activities (such as wearing face masks, social distancing, and self-isolation). However, the prolonged perception of pandemic-related uncertainty and risk could also lead to heightened feelings of distress and anxiety [ 31 , 33 ], see also [ 34 , 35 , 36 , 37 ].

This new and unprecedented disease provided the ground for researchers worldwide to investigate the COVID-19 pandemic. To date (August 2022), approximately 8072 studies have been recorded on the U.S. National Library of Medicine ClinicalTrials.gov [ 38 ] and 12002 systematic reviews have been registered at PROSPERO, concerning COVID-19. However, to our knowledge, there is little known about qualitative research as a response to the COVID-19 pandemic and how it impacted older adults’ well-being [ 39 ]. In particular, little is known about how older people experienced the pandemic. Thus, our research question considers: How did older adults experience the COVID-19 pandemic worldwide?

We use a qualitative evidence synthesis (QES) recommended by Cochrane Qualitative and Implementation Methods Group to identify peer-reviewed articles [ 40 ]. This provides an overview of existing research, identifies potential research gaps, and develops new cumulative knowledge concerning the COVID-19 pandemic and older adults’ experiences. QES is a valuable method for its potential to contribute to research and policy [ 41 ]. Flemming and Noyes [ 40 ] argue that the evidence synthesis from qualitative research provides a richer interpretation compared to single primary research. They identified an increasing demand for qualitative evidence synthesis from a wide range of “health and social professionals, policymakers, guideline developers and educationalists” (p.1).

Methodology

A systematic literature review requires a specific approach compared to other reviews. Although there is no consensus on how it is conducted, recent systematic literature reviews have agreed the following reporting criteria are addressed [ 42 , 43 ]: (a) a research question; (b) reporting database, and search strategy; (c) inclusion and exclusion criteria; (d) reporting selection methods; (e) critically appraisal tools; (f) data analysis and synthesis. We applied these criteria in our study and began by registering the research protocol with Prospero [ 44 ].

The study is registered at Prospero [ 44 ]. This systematic literature review incorporates qualitative studies concerning older adults’ experiences during the COVID-19 pandemic.

Search strategy

The primary qualitative articles were identified via a systematic search as per the qualitative-specific SPIDER approach [ 45 ]. The SPIDER tool is designed to structure qualitative research questions, focusing less on interventions and more on study design, and ‘samples’ rather than populations, encompassing:

S-Sample. This includes all articles concerning older adults aged 60 +  [ 1 ].

P-Phenomena of Interest. How did older adults experience the COVID-19 pandemic?

D-Design. We aim to investigate qualitative studies concerning the experiences of older adults during the COVID-19 pandemic.

E-Evaluation. The evaluation of studies will be evaluated with the amended Critical Appraisal Skills Programme CASP [ 46 ].

R-Research type Qualitative

Information source

The following databases were searched: PsychInfo, Medline, CINAHL, Web of Science, Annual Review, Annual Review of Gerontology, and Geriatrics. A hand search was conducted on Google Scholar and additional searches examined the reference lists of the included papers. The keyword search included the following terms: (older adults or elderly) AND (COVID-19 or SARS or pandemic) AND (experiences); (older adults) AND (experience) AND (covid-19) OR (coronavirus); (older adults) AND (experience) AND (covid-19 OR coronavirus) AND (Qualitative). Additional hand search terms included e.g. senior, senior citizen, or old age.

Inclusion and exclusion criteria

Articles were included when they met the following criteria: primary research using qualitative methods related to the lived experience of older adults aged 60 + (i.e. the experiences of individuals during the COVID-19 pandemic); peer-reviewed journal articles published in English; related to the COVID-19 pandemic; empirical research; published from 2020 till August 2022.

Articles were excluded when: papers discussed health professionals’ experiences; diagnostics; medical studies; interventions; day-care; home care; or carers; experiences with dementia; studies including hospitals; quantitative studies; mixed-method studies; single-case studies; people under the age of 60; grey literature; scoping reviews, and systematic reviews. We excluded clinical/care-related studies as we wanted to explore the everyday experiences of people aged 60 + . Mixed-method studies were excluded as we were interested in what was represented in solely qualitative studies. However, we acknowledge, that mixed-method studies are valuable for future systematic reviews.

Meta-ethnography

The qualitative synthesis was undertaken by using meta-ethnography. The authors have chosen meta-ethnography over other methodologies as it is an inductive and interpretive synthesis analysis and is uniquely “suited to developing new conceptual models and theories” ([ 47 ], p 2), see also [ 48 ]. Therefore, it combines well with constructivist grounded theory methodology. Meta-ethnography also examines and identifies areas of disagreements between studies [ 48 ].

This is of particular interest as the lived experiences of older adults during the COVID-19 pandemic were likely to be diverse. The method enables the researcher to synthesise the findings (e.g. themes, concepts) from primary studies, acknowledging primary data (quotes) by “using a unique translation synthesis method to transcend the findings of individual study accounts and create higher order” constructs ([ 47 ], p. 2). The following seven steps were applied:

Getting started (identify area of interest). We were interested in the lived experiences of older adults worldwide.

Deciding what was relevant to the initial interest (defining the focus, locating relevant studies, decision to include studies, quality appraisal). We decided on the inclusion and exclusion criteria and an appropriate quality appraisal.

Reading the studies. We used the screening process described below (title, abstract, full text)

Determining how the studies were related (extracting first-order constructs- participants’ quotes and second-order construct- primary author interpretation, clustering the themes from the studies into new categories (Table 3 ).

Translating the studies into one another (comparing and contrasting the studies, checking commonalities or differences of each article) to organise and develop higher-order constructs by using constant comparison (Table 3 ). Translating is the process of finding commonalities between studies [ 48 ].

Synthesising the translation (reciprocal and refutational synthesis, a lines of argument synthesis (interpretation of the relationship between the themes- leads to key themes and constructs of higher order; creating new meaning, Tables 2 , 3 ),

Expressing the synthesis (writing up the findings) [ 47 , 48 ].

Screening and Study Selection

A 4-stage screening protocol was followed (Fig.  1 Prisma). First, all selected studies were screened for duplicates, which were deleted. Second, all remaining studies were screened for eligibility, and non-relevant studies were excluded at the preliminary stage. These screening steps were as follows: 1. title screening; 2. abstract screening, by the first and senior authors independently; and 3. full-text screening which was undertaken for almost all papers by the first author. However, 2 papers [ 9 , 23 ] were assessed independently by LS, LR, and LMM to avoid a conflict of interest. The other co-authors also screened independently a portion of the papers each, to ensure that each paper had two independent screens to determine inclusion in the review [ 49 ]. This avoided bias and confirmed the eligibility of the included papers (Fig.  1 ). Endnote reference management was used to store the articles and aid the screening process.

Prisma flow diagram adapted from Page et al. [ 50 ]. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71 )

Data extraction

After title and abstract screening, 39 papers were selected for reading the full article. 7 papers were excluded after the full-text assessment (1 study was conducted in 2017, but published in 2021; 2 papers were not fully available in English, 2 papers did not address the research question, 1 article was based on a conference abstract only, 1 article had only one participant age 65 +).

The full-text screening included 32 studies. All the included studies, alongside the CASP template, data extraction table, the draft of this article, and translation for synthesising the findings [ 47 , 48 ] were available and accessible on google drive for all co-authors. All authors discussed the findings in regular meetings.

Quality appraisal

A critical appraisal tool assesses a study for its trustworthiness, methodological rigor, and biases and ensures “transparency in the assessment of primary research” ([ 51 ], p. 5); see also [ 48 , 49 , 50 , 51 , 52 , 53 ]. There is currently no gold standard for assessing primary qualitative studies, but different authors agreed that the amended CASPS checklist was appropriate to assess qualitative studies [ 46 , 54 ]. Thus, we use the amended CASP appraisal tool [ 42 ]. The amended CASP appraisal tool aims to improve qualitative evidence synthesis by assessing ontology and epistemology (Table 1 CASP appraisal tool).

A numerical score was assigned to each question to indicate whether the criteria had been met (= 2), partially met (= 1), or not met (= 0) [ 54 ]; see also [ 55 ]. The score 16 – 22 are considered to be moderate and high-quality studies. The studies scored 15 and below were identified as low-quality papers. Although we focus on higher-quality papers, we did not exclude papers to avoid the exclusion of insightful and meaningful data [ 42 , 48 , 52 , 53 , 54 , 55 , 56 , 57 ]. The quality of the paper was considered in developing the evidence synthesis.

We followed the appraisal questions applied for each included study and answered the criteria either ‘Yes’, ‘Cannot tell’, or ‘No’. (Table 1 CASP appraisal criteria). The tenth question asking the value of the article was answered with ‘high’ of importance, ‘middle’, or low of importance. The new eleventh question in the CASP tool concerning ontology and epistemology was answered with yes, no, or partly (Table 1 ).

Data synthesis

The data synthesis followed the seven steps of Meta-Ethnography developed by Noblit & Hare [ 58 ], starting the data synthesis at step 3, described in detail by [ 47 ]. This encompasses: reading the studies; determining how the studies are related; translating the studies into one another; synthesis the translations; and expressing synthesis. This review provides a synthesis of the findings from studies related to the experiences of older adults during the COVID-19 pandemic. The qualitative analyses are based on constructivist grounded theory [ 59 ] to identify the experiences of older adults during the COVID-19 pandemic (non-clinical) populations. The analysis is inductive and iterative, uses constant comparison, and aims to develop a theory. The qualitative synthesis encompasses all text labelled as ‘results’ or ‘findings’ and uses this as raw data. The raw data includes participant’s quotes; thus, the synthesis is grounded in the participant's experience [ 47 , 48 , 60 , 61 ]. The initial coding was undertaken for each eligible article line by line. Please see Table 2 Themes per author and country. Focused coding was applied using constant comparison, which is a widely used approach in grounded theory [ 61 ]. In particular, common and recurring as well as contradicting concepts within the studies were identified, clustered into categories, and overarching higher order constructs were developed [ 47 , 48 , 60 ] (Tables 2 , 3 , 4 ).

We identified twenty-seven out of thirty-two studies as moderate-high quality; they met most of the criteria (scoring 16/22 or above on the CASP; [ 54 ]. Only five papers were identified as low qualitative papers scoring 15 and below [ 71 , 73 , 74 , 86 , 91 ]. Please see the scores provided for each paper in Table 4 . The low-quality papers did not provide sufficient details regarding the researcher’s relationship with the participants, sampling and recruitment, data collection, rigor in the analysis, or epistemological or ontological reasoning. For example, Yildirim [ 91 ] used verbatim notes as data without recording or transcribing them. This article described the analytical process briefly but was missing a discussion of the applied reflexivity of using verbatim notes and its limitations [ 92 ].

This systematic review found that many studies did not mention the relationship between the authors and the participant. The CASP critical appraisal tool asks: Has the relationship between the researcher and participants been adequately considered? (reflecting on own role, potential bias). Many studies reported that the recruitment was drawn from larger studies and that the qualitative study was a sub-study. Others reported that participants contacted the researcher after advertising the study. One study Goins et al., [ 72 ] reported that students recruited family members, but did not discuss how this potential bias impacted the results.

Our review brings new insights into older adults’ experiences during the pandemic worldwide. The studies were conducted on almost all continents. The majority of the articles were written in Europe followed by North America and Canada (4: USA; 3: Canada, UK; 2: Brazil, India, Netherlands, Sweden, Turkey 2; 1: Austria, China, Finland, India/Iran, Mauritius, New Zealand, Serbia, Spain, Switzerland, Uganda, UK/Ireland, UK/Colombia) (see Fig.  2 ). Note, as the review focuses on English language publications, we are unable to comment on qualitative research conducted in other languages see [ 72 ].

Numbers of publications by country

The characteristics of the included studies and the presence of analytical themes can be found in Table 4 . We used the following characteristics: Author and year of publication, research aims, the country conducted, Participant’s age, number of participants, analytical methodology, CASP score, and themes.

We identified three themes: desired and challenged wellbeing; coping and adaptation; discrimination and intersectionality. We will discuss the themes in turn.

Desired and challenged wellbeing

Most of the studies reported the impact of the COVID-19 pandemic on the well-being of older adults. Factors which influenced wellbeing included: risk communication and risk perception; social connectivity; confinement (at home); and means of coping and adapting. In this context, well-being refers to the evidence reported about participants' physical and mental health, and social connectivity.

Risk perception and risk communication

Politicians and media transmitted messages about the response to the pandemic to the public worldwide. These included mortality and morbidity reports, and details of health and safety regulations like social distancing, shielding- self-isolation, or wearing masks [ 34 , 35 , 36 , 37 ]. As this risk communication is crucial to combat the spread of the virus, it is also important to understand how people perceived the reporting during the pandemic.

Seven studies reported on how the mass media impacted participants' well-being [ 23 , 67 , 68 , 70 , 72 , 81 , 85 ]. Sangrar et al. [ 68 ] investigated how older adults responded to COVID-19 messaging: “My reaction was to try to make sure that I listen to everything and [I] made sure I was aware of all the suggestions and the precautions that were being expressed by various agencies …”. (p. 4). Other studies reported the negative impact on participants' well-being of constant messaging and as a consequence stopped watching the news to maintain emotional well-being [ 3 , 67 , 68 , 70 , 72 , 81 , 85 ]. Derrer-Merk et al. [ 23 ] reported one participant said that “At first, watching the news every day is depressing and getting more and more depressing by the day, so I’ve had to stop watching it for my own peace of mind” (p. 13). In addition, news reporting impacted participants’ risk perception. For example, “Sometimes we are scared to hear the huge coverage of COVID-19 news, in particular the repeated message ‘older is risky’, although the message is useful.” ([ 81 ], p5).

• Social connectivity

Social connectivity and support from family and community were found in fourteen of the studies as important themes [ 9 , 62 , 66 , 67 , 68 , 75 , 76 , 77 , 78 , 79 , 80 , 83 , 84 , 90 ].

The impact of COVID-19 on social networks highlighted the diverse experiences of participants. Some participants reported that the size of social contact was reduced: “We have been quite isolated during this corona time” ?([ 80 ], p. 3). Whilst other participants reported that the network was stable except that the method of contact was different: “These friends and relatives, they visited and called as often as before, but of course, we needed to use the telephone when it was not possible to meet” ([ 77 ], p. 5). Many participants in this study did not want to expand their social network see also [ 9 , 77 , 78 , 79 ]. Hafford-Letchfield et al. [ 76 ] reported that established social networks and relationships were beneficial for the participants: “Covid has affected our relationship (with partner), we spend some really positive close time together and support each other a lot” (p. 7).

On the other hand, other studies reported decreases of, and gaps in, social connectedness: “I couldn’t do a lot of things that I’ve been doing for years. That was playing competitive badminton three times a week, I couldn’t do that. I couldn’t get up early and go volunteer in Seattle” [ 9 , 67 , 75 ]. A loss of social connection with children and grandchildren was often mentioned: “We cannot see our grandchildren up close and personal because, well because they [the parents] don’t want us, they don’t want to risk our being with the kids … it’s been an emotional loss exacerbated by the COVID thing” ([ 68 ] p.10); see also [ 9 , 67 , 78 ]. On the contrary, Chemen & Gopalla [ 66 ] note that those older adults who were living with other family members reported that they were more valued: “Last night my daughter-in-law thanked me for helping with my granddaughter” (p.4).

Despite reports of social disconnectedness, some studies highlighted the importance of support from family members and how support changed during the COVID-19 pandemic [ 9 , 62 , 81 , 83 , 90 ]. Yang et al. [ 90 ] argued that social support was essential during the Lockdown in China: “N6 said: ‘I asked my son-in-law to take me to the hospital” (p. 4810). Mahapatra et al. [ 81 ] found, in an Indian study, that the complex interplay of support on different levels (individual, family, and community) helped participants to adapt to the new situation. For example, this participant reported that: “The local police are very helpful. When I rang them for something and asked them to find out about it, they responded immediately” (p. 5).

Impact of confinement on well being

Most articles highlighted the impact of confinement on older adults’ well-being [ 9 , 62 , 63 , 65 , 67 , 69 , 70 , 72 , 75 , 77 , 78 , 79 , 81 , 82 , 83 , 85 , 89 , 90 ].

Some studies found that participants maintained emotional well-being during the pandemic and it did not change their lifestyle [ 79 , 80 , 82 , 83 , 89 , 92 ]: “Actually, I used this crisis period to clean my house. Bookcases are completely cleaned and I discarded old books. Well, we have actually been very busy with those kind of jobs. So, we were not bored at all” ([ 79 ], p. 5). In McKinlay et al. [ 82 ]’s study, nearly half of the participants found that having a sense of purpose helped to maintain their well-being: “You have to have a purpose you see. I think mental resilience is all about having a sense of purpose” (p. 6).

However, at the same time, the majority of the articles (12 out of 18) highlighted the negative impact of confinement and social distancing. Participants talked of increased depressive feelings and anxiety. For example, one of Akkus et al.’s [ 62 ] participants said: “... I am depressed; people died. Terrible disease does not give up, it always kills, I am afraid of it …” (p. 549). Similarly, one of Falvo et al.’s [ 67 ] participants remarked: “I am locked inside my house and I am afraid to go out” (p. 7).

Many of the studies reported the negative impact of loneliness as a result of confinement on participants’ well-being including [ 69 , 70 , 72 , 78 , 79 , 90 , 93 ]. Falvo et al. [ 67 ] reported that many participants experienced loneliness: “What sense does it make when you are not even able to see a family member? I mean, it is the saddest thing not to have the comfort of having your family next to you, to be really alone” (p. 8).

Not all studies found a negative impact on loneliness. For example, a “loner advantage” was found by Xie et al. ([ 82 ], p. 386). In this study participants found benefits in already being alone “It’s just a part of who I am, and I think that helps—if you can be alone, it really is an asset when you have to be alone” ([ 82 ], p. 386).

Bundy et al. [ 80 ] investigated loneliness from already lonely older adults and found that many participants did not attribute the loneliness to the pandemic: “It’s not been a whole lot, because I was already sitting around the house a whole lot anyway ( …). It’s basically the same, pretty well … I’d pretty well be like this anyway with COVID or without COVID” (p. 873) (see also [ 83 ]).

A study from Serbia investigated how the curfew was perceived 15 months afterward. Some participants were calm: “I realized that … well … it was simply necessary. For that reason, we accepted it as a measure that is for the common good” ([ 70 ], p.634). Others were shocked: “Above all, it was a huge surprise and sort of a shock, a complete shock because I have never, ever seen it in my life and I felt horrible, because I thought that something even worse is coming, that I even could not fathom” ([ 70 ], p. 634).

The lockdowns brought not only mental health issues to the fore but impacted the physical health of participants. Some reported they were fearful of the COVID-19 pandemic: “... For a little while I was afraid to leave, to go outside. I didn’t know if you got it from the air” ([ 75 ]. p. 6). Another study reported: “It’s been important for me to walk heartily so that I get a bit sweaty and that I breathe properly so that I fill my lungs—so that I can be prepared—and be as strong as possible, in case I should catch that coronavirus” ([ 77 ], p. 9); see also [ 70 , 78 , 82 , 85 ].

Coping and adaptation

Many studies mentioned older adults’ processes of coping and adaptation during the pandemic [ 63 , 64 , 68 , 69 , 72 , 75 , 79 , 81 , 85 , 87 , 88 , 89 , 90 ].

A variety of coping processes were reported including: acceptance; behavioural adaptation; emotional regulation; creating new routines; or using new technology. Kremers et al. [ 79 ] reported: “We are very realistic about the situation and we all have to go through it. Better days will come” (p. e71). Behavioural adaptation was reported: “Because I’m asthmatic, I was wearing the disposable masks, I really had trouble breathing. But I was determined to find a mask I could wear” ([ 68 ], p. 14). New routines with protective hygiene helped some participants at the beginning of the pandemic to cope with the health threat: “I am washing my hands all the time, my hands are raw from washing them all the time, I don't think I need to wash them as much as I do but I do it just in case, I don’t have anybody coming in, so there is nobody contaminating me, but I keep washing” ([ 69 ], p. 4391); see also [ 72 ]. Verhage et al. [ 87 ] reported strategies of coping including self-enhancing comparisons, distraction, and temporary acceptance: “There are so many people in worse circumstances …” (p. e294). Other studies reported how participants used a new technology: “I have recently learned to use WhatsApp, where I can make video phone calls.” ([ 88 ], p. 163); see also [ 89 ].

Discrimination -intersectionality (age and race/gender identity)

Seven studies reported ageism, racism, and gender discrimination experienced by older adults during the pandemic [ 23 , 63 , 67 , 70 , 76 , 84 , 88 ].

Prigent et al. [ 84 ], conducted in a New Zealand study, found that ageism was reciprocal. Younger people spoke against older adults: “why don’t you do everyone a favour and drop dead you f******g b**** it’s all because of ones like you that people are losing jobs” (p. 11). On the other hand, older adults spoke against the younger generation: “Shame to see the much younger generations often flout the rules and generally risk the gains made by the team. Sheer arrogance on their part and no sanctions applied” (p.11). Although one study reported benevolent ageism [ 23 ] most studies found hostile ageism [ 23 , 63 , 67 , 70 , 76 , 84 ]. One study from Canada exploring 15 older adult’s Chinese immigrants’ experiences reported racism as people around them thought they would bring the virus into the country. The negative impact on existing friendships was told by a Chinese man aged 69 “I can tell some people are blatantly despising us. I can feel it. When I talked with my Caucasian friends verbally, they would indirectly blame us for the problem. Eventually, many of our friendships ended because of this issue” ([ 88 ], p161). In addition, this study reported ageism when participants in nursing homes felt neglected by the Canadian government.

Two papers reported experiences of sexual and gender minorities (SGM) (e.g. transgender, queer, lesbian or gay) and found additional burdens during the pandemic [ 63 , 76 ]. People experienced marginalisation, stereotypes, and discrimination, as well as financial crisis: “I have faced this throughout life. Now people look at me in a way as if I am responsible for the virus.” ([ 63 ], p. 6). The consequence of marginalisation and ignorance of people with different gender identities was also noted by Hafford- Letchfield et al. [ 76 ]: “People have been moved out of their accommodation into hotels with people they don't know …. a gay man committed suicide, community members know of several that have attempted suicide. They are feeling pretty marginalised and vulnerable and you see what people are writing on the chat pages” (p.4). The intersection of ageism, racism, and heterosexism and its negative impact on people’s well-being during the pandemic reflects additional burden and stressors for older adults.

This systematic literature review is important as it provides new insights into the lived experiences of older adults during the COVID-19 pandemic, worldwide. Our study highlights that the COVID-19 pandemic brought an increase in English-written qualitative articles to the fore. We found that 32 articles met the inclusion criteria but 5 were low quality. A lack of transparency reduces the trustworthiness of the study for the reader and the scientific community. This is particularly relevant as qualitative research is often criticised for its bias or lack of rigor [ 94 ]. However, their findings are additional evidence for our study.

Our aim was to explore, in a systematic literature review, the lived experiences of older adults during the COVID-19 pandemic worldwide. The evidence highlights the themes of desired and challenged wellbeing, coping and adaptation, and discrimination and intersectionality, on wellbeing.

Perceived risk communication was experienced by many participants as overwhelming and anxiety-provoking. This finding supports Anwar et al.’s [ 37 ] study from the beginning of the pandemic which found, in addition to circulating information, that mass media influenced the public's behaviour and in consequence the spread of disease. The impact can be positive but has also been revealed to be negative as well. They suggest evaluating the role of the mass media in relation to what and how it has been conveyed and perceived. The disrupted social connectivity found in our review supports earlier studies that reported the negative impact of people’s well-being [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ] at the beginning of the pandemic. This finding is important for future health crisis management, as the protective health measures such as confinement or self-isolation had a negative impact on many of the participants’ emotional wellbeing including increased anxiety, feelings of depression, and loneliness during the lockdowns. As a result of our review, future protective health measures should support people’s desire to maintain proximity with their loved ones and friends. However, we want to stress that our findings are mixed.

The ability of older adults to adapt and cope with the health crisis is important: many of the reported studies noted the diverse strategies used by older people to adapt to new circumstances. These included learning new technologies or changing daily routines. Politicians and the media and politicians should recognise both older adults' risk of disease and its consequences, but also their adaptability in the face of fast-changing health measures. This analysis supports studies conducted over the past decades on lifespan development, which found that people learn and adapt livelong to changing circumstances [ 95 , 96 , 97 ].

We found that discrimination against age, race, and gender identity was reported in some studies, in particular exploring participants’ experiences with immigration backgrounds and sexual and gender minorities. These studies highlighted the intersection of age and gender or race and were additional stressors for older adults and support the findings from Ramirez et al. [ 98 ] This review suggests that more research should be conducted to investigate the experiences of minority groups to develop relevant policies for future health crises.

Our review was undertaken two years after the pandemic started. At the cut-off point of our search strategy, no longitudinal studies had been found. However, in December 2022 a longitudinal study conducted in the USA explored older adult’s advice given to others [ 99 ]. They found that fostering and maintaining well-being, having a positive life perspective, and being connected to others were coping strategies during the pandemic [ 100 ]. This study supports the results of the higher order constructs of coping and adaptation in this study. Thus, more longitudinal studies are needed to enhance our understanding of the long-term consequences of the COVID-19 pandemic. The impact of the COVID-19 restrictions on older adults’ lives is evident. We suggest that future strategies and policies, which aim to protect older adults, should not only focus on the physical health threat but also acknowledge older adults' needs including psychological support, social connectedness, and instrumental support. The policies regarding older adult’s protections changed quickly but little is known about older adults’ involvement in decision making [ 100 ]. We suggest including older adults as consultants in policymaking decisions to ensure that their own self-determinism and independence are taken into consideration.

There are some limitations to this study. It did not include the lived experiences of older adults in care facilities or hospitals. The studies were undertaken during the COVID-19 pandemic and therefore data collection was not generally undertaken face-to-face. Thus, many studies included participants who had access to a phone, internet, or email, others could not be contacted. Additionally, we did not include published papers after August 2022. Even after capturing the most commonly used terms and performing additional hand searches, the search terms used might not be comprehensive. The authors found the quality of the papers to be variable, and their credibility was in question. We acknowledge that more qualitative studies might have been published in other languages than English and were not considered in this analysis.

To conclude, this systematic literature review found many similarities in the experiences of older adults during the Covid-19 pandemic despite cultural and socio-economic differences. However, we stress to acknowledge the heterogeneity of the experiences. This study highlights that the interplay of mass media reports of the COVID-19 pandemic and the policies to protect older adults had a direct impact on older adults’ well-being. The intersection of ‘isms’ (ageism, racism, and heterosexism) brought an additional burden for some older adults [ 98 ]. These results and knowledge about the drawbacks of health-protecting measures need to be included in future policies to maintain older adults’ well-being during a health crisis.

Availability of data and materials

The systematic literature review is based on already published articles. And all data analysed during this study are included in this manuscript. No additional data was used.

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Research trends in social media addiction and problematic social media use: A bibliometric analysis

Alfonso pellegrino.

1 Sasin School of Management, Chulalongkorn University, Bangkok, Thailand

Alessandro Stasi

2 Business Administration Division, Mahidol University International College, Mahidol University, Nakhon Pathom, Thailand

Veera Bhatiasevi

Associated data.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Despite their increasing ubiquity in people's lives and incredible advantages in instantly interacting with others, social media's impact on subjective well-being is a source of concern worldwide and calls for up-to-date investigations of the role social media plays in mental health. Much research has discovered how habitual social media use may lead to addiction and negatively affect adolescents' school performance, social behavior, and interpersonal relationships. The present study was conducted to review the extant literature in the domain of social media and analyze global research productivity during 2013–2022. Bibliometric analysis was conducted on 501 articles that were extracted from the Scopus database using the keywords social media addiction and problematic social media use. The data were then uploaded to VOSviewer software to analyze citations, co-citations, and keyword co-occurrences. Volume, growth trajectory, geographic distribution of the literature, influential authors, intellectual structure of the literature, and the most prolific publishing sources were analyzed. The bibliometric analysis presented in this paper shows that the US, the UK, and Turkey accounted for 47% of the publications in this field. Most of the studies used quantitative methods in analyzing data and therefore aimed at testing relationships between variables. In addition, the findings in this study show that most analysis were cross-sectional. Studies were performed on undergraduate students between the ages of 19–25 on the use of two social media platforms: Facebook and Instagram. Limitations as well as research directions for future studies are also discussed.

Introduction

Social media generally refers to third-party internet-based platforms that mainly focus on social interactions, community-based inputs, and content sharing among its community of users and only feature content created by their users and not that licensed from third parties ( 1 ). Social networking sites such as Facebook, Instagram, and TikTok are prominent examples of social media that allow people to stay connected in an online world regardless of geographical distance or other obstacles ( 2 , 3 ). Recent evidence suggests that social networking sites have become increasingly popular among adolescents following the strict policies implemented by many countries to counter the COVID-19 pandemic, including social distancing, “lockdowns,” and quarantine measures ( 4 ). In this new context, social media have become an essential part of everyday life, especially for children and adolescents ( 5 ). For them such media are a means of socialization that connect people together. Interestingly, social media are not only used for social communication and entertainment purposes but also for sharing opinions, learning new things, building business networks, and initiate collaborative projects ( 6 ).

Among the 7.91 billion people in the world as of 2022, 4.62 billion active social media users, and the average time individuals spent using the internet was 6 h 58 min per day with an average use of social media platforms of 2 h and 27 min ( 7 ). Despite their increasing ubiquity in people's lives and the incredible advantages they offer to instantly interact with people, an increasing number of studies have linked social media use to negative mental health consequences, such as suicidality, loneliness, and anxiety ( 8 ). Numerous sources have expressed widespread concern about the effects of social media on mental health. A 2011 report by the American Academy of Pediatrics (AAP) identifies a phenomenon known as Facebook depression which may be triggered “when preteens and teens spend a great deal of time on social media sites, such as Facebook, and then begin to exhibit classic symptoms of depression” ( 9 ). Similarly, the UK's Royal Society for Public Health (RSPH) claims that there is a clear evidence of the relationship between social media use and mental health issues based on a survey of nearly 1,500 people between the ages of 14–24 ( 10 ). According to some authors, the increase in usage frequency of social media significantly increases the risks of clinical disorders described (and diagnosed) as “Facebook depression,” “fear of missing out” (FOMO), and “social comparison orientation” (SCO) ( 11 ). Other risks include sexting ( 12 ), social media stalking ( 13 ), cyber-bullying ( 14 ), privacy breaches ( 15 ), and improper use of technology. Therefore, social media's impact on subjective well-being is a source of concern worldwide and calls for up-to-date investigations of the role social media plays with regard to mental health ( 8 ). Many studies have found that habitual social media use may lead to addiction and thus negatively affect adolescents' school performance, social behavior, and interpersonal relationships ( 16 – 18 ). As a result of addiction, the user becomes highly engaged with online activities motivated by an uncontrollable desire to browse through social media pages and “devoting so much time and effort to it that it impairs other important life areas” ( 19 ).

Given these considerations, the present study was conducted to review the extant literature in the domain of social media and analyze global research productivity during 2013–2022. The study presents a bibliometric overview of the leading trends with particular regard to “social media addiction” and “problematic social media use.” This is valuable as it allows for a comprehensive overview of the current state of this field of research, as well as identifies any patterns or trends that may be present. Additionally, it provides information on the geographical distribution and prolific authors in this area, which may help to inform future research endeavors.

In terms of bibliometric analysis of social media addiction research, few studies have attempted to review the existing literature in the domain extensively. Most previous bibliometric studies on social media addiction and problematic use have focused mainly on one type of screen time activity such as digital gaming or texting ( 20 ) and have been conducted with a focus on a single platform such as Facebook, Instagram, or Snapchat ( 21 , 22 ). The present study adopts a more comprehensive approach by including all social media platforms and all types of screen time activities in its analysis.

Additionally, this review aims to highlight the major themes around which the research has evolved to date and draws some guidance for future research directions. In order to meet these objectives, this work is oriented toward answering the following research questions:

• (1) What is the current status of research focusing on social media addiction?
• (2) What are the key thematic areas in social media addiction and problematic use research?
• (3) What is the intellectual structure of social media addiction as represented in the academic literature?
• (4) What are the key findings of social media addiction and problematic social media research?
• (5) What possible future research gaps can be identified in the field of social media addiction?

These research questions will be answered using bibliometric analysis of the literature on social media addiction and problematic use. This will allow for an overview of the research that has been conducted in this area, including information on the most influential authors, journals, countries of publication, and subject areas of study. Part 2 of the study will provide an examination of the intellectual structure of the extant literature in social media addiction while Part 3 will discuss the research methodology of the paper. Part 4 will discuss the findings of the study followed by a discussion under Part 5 of the paper. Finally, in Part 7, gaps in current knowledge about this field of research will be identified.

Literature review

Social media addiction research context.

Previous studies on behavioral addictions have looked at a lot of different factors that affect social media addiction focusing on personality traits. Although there is some inconsistency in the literature, numerous studies have focused on three main personality traits that may be associated with social media addiction, namely anxiety, depression, and extraversion ( 23 , 24 ).

It has been found that extraversion scores are strongly associated with increased use of social media and addiction to it ( 25 , 26 ). People with social anxiety as well as people who have psychiatric disorders often find online interactions extremely appealing ( 27 ). The available literature also reveals that the use of social media is positively associated with being female, single, and having attention deficit hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), or anxiety ( 28 ).

In a study by Seidman ( 29 ), the Big Five personality traits were assessed using Saucier's ( 30 ) Mini-Markers Scale. Results indicated that neurotic individuals use social media as a safe place for expressing their personality and meet belongingness needs. People affected by neurosis tend to use online social media to stay in touch with other people and feel better about their social lives ( 31 ). Narcissism is another factor that has been examined extensively when it comes to social media, and it has been found that people who are narcissistic are more likely to become addicted to social media ( 32 ). In this case users want to be seen and get “likes” from lots of other users. Longstreet and Brooks ( 33 ) did a study on how life satisfaction depends on how much money people make. Life satisfaction was found to be negatively linked to social media addiction, according to the results. When social media addiction decreases, the level of life satisfaction rises. But results show that in lieu of true-life satisfaction people use social media as a substitute (for temporary pleasure vs. longer term happiness).

Researchers have discovered similar patterns in students who tend to rank high in shyness: they find it easier to express themselves online rather than in person ( 34 , 35 ). With the use of social media, shy individuals have the opportunity to foster better quality relationships since many of their anxiety-related concerns (e.g., social avoidance and fear of social devaluation) are significantly reduced ( 36 , 37 ).

Problematic use of social media

The amount of research on problematic use of social media has dramatically increased since the last decade. But using social media in an unhealthy manner may not be considered an addiction or a disorder as this behavior has not yet been formally categorized as such ( 38 ). Although research has shown that people who use social media in a negative way often report negative health-related conditions, most of the data that have led to such results and conclusions comprise self-reported data ( 39 ). The dimensions of excessive social media usage are not exactly known because there are not enough diagnostic criteria and not enough high-quality long-term studies available yet. This is what Zendle and Bowden-Jones ( 40 ) noted in their own research. And this is why terms like “problematic social media use” have been used to describe people who use social media in a negative way. Furthermore, if a lot of time is spent on social media, it can be hard to figure out just when it is being used in a harmful way. For instance, people easily compare their appearance to what they see on social media, and this might lead to low self-esteem if they feel they do not look as good as the people they are following. According to research in this domain, the extent to which an individual engages in photo-related activities (e.g., taking selfies, editing photos, checking other people's photos) on social media is associated with negative body image concerns. Through curated online images of peers, adolescents face challenges to their self-esteem and sense of self-worth and are increasingly isolated from face-to-face interaction.

To address this problem the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) has been used by some scholars ( 41 , 42 ). These scholars have used criteria from the DSM-V to describe one problematic social media use, internet gaming disorder, but such criteria could also be used to describe other types of social media disorders. Franchina et al. ( 43 ) and Scott and Woods ( 44 ), for example, focus their attention on individual-level factors (like fear of missing out) and family-level factors (like childhood abuse) that have been used to explain why people use social media in a harmful way. Friends-level factors have also been explored as a social well-being measurement to explain why people use social media in a malevolent way and demonstrated significant positive correlations with lower levels of friend support ( 45 ). Macro-level factors have also been suggested, such as the normalization of surveillance ( 46 ) and the ability to see what people are doing online ( 47 ). Gender and age seem to be highly associated to the ways people use social media negatively. Particularly among girls, social media use is consistently associated with mental health issues ( 41 , 48 , 49 ), an association more common among older girls than younger girls ( 46 , 48 ).

Most studies have looked at the connection between social media use and its effects (such as social media addiction) and a number of different psychosomatic disorders. In a recent study conducted by Vannucci and Ohannessian ( 50 ), the use of social media appears to have a variety of effects “on psychosocial adjustment during early adolescence, with high social media use being the most problematic.” It has been found that people who use social media in a harmful way are more likely to be depressed, anxious, have low self-esteem, be more socially isolated, have poorer sleep quality, and have more body image dissatisfaction. Furthermore, harmful social media use has been associated with unhealthy lifestyle patterns (for example, not getting enough exercise or having trouble managing daily obligations) as well as life threatening behaviors such as illicit drug use, excessive alcohol consumption and unsafe sexual practices ( 51 , 52 ).

A growing body of research investigating social media use has revealed that the extensive use of social media platforms is correlated with a reduced performance on cognitive tasks and in mental effort ( 53 ). Overall, it appears that individuals who have a problematic relationship with social media or those who use social media more frequently are more likely to develop negative health conditions.

Social media addiction and problematic use systematic reviews

Previous studies have revealed the detrimental impacts of social media addiction on users' health. A systematic review by Khan and Khan ( 20 ) has pointed out that social media addiction has a negative impact on users' mental health. For example, social media addiction can lead to stress levels rise, loneliness, and sadness ( 54 ). Anxiety is another common mental health problem associated with social media addiction. Studies have found that young adolescents who are addicted to social media are more likely to suffer from anxiety than people who are not addicted to social media ( 55 ). In addition, social media addiction can also lead to physical health problems, such as obesity and carpal tunnel syndrome a result of spending too much time on the computer ( 22 ).

Apart from the negative impacts of social media addiction on users' mental and physical health, social media addiction can also lead to other problems. For example, social media addiction can lead to financial problems. A study by Sharif and Yeoh ( 56 ) has found that people who are addicted to social media tend to spend more money than those who are not addicted to social media. In addition, social media addiction can also lead to a decline in academic performance. Students who are addicted to social media are more likely to have lower grades than those who are not addicted to social media ( 57 ).

Research methodology

Bibliometric analysis.

Merigo et al. ( 58 ) use bibliometric analysis to examine, organize, and analyze a large body of literature from a quantitative, objective perspective in order to assess patterns of research and emerging trends in a certain field. A bibliometric methodology is used to identify the current state of the academic literature, advance research. and find objective information ( 59 ). This technique allows the researchers to examine previous scientific work, comprehend advancements in prior knowledge, and identify future study opportunities.

To achieve this objective and identify the research trends in social media addiction and problematic social media use, this study employs two bibliometric methodologies: performance analysis and science mapping. Performance analysis uses a series of bibliometric indicators (e.g., number of annual publications, document type, source type, journal impact factor, languages, subject area, h-index, and countries) and aims at evaluating groups of scientific actors on a particular topic of research. VOSviewer software ( 60 ) was used to carry out the science mapping. The software is used to visualize a particular body of literature and map the bibliographic material using the co-occurrence analysis of author, index keywords, nations, and fields of publication ( 61 , 62 ).

Data collection

After picking keywords, designing the search strings, and building up a database, the authors conducted a bibliometric literature search. Scopus was utilized to gather exploration data since it is a widely used database that contains the most comprehensive view of the world's research output and provides one of the most effective search engines. If the research was to be performed using other database such as Web Of Science or Google Scholar the authors may have obtained larger number of articles however they may not have been all particularly relevant as Scopus is known to have the most widest and most relevant scholar search engine in marketing and social science. A keyword search for “social media addiction” OR “problematic social media use” yielded 553 papers, which were downloaded from Scopus. The information was gathered in March 2022, and because the Scopus database is updated on a regular basis, the results may change in the future. Next, the authors examined the titles and abstracts to see whether they were relevant to the topics treated. There were two common grounds for document exclusion. First, while several documents emphasized the negative effects of addiction in relation to the internet and digital media, they did not focus on social networking sites specifically. Similarly, addiction and problematic consumption habits were discussed in relation to social media in several studies, although only in broad terms. This left a total of 511 documents. Articles were then limited only to journal articles, conference papers, reviews, books, and only those published in English. This process excluded 10 additional documents. Then, the relevance of the remaining articles was finally checked by reading the titles, abstracts, and keywords. Documents were excluded if social networking sites were only mentioned as a background topic or very generally. This resulted in a final selection of 501 research papers, which were then subjected to bibliometric analysis (see Figure 1 ).

Preferred reporting items for systematic reviews and meta-analysis (PRISMA) flowchart showing the search procedures used in the review.

After identifying 501 Scopus files, bibliographic data related to these documents were imported into an Excel sheet where the authors' names, their affiliations, document titles, keywords, abstracts, and citation figures were analyzed. These were subsequently uploaded into VOSViewer software version 1.6.8 to begin the bibliometric review. Descriptive statistics were created to define the whole body of knowledge about social media addiction and problematic social media use. VOSViewer was used to analyze citation, co-citation, and keyword co-occurrences. According to Zupic and Cater ( 63 ), co-citation analysis measures the influence of documents, authors, and journals heavily cited and thus considered influential. Co-citation analysis has the objective of building similarities between authors, journals, and documents and is generally defined as the frequency with which two units are cited together within the reference list of a third article.

The implementation of social media addiction performance analysis was conducted according to the models recently introduced by Karjalainen et al. ( 64 ) and Pattnaik ( 65 ). Throughout the manuscript there are operational definitions of relevant terms and indicators following a standardized bibliometric approach. The cumulative academic impact (CAI) of the documents was measured by the number of times they have been cited in other scholarly works while the fine-grained academic impact (FIA) was computed according to the authors citation analysis and authors co-citation analysis within the reference lists of documents that have been specifically focused on social media addiction and problematic social media use.

Results of the study presented here include the findings on social media addiction and social media problematic use. The results are presented by the foci outlined in the study questions.

Volume, growth trajectory, and geographic distribution of the literature

After performing the Scopus-based investigation of the current literature regarding social media addiction and problematic use of social media, the authors obtained a knowledge base consisting of 501 documents comprising 455 journal articles, 27 conference papers, 15 articles reviews, 3 books and 1 conference review. The included literature was very recent. As shown in Figure 2 , publication rates started very slowly in 2013 but really took off in 2018, after which publications dramatically increased each year until a peak was reached in 2021 with 195 publications. Analyzing the literature published during the past decade reveals an exponential increase in scholarly production on social addiction and its problematic use. This might be due to the increasingly widespread introduction of social media sites in everyday life and the ubiquitous diffusion of mobile devices that have fundamentally impacted human behavior. The dip in the number of publications in 2022 is explained by the fact that by the time the review was carried out the year was not finished yet and therefore there are many articles still in press.

Annual volume of social media addiction or social media problematic use ( n = 501).

The geographical distribution trends of scholarly publications on social media addiction or problematic use of social media are highlighted in Figure 3 . The articles were assigned to a certain country according to the nationality of the university with whom the first author was affiliated with. The figure shows that the most productive countries are the USA (92), the U.K. (79), and Turkey ( 63 ), which combined produced 236 articles, equal to 47% of the entire scholarly production examined in this bibliometric analysis. Turkey has slowly evolved in various ways with the growth of the internet and social media. Anglo-American scholarly publications on problematic social media consumer behavior represent the largest research output. Yet it is interesting to observe that social networking sites studies are attracting many researchers in Asian countries, particularly China. For many Chinese people, social networking sites are a valuable opportunity to involve people in political activism in addition to simply making purchases ( 66 ).

Global dispersion of social networking sites in relation to social media addiction or social media problematic use.

Analysis of influential authors

This section analyses the high-impact authors in the Scopus-indexed knowledge base on social networking sites in relation to social media addiction or problematic use of social media. It provides valuable insights for establishing patterns of knowledge generation and dissemination of literature about social networking sites relating to addiction and problematic use.

Table 1 acknowledges the top 10 most highly cited authors with the highest total citations in the database.

Highly cited authors on social media addiction and problematic use ( n = 501).

a Total link strength indicates the number of publications in which an author occurs.

Table 1 shows that MD Griffiths (sixty-five articles), CY Lin (twenty articles), and AH Pakpour (eighteen articles) are the most productive scholars according to the number of Scopus documents examined in the area of social media addiction and its problematic use . If the criteria are changed and authors ranked according to the overall number of citations received in order to determine high-impact authors, the same three authors turn out to be the most highly cited authors. It should be noted that these highly cited authors tend to enlist several disciplines in examining social media addiction and problematic use. Griffiths, for example, focuses on behavioral addiction stemming from not only digital media usage but also from gambling and video games. Lin, on the other hand, focuses on the negative effects that the internet and digital media can have on users' mental health, and Pakpour approaches the issue from a behavioral medicine perspective.

Intellectual structure of the literature

In this part of the paper, the authors illustrate the “intellectual structure” of the social media addiction and the problematic use of social media's literature. An author co-citation analysis (ACA) was performed which is displayed as a figure that depicts the relations between highly co-cited authors. The study of co-citation assumes that strongly co-cited authors carry some form of intellectual similarity ( 67 ). Figure 4 shows the author co-citation map. Nodes represent units of analysis (in this case scholars) and network ties represent similarity connections. Nodes are sized according to the number of co-citations received—the bigger the node, the more co-citations it has. Adjacent nodes are considered intellectually similar.

Two clusters, representing the intellectual structure of the social media and its problematic use literature.

Scholars belonging to the green cluster (Mental Health and Digital Media Addiction) have extensively published on medical analysis tools and how these can be used to heal users suffering from addiction to digital media, which can range from gambling, to internet, to videogame addictions. Scholars in this school of thought focus on the negative effects on users' mental health, such as depression, anxiety, and personality disturbances. Such studies focus also on the role of screen use in the development of mental health problems and the increasing use of medical treatments to address addiction to digital media. They argue that addiction to digital media should be considered a mental health disorder and treatment options should be made available to users.

In contrast, scholars within the red cluster (Social Media Effects on Well Being and Cyberpsychology) have focused their attention on the effects of social media toward users' well-being and how social media change users' behavior, focusing particular attention on the human-machine interaction and how methods and models can help protect users' well-being. Two hundred and two authors belong to this group, the top co-cited being Andreassen (667 co-citations), Pallasen (555 co-citations), and Valkenburg (215 co-citations). These authors have extensively studied the development of addiction to social media, problem gambling, and internet addiction. They have also focused on the measurement of addiction to social media, cyberbullying, and the dark side of social media.

Most influential source title in the field of social media addiction and its problematic use

To find the preferred periodicals in the field of social media addiction and its problematic use, the authors have selected 501 articles published in 263 journals. Table 2 gives a ranked list of the top 10 journals that constitute the core publishing sources in the field of social media addiction research. In doing so, the authors analyzed the journal's impact factor, Scopus Cite Score, h-index, quartile ranking, and number of publications per year.

Top 10 most cited and more frequently mentioned documents in the field of social media addiction.

The journal Addictive Behaviors topped the list, with 700 citations and 22 publications (4.3%), followed by Computers in Human Behaviors , with 577 citations and 13 publications (2.5%), Journal of Behavioral Addictions , with 562 citations and 17 publications (3.3%), and International Journal of Mental Health and Addiction , with 502 citations and 26 publications (5.1%). Five of the 10 most productive journals in the field of social media addiction research are published by Elsevier (all Q1 rankings) while Springer and Frontiers Media published one journal each.

Documents citation analysis identified the most influential and most frequently mentioned documents in a certain scientific field. Andreassen has received the most citations among the 10 most significant papers on social media addiction, with 405 ( Table 2 ). The main objective of this type of studies was to identify the associations and the roles of different variables as predictors of social media addiction (e.g., ( 19 , 68 , 69 )). According to general addiction models, the excessive and problematic use of digital technologies is described as “being overly concerned about social media, driven by an uncontrollable motivation to log on to or use social media, and devoting so much time and effort to social media that it impairs other important life areas” ( 27 , 70 ). Furthermore, the purpose of several highly cited studies ( 31 , 71 ) was to analyse the connections between young adults' sleep quality and psychological discomfort, depression, self-esteem, and life satisfaction and the severity of internet and problematic social media use, since the health of younger generations and teenagers is of great interest this may help explain the popularity of such papers. Despite being the most recent publication Lin et al.'s work garnered more citations annually. The desire to quantify social media addiction in individuals can also help explain the popularity of studies which try to develop measurement scales ( 42 , 72 ). Some of the highest-ranked publications are devoted to either the presentation of case studies or testing relationships among psychological constructs ( 73 ).

Keyword co-occurrence analysis

The research question, “What are the key thematic areas in social media addiction literature?” was answered using keyword co-occurrence analysis. Keyword co-occurrence analysis is conducted to identify research themes and discover keywords. It mainly examines the relationships between co-occurrence keywords in a wide variety of literature ( 74 ). In this approach, the idea is to explore the frequency of specific keywords being mentioned together.

Utilizing VOSviewer, the authors conducted a keyword co-occurrence analysis to characterize and review the developing trends in the field of social media addiction. The top 10 most frequent keywords are presented in Table 3 . The results indicate that “social media addiction” is the most frequent keyword (178 occurrences), followed by “problematic social media use” (74 occurrences), “internet addiction” (51 occurrences), and “depression” (46 occurrences). As shown in the co-occurrence network ( Figure 5 ), the keywords can be grouped into two major clusters. “Problematic social media use” can be identified as the core theme of the green cluster. In the red cluster, keywords mainly identify a specific aspect of problematic social media use: social media addiction.

Frequency of occurrence of top 10 keywords.

Keywords co-occurrence map. Threshold: 5 co-occurrences.

The results of the keyword co-occurrence analysis for journal articles provide valuable perspectives and tools for understanding concepts discussed in past studies of social media usage ( 75 ). More precisely, it can be noted that there has been a large body of research on social media addiction together with other types of technological addictions, such as compulsive web surfing, internet gaming disorder, video game addiction and compulsive online shopping ( 76 – 78 ). This field of research has mainly been directed toward teenagers, middle school students, and college students and university students in order to understand the relationship between social media addiction and mental health issues such as depression, disruptions in self-perceptions, impairment of social and emotional activity, anxiety, neuroticism, and stress ( 79 – 81 ).

The findings presented in this paper show that there has been an exponential increase in scholarly publications—from two publications in 2013 to 195 publications in 2021. There were 45 publications in 2022 at the time this study was conducted. It was interesting to observe that the US, the UK, and Turkey accounted for 47% of the publications in this field even though none of these countries are in the top 15 countries in terms of active social media penetration ( 82 ) although the US has the third highest number of social media users ( 83 ). Even though China and India have the highest number of social media users ( 83 ), first and second respectively, they rank fifth and tenth in terms of publications on social media addiction or problematic use of social media. In fact, the US has almost double the number of publications in this field compared to China and almost five times compared to India. Even though East Asia, Southeast Asia, and South Asia make up the top three regions in terms of worldwide social media users ( 84 ), except for China and India there have been only a limited number of publications on social media addiction or problematic use. An explanation for that could be that there is still a lack of awareness on the negative consequences of the use of social media and the impact it has on the mental well-being of users. More research in these regions should perhaps be conducted in order to understand the problematic use and addiction of social media so preventive measures can be undertaken.

From the bibliometric analysis, it was found that most of the studies examined used quantitative methods in analyzing data and therefore aimed at testing relationships between variables. In addition, many studies were empirical, aimed at testing relationships based on direct or indirect observations of social media use. Very few studies used theories and for the most part if they did they used the technology acceptance model and social comparison theories. The findings presented in this paper show that none of the studies attempted to create or test new theories in this field, perhaps due to the lack of maturity of the literature. Moreover, neither have very many qualitative studies been conducted in this field. More qualitative research in this field should perhaps be conducted as it could explore the motivations and rationales from which certain users' behavior may arise.

The authors found that almost all the publications on social media addiction or problematic use relied on samples of undergraduate students between the ages of 19–25. The average daily time spent by users worldwide on social media applications was highest for users between the ages of 40–44, at 59.85 min per day, followed by those between the ages of 35–39, at 59.28 min per day, and those between the ages of 45–49, at 59.23 per day ( 85 ). Therefore, more studies should be conducted exploring different age groups, as users between the ages of 19–25 do not represent the entire population of social media users. Conducting studies on different age groups may yield interesting and valuable insights to the field of social media addiction. For example, it would be interesting to measure the impacts of social media use among older users aged 50 years or older who spend almost the same amount of time on social media as other groups of users (56.43 min per day) ( 85 ).

A majority of the studies tested social media addiction or problematic use based on only two social media platforms: Facebook and Instagram. Although Facebook and Instagram are ranked first and fourth in terms of most popular social networks by number of monthly users, it would be interesting to study other platforms such as YouTube, which is ranked second, and WhatsApp, which is ranked third ( 86 ). Furthermore, TikTok would also be an interesting platform to study as it has grown in popularity in recent years, evident from it being the most downloaded application in 2021, with 656 million downloads ( 87 ), and is ranked second in Q1 of 2022 ( 88 ). Moreover, most of the studies focused only on one social media platform. Comparing different social media platforms would yield interesting results because each platform is different in terms of features, algorithms, as well as recommendation engines. The purpose as well as the user behavior for using each platform is also different, therefore why users are addicted to these platforms could provide a meaningful insight into social media addiction and problematic social media use.

Lastly, most studies were cross-sectional, and not longitudinal, aiming at describing results over a certain point in time and not over a long period of time. A longitudinal study could better describe the long-term effects of social media use.

This study was conducted to review the extant literature in the field of social media and analyze the global research productivity during the period ranging from 2013 to 2022. The study presents a bibliometric overview of the leading trends with particular regard to “social media addiction” and “problematic social media use.” The authors applied science mapping to lay out a knowledge base on social media addiction and its problematic use. This represents the first large-scale analysis in this area of study.

A keyword search of “social media addiction” OR “problematic social media use” yielded 553 papers, which were downloaded from Scopus. After performing the Scopus-based investigation of the current literature regarding social media addiction and problematic use, the authors ended up with a knowledge base consisting of 501 documents comprising 455 journal articles, 27 conference papers, 15 articles reviews, 3 books, and 1 conference review.

The geographical distribution trends of scholarly publications on social media addiction or problematic use indicate that the most productive countries were the USA (92), the U.K. (79), and Turkey ( 63 ), which together produced 236 articles. Griffiths (sixty-five articles), Lin (twenty articles), and Pakpour (eighteen articles) were the most productive scholars according to the number of Scopus documents examined in the area of social media addiction and its problematic use. An author co-citation analysis (ACA) was conducted which generated a layout of social media effects on well-being and cyber psychology as well as mental health and digital media addiction in the form of two research literature clusters representing the intellectual structure of social media and its problematic use.

The preferred periodicals in the field of social media addiction and its problematic use were Addictive Behaviors , with 700 citations and 22 publications, followed by Computers in Human Behavior , with 577 citations and 13 publications, and Journal of Behavioral Addictions , with 562 citations and 17 publications. Keyword co-occurrence analysis was used to investigate the key thematic areas in the social media literature, as represented by the top three keyword phrases in terms of their frequency of occurrence, namely, “social media addiction,” “problematic social media use,” and “social media addiction.”

This research has a few limitations. The authors used science mapping to improve the comprehension of the literature base in this review. First and foremost, the authors want to emphasize that science mapping should not be utilized in place of established review procedures, but rather as a supplement. As a result, this review can be considered the initial stage, followed by substantive research syntheses that examine findings from recent research. Another constraint stems from how 'social media addiction' is defined. The authors overcame this limitation by inserting the phrase “social media addiction” OR “problematic social media use” in the search string. The exclusive focus on SCOPUS-indexed papers creates a third constraint. The SCOPUS database has a larger number of papers than does Web of Science although it does not contain all the publications in a given field.

Although the total body of literature on social media addiction is larger than what is covered in this review, the use of co-citation analyses helped to mitigate this limitation. This form of bibliometric study looks at all the publications listed in the reference list of the extracted SCOPUS database documents. As a result, a far larger dataset than the one extracted from SCOPUS initially has been analyzed.

The interpretation of co-citation maps should be mentioned as a last constraint. The reason is that the procedure is not always clear, so scholars must have a thorough comprehension of the knowledge base in order to make sense of the result of the analysis ( 63 ). This issue was addressed by the authors' expertise, but it remains somewhat subjective.

Implications

The findings of this study have implications mainly for government entities and parents. The need for regulation of social media addiction is evident when considering the various risks associated with habitual social media use. Social media addiction may lead to negative consequences for adolescents' school performance, social behavior, and interpersonal relationships. In addition, social media addiction may also lead to other risks such as sexting, social media stalking, cyber-bullying, privacy breaches, and improper use of technology. Given the seriousness of these risks, it is important to have regulations in place to protect adolescents from the harms of social media addiction.

Regulation of social media platforms

One way that regulation could help protect adolescents from the harms of social media addiction is by limiting their access to certain websites or platforms. For example, governments could restrict adolescents' access to certain websites or platforms during specific hours of the day. This would help ensure that they are not spending too much time on social media and are instead focusing on their schoolwork or other important activities.

Another way that regulation could help protect adolescents from the harms of social media addiction is by requiring companies to put warning labels on their websites or apps. These labels would warn adolescents about the potential risks associated with excessive use of social media.

Finally, regulation could also require companies to provide information about how much time each day is recommended for using their website or app. This would help adolescents make informed decisions about how much time they want to spend on social media each day. These proposed regulations would help to protect children from the dangers of social media, while also ensuring that social media companies are more transparent and accountable to their users.

Parental involvement in adolescents' social media use

Parents should be involved in their children's social media use to ensure that they are using these platforms safely and responsibly. Parents can monitor their children's online activity, set time limits for social media use, and talk to their children about the risks associated with social media addiction.

Education on responsible social media use

Adolescents need to be educated about responsible social media use so that they can enjoy the benefits of these platforms while avoiding the risks associated with addiction. Education on responsible social media use could include topics such as cyber-bullying, sexting, and privacy breaches.

Research directions for future studies

A content analysis was conducted to answer the fifth research questions “What are the potential research directions for addressing social media addiction in the future?” The study reveals that there is a lack of screening instruments and diagnostic criteria to assess social media addiction. Validated DSM-V-based instruments could shed light on the factors behind social media use disorder. Diagnostic research may be useful in order to understand social media behavioral addiction and gain deeper insights into the factors responsible for psychological stress and psychiatric disorders. In addition to cross-sectional studies, researchers should also conduct longitudinal studies and experiments to assess changes in users' behavior over time ( 20 ).

Another important area to examine is the role of engagement-based ranking and recommendation algorithms in online habit formation. More research is required to ascertain how algorithms determine which content type generates higher user engagement. A clear understanding of the way social media platforms gather content from users and amplify their preferences would lead to the development of a standardized conceptualization of social media usage patterns ( 89 ). This may provide a clearer picture of the factors that lead to problematic social media use and addiction. It has been noted that “misinformation, toxicity, and violent content are inordinately prevalent” in material reshared by users and promoted by social media algorithms ( 90 ).

Additionally, an understanding of engagement-based ranking models and recommendation algorithms is essential in order to implement appropriate public policy measures. To address the specific behavioral concerns created by social media, legislatures must craft appropriate statutes. Thus, future qualitative research to assess engagement based ranking frameworks is extremely necessary in order to provide a broader perspective on social media use and tackle key regulatory gaps. Particular emphasis must be placed on consumer awareness, algorithm bias, privacy issues, ethical platform design, and extraction and monetization of personal data ( 91 ).

From a geographical perspective, the authors have identified some main gaps in the existing knowledge base that uncover the need for further research in certain regions of the world. Accordingly, the authors suggest encouraging more studies on internet and social media addiction in underrepresented regions with high social media penetration rates such as Southeast Asia and South America. In order to draw more contributions from these countries, journals with high impact factors could also make specific calls. This would contribute to educating social media users about platform usage and implement policy changes that support the development of healthy social media practices.

The authors hope that the findings gathered here will serve to fuel interest in this topic and encourage other scholars to investigate social media addiction in other contexts on newer platforms and among wide ranges of sample populations. In light of the rising numbers of people experiencing mental health problems (e.g., depression, anxiety, food disorders, and substance addiction) in recent years, it is likely that the number of papers related to social media addiction and the range of countries covered will rise even further.

Data availability statement

Author contributions.

AP took care of bibliometric analysis and drafting the paper. VB took care of proofreading and adding value to the paper. AS took care of the interpretation of the findings. All authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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