social media and democracy research paper

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• Published: 07 November 2022

A systematic review of worldwide causal and correlational evidence on digital media and democracy

• Philipp Lorenz-Spreen   ORCID: orcid.org/0000-0001-6319-4154 1   na1 ,
• Lisa Oswald   ORCID: orcid.org/0000-0002-8418-282X 2   na1 ,
• Stephan Lewandowsky   ORCID: orcid.org/0000-0003-1655-2013 3 , 4 &
• Ralph Hertwig   ORCID: orcid.org/0000-0002-9908-9556 1  

Nature Human Behaviour volume  7 ,  pages 74–101 ( 2023 ) Cite this article

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One of today’s most controversial and consequential issues is whether the global uptake of digital media is causally related to a decline in democracy. We conducted a systematic review of causal and correlational evidence ( N  = 496 articles) on the link between digital media use and different political variables. Some associations, such as increasing political participation and information consumption, are likely to be beneficial for democracy and were often observed in autocracies and emerging democracies. Other associations, such as declining political trust, increasing populism and growing polarization, are likely to be detrimental to democracy and were more pronounced in established democracies. While the impact of digital media on political systems depends on the specific variable and system in question, several variables show clear directions of associations. The evidence calls for research efforts and vigilance by governments and civil societies to better understand, design and regulate the interplay of digital media and democracy.

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The ongoing heated debate on the opportunities and dangers that digital media pose to democracy has been hampered by disjointed and conflicting results (for recent overviews, see refs. 1 , 2 , 3 , 4 ). Disagreement about the role of new media is not a novel phenomenon; throughout history, evolving communication technologies have provoked concerns and debates. One likely source of concern is the dual-use dilemma, that is, the inescapable fact that technologies can be used for both noble and malicious aims. For instance, during the Second World War, radio was used as a propaganda tool by Nazi Germany 5 , whereas allied radio, such as the BBC, supported resistance against the Nazi regime, for example, by providing tactical information on allied military activities 6 , 7 . In the context of the Rwandan genocide, radio was used to incite Rwandan Hutus to massacre the country’s Tutsi minority 8 . In the aftermath of the genocide, using the same means to cause different ends, the radio soap opera ‘Musekeweya’ successfully reduced intergroup prejudice in a year-long field experiment 9 , 10 .

Digital media appears to be another double-edged sword. On the one hand, it can empower citizens, as demonstrated in movements such as the Arab Spring 11 , Fridays for Future and #MeToo 12 . On the other hand, digital media can also be instrumental in inciting destructive behaviours and tendencies such as polarization and populism 13 , as well as fatal events such as the attack on the United States Capitol in January 2021. Relatedly, the way political leaders use or avoid digital media can vary greatly depending on the political context. Former US President Trump used it to spread numerous lies ranging from claims about systematic voter fraud in the 2020 presidential election to claims about the harmlessness of Covid-19. In spring 2022, Russian President Putin had banned most social media platforms that would bypass the state-controlled classical media, probably to prevent access to information about his army’s attack on Ukraine 14 . At the same time, Ukrainian President Zelensky has skilfully used social media to boost Ukrainian morale and engage in the information war with Russia. Examples of the dual-use dilemma of digital media abound.

Clearly, digital media can foster liberation, democratization and participation, but can also play an important role in eroding democracy. The role of digital media is further complicated because unlike other communication technologies, it enables individuals to easily produce and disseminate content themselves, and offers largely frictionless interaction between users. These properties have not only moved the self-organized political behaviour of citizens into the spotlight 15 , but have also shifted power to large digital media platforms. Unlike broadcasters, digital media platforms typically do not create content; instead, their power lies in providing and governing a digital infrastructure. Although that infrastructure could serve as an online public sphere 16 , it is the platforms that exert much control over the dynamics of information flow.

Our goal is to advance the scientific and public debate on the relationship between digital media and democracy by providing an evidence-based picture of this complex constellation. To this end, we comprehensively reviewed and synthesized the available scientific knowledge 17 on the link between digital media and various politically important variables such as participation, trust and polarization.

We aimed to answer the pre-registered question “If, to what degree and in which contexts, do digital media have detrimental effects on democracy?” (pre-registered protocol, including research question and search strategy, at https://osf.io/7ry4a/ ). This two-stage question encompasses, first, the assessment of the direction of effects and, second, how these effects play out as a function of political contexts.

A major difficulty facing researchers and policy makers is that most studies relating digital media use to political attitudes and behaviours are correlational. Because it is nearly impossible to simulate democracy in the laboratory, researchers are forced to rely on observational data that typically only provide correlational evidence. We therefore pursued two approaches. First, we collected and synthesized a broad set of articles that examine associations between digital media use and different political variables. We then conducted an in-depth analysis of the small subset of articles reporting causal evidence. This two-step approach permitted us to focus on causal effects while still taking the full spectrum of correlational evidence into account.

For the present purpose, we adopted a broad understanding of digital media, ranging from general internet access to the use of specific social media platforms, including exposure to certain types of content on these platforms. To be considered as a valid digital media variable in our review, information or discussion forums must be hosted via the internet or need to describe specific features of online communication. For example, we considered the online outlets of traditional newspapers or TV channels as digital source of political information but not the original traditional media themselves. We provide an overview of digital media variables present in our review sample in Fig. 1d and discriminate in our analyses between the two overarching types of digital media: internet, broadly defined, on the one hand and social media in particular on the other hand.

a , Combinations of variables in the sample: digital media (A), political variables (B) and content features such as selective exposure or misinformation (C). Numbers in brackets count articles in our sample that measure an association between variables. b , Geographic distribution of articles that reported site of data collection. c , d , Distribution of measurements (counted separately whenever one article reported several variables) over combinations of outcome variables and methods ( c ) and over combinations of outcome variables and digital media variables ( d ).

We further aimed to synthesize evidence on a broad spectrum of political attitudes and behaviours that are relevant to basic democratic principles 18 . We therefore grounded our assessment of political variables in the literature that examines elements of modern democracies that are considered essential to their functioning, such as citizens’ basic trust in media and institutions 19 , a well-informed public 20 , an active civil society 21 , 22 and exposure to a variety of opinions 23 , 24 . We also included phenomena that are considered detrimental to the functioning of democracies, including open discrimination against people 25 , political polarization to the advantage of political extremists and populists 26 and social segregation in homogeneous networks 23 , 27 .

The political variables in focus are themselves multidimensional and may be heterogeneous and conflicting. For example, polarization encompasses partisan sorting 28 , affective polarization 29 , issue alignment 30 , 31 and a number of other phenomena (see ref. 32 for an excellent literature review on media effects on variations of ideological and affective polarization). For our purpose, however, we take a broader perspective, examining and comparing across different political variables the directions—beneficial or detrimental to democracy—in which digital media effects play out.

Notwithstanding the nuances within each dimension of political behaviour, wherever possible we explicitly interpreted each change in a political variable as tending to be either beneficial or detrimental to democracy. Even though we tried to refrain from normative judgements, the nature of our research question required us to interpret the reported evidence regarding its relation to democracy. For example, an increase in political knowledge is generally considered to be beneficial under the democratic ideal of an informed citizenry 20 . Similarly, a certain level of trust in democratic institutions is crucial for a functioning democracy 33 . By contrast, various forms of polarization (particularly affective polarization) tend to split societies into opposing camps and threaten democratic decision-making 34 , 35 . Likewise, populist politics that are often coupled with right-wing nationalist ideologies, artificially divide society into a corrupt ‘elite’ that is opposed by ‘the people’, which runs counter to the ideals of a pluralistic democracy and undermines citizens’ trust in politics and the media 36 , 37 . We therefore considered polarization and populism, for example, to be detrimental to democracy.

There are already some systematic reviews of subsets of associations between political behaviour and media use that fall within the scope of our analysis, including reviews of the association between media and radicalization 38 , 39 , polarization 32 , hate speech 40 , participation 41 , 42 , 43 , 44 , 45 , echo chambers 46 and campaigning on Twitter 47 . These extant reviews, however, did not contrast and integrate the wide range of politically relevant variables into one comprehensive analysis—an objective that we pursue here. For the most relevant review articles, we matched the references provided in them with our reference list (see Materials and Methods for details). Importantly, and unlike some extant reviews, our focus is not on institutions, the political behaviour of political elites (for example, their strategic use of social media; see refs. 47 , 48 ), or higher-level outcomes (for example, policy innovation in governments 49 ). We also did not consider the effects of traditional media (for example, television or radio) or consumption behaviours that are not specific to digital media (for example, selective exposure 50 ). Furthermore, we did not focus on the microscopic psychological mechanisms that could shape polarization on social media (for a review, see ref. 51 ). For reasons of external validity, we omitted small-scale laboratory-only experiments (for example, see ref. 52 ), but included field experiments in our review. We included studies using a variety of methods—from surveys to large-scale analyses of social media data—and across different disciplines that are relevant to our research question. Details on the inclusion and exclusion criteria are provided in Materials and Methods. Our goal for this knowledge synthesis is to provide a nuanced foundation of shared facts for a constructive stage in the academic but also societal debate about the future of digital media and their role in democracy. In our view, this debate and the future design of digital media for democracy require a comprehensive assessment of its impact. We therefore not only focus on individual dimensions of political behaviour but also compare these dimensions and the methods by which they have been researched so far, thus going beyond the extant reviews. This approach aims to stimulate research that fills evidence gaps and establishes missing links that only become apparent when comparing the dimensions.

After conducting a pre-registered search (most recent update 15 September 2021) and selection process, we arrived at a final sample of N  = 496 articles. For further analysis, we classified them by the set of variables between which they report associations: type of digital media (for example, social media, online news), political variables (for example, trust, participation) and characteristics of the information ecology (for example, misinformation, selective exposure), as depicted in Fig. 1a . Each article was coded according to the combination of these variables as well as the method, specific outcome variable and, if applicable, the direction of association and potential moderator variables (see Materials and Methods for details). The resulting table of the fully coded set of studies can be found at https://osf.io/7ry4a/ , alongside the code for the analyses and visualizations offered here.

Figure 1 reports the composition of the set of included articles. Figure 1a confirms that the search query mainly returned articles concerned with the most relevant associations between digital media and political outcomes. Most of the articles were published in the last 5 years, highlighting the fast growth of interest in the link between digital media and democracy. Articles span a range of disciplines, including political science, psychology, computational science and communication science. Although a preponderance of articles focused on the United States, there was still a large geographical variation overall (see Fig. 1b ).

Figure 1c shows the distribution of measurements (counted separately when one article reported several outcomes) across methods and political variables. Our search query was designed to capture a broad range of politically relevant variables, which meant that we had to group them into broader categories. The ten most frequently reported categories of variables were trust in institutions, different variants of political participation (for example, voter turnout or protest participation), exposure to diverse viewpoints in the news, political knowledge, political expression, measures of populism (for example, support for far-right parties or anti-minority rhetoric), prevalence and spread of misinformation, measures of polarization (for example, negative attitudes towards political opponents or fragmented and adversarial discourse), homophily in social networks (that is, social connections between like-minded individuals) and online hate (that is, hate speech or hate crime). Similarly, the distribution of outcomes and associated digital media variables in Fig. 1d shows that many studies focused on political information online, and specifically political information on social media, in combination with political polarization and participation, while other digital media variables, such as messenger platforms are less explored. The full table, including the reported political variables within each category, can be found at https://osf.io/7ry4a/ . Figure 1 also reveals gaps in the literature, such as rarely explored geographical regions (for example, Africa) and under-studied methods–variable combinations (for example, involving the combination of data sources such as social media data with survey or secondary data).

Direction of associations

In the first part of our research question, we ask whether the available evidence suggests that the effects of digital media are predominantly beneficial or detrimental to democracy. To find an answer, we first selected subsets of articles that addressed the ten most frequently studied categories of political variables (hereafter simply referred to as political variables). We did not test specific hypotheses in our review. A total of N  = 354 associations were reported for these variables (when an article examined two relevant outcome variables, two associations were counted). The independent variable across these articles was always a measure of the usage of some type of digital media, such as online news consumption or social media uptake. Statistically speaking, the independent variables can be positively or negatively associated with the political outcome variable. For instance, more digital media use could be associated with more expression of hate (positive association), less expression of hate (negative association), or not associated at all. We decided to present relationships not at a statistical level but at a conceptual level. We therefore classified each observed statistical association as beneficial or detrimental depending on whether its direction was aligned or misaligned with democracy. For example, a positive statistical association between digital media use and hate speech was coded as a detrimental association; by contrast, a positive statistical association between digital media use and participation was coded as beneficial. Throughout, we represent beneficial associations in turquoise and detrimental associations in orange, irrespective of the underlying statistical polarity.

Figure 2 provides an overview of the ten most frequently studied political variables and the reported directions—colour-coded in terms of whether they are beneficial or detrimental to democracy—of each of their associations with digital media use. This overview encompasses both correlational and causal evidence. Some findings in Fig. 2 suggest that digital media can foster democratic objectives. First, the associations reported for participation point mostly in beneficial directions for democracy (aligned with previous results 45 ), including a wide range of political and civic behaviour (Fig. 1d ), from low-effort participation such as liking/sharing political messages on social media to high-cost activities such as protesting in oppressive regimes. Second, measures of political knowledge and diversity of news exposure appear to be associated with digital media in beneficial ways, but the overall picture was slightly less clear. Third, the literature is also split on how political expression is associated with digital media. Articles reporting beneficial associations between digital media and citizens’ political expression were opposed by a number of articles describing detrimental associations. These detrimental associations relate to the ‘spiral of silence’ idea, that is, the notion that people’s willingness to express their political opinions online depends on the perceived popularity of their opinions (see relevant overview articles 53 , 54 ).

Directions of associations are reported for various political variables (see Fig. 1d for a breakdown). Insets show examples of the distribution of associations with trust, news exposure, polarization and network homophily over the different digital media variables with which they were associated.

Fourth, we observed consistent detrimental associations for a number of variables. Specifically, the associations with trust in institutions were overwhelmingly pointing in directions detrimental to a functioning democracy. Measures of hate, polarization and populism were also widely reported to have detrimental associations with digital media use in the clear majority of articles. Likewise, increased digital media use was often associated with a greater exposure to misinformation. Finally, we also found that digital media were associated with homophily in social networks in detrimental ways (mostly measured on social media, and here especially on Twitter), but the pattern of evidence was a little less consistent. Differences in the consistency of results were also reflected when broken down along associated digital media variables (see insets in Fig. 2 ). For instance, both trust and polarization measures were consistently associated with media use across types of digital media ranging from social media to political information online; in contrast, results for homophily were concentrated on social media and especially on Twitter, while measurements of news exposure were mostly concentrated on political information online.This points not only to different operationalizations of related outcome measures, such as diverse information exposure and homophilic network structures, but also to differences between the distinct domains of digital media in which these very related phenomena are measured. Similar observations can be made when separating associations between general types of digital media: social media vs internet more broadly (Supplementary Fig. 1 ).

Next, we distinguished between articles reporting correlational versus causal evidence and focused on the small subset of articles reporting the latter ( N  = 24). We excluded causal evidence on the effects of voting advice applications from our summary as a very specific form of digital media, explicitly constructed to inform vote choices, and already extensively discussed in a meta-analysis 55 .

Causal inference

Usually, the absence of randomized treatment assignment, an inescapable feature of observational data (for example, survey data), precludes the identification of causal effects because individuals differ systematically on variables other than the treatment (or independent) variable. However, under certain conditions, it is possible to rule out non-causal explanations for associations, even in studies without random assignment that are based on observational data (see refs. 56 , 57 , 58 ). For a more detailed explanation of the fundamental principles of causal inference, see Supplementary Material page 5 and, for example, the work of the 2021 laureates of the Nobel Memorial Prize in Economics 56 , 57 , 58 .

Common causal inference techniques that were used in our sample include instrumental variable designs that introduce exogenous variation in the treatment variable 59 , 60 , 61 , 62 , 63 , matching approaches to explicitly balance treatment and control groups 64 , 65 , 66 , and panel designs that account for unobserved confounders with unit and/or time-fixed effects 67 , 68 . We also found multiple large-scale field experiments conducted on social media platforms 69 , 70 , 71 , 72 as well as various natural experiments 59 , 61 , 62 , 73 .

Figure 3 summarizes the findings and primary causal inference techniques of these articles. Again, causal effects were coded as beneficial for or detrimental to democracy. This figure is structured according to whether evidence stemmed from established democracies or from emerging democracies and authoritarian regimes, adopting classifications from the Liberal Democracy Index provided by the Varieties of Democracy project 18 . In some autocratic regimes (for example, China), it is particularly difficult to interpret certain effects. For example, a loss of trust in government suggests a precarious development for an established democracy; in authoritarian regimes, however, it may indicate a necessary step toward overcoming an oppressive regime and, eventually, progressing towards a more liberal and democratic system. Instead of simply adopting the authors’ interpretation of the effects or imposing our own interpretation of effects in authoritarian contexts, we leave this interpretation to the reader (denoted in purple in the figure). The overall picture converges closely with the one drawn in Fig. 2 . We found general trends of digital media use increasing participation and knowledge but also increasing political polarization and decreasing trust that mostly aligned with correlational evidence.

Each box represents one article. Treatments (T) are in white boxes on the left, political outcome (O) variables in coloured boxes on the right; M denotes mediators; H represents sources of effect heterogeneity or moderators. Positive (+) and negative (−) signs at paths indicate reported direction of effects. Location of sample indicated in top right corner of boxes, primary causal inference strategy in bottom left. Strategies include statistical estimation strategies such as instrumental variables (IV), matching and panel designs (PD) that use, for example, fixed effects (FE) or difference in difference (DiD) for causal estimation, as well as lab or field experiments (for example, field experiments rolled out on various platforms that are often supplemented with IV estimation to account for imperfect compliance). Detrimental effects on liberal democracy are shown in orange, beneficial effects in turquoise, effects open to interpretation in purple and null effects in grey. Solid arrows represent pathways for which authors provide causal identification strategies, dashed arrows represent descriptive (mediation) pathways.

Effects on key political variables

In the following sections, we provide a short synopsis of the results, point to conflicting trends and highlight some examples of the full set of correlational and causal evidence, reported in Figs. 2 and 3 , for six variables that we found to be particularly crucial for democracy: participation, trust, political knowledge, polarization, populism, network structures and news exposure. The chosen examples are stand-ins and illustrations of the general trends.

Participation

Consistent with past meta-analyses 42 , 43 , 45 , the body of correlational evidence supported a beneficial association between digital media use and political participation and mobilization.

Causal analyses of the effects of digital media on political participation in established democracies mostly studied voting and voter turnout 64 , 67 , 71 , 74 , 75 , 76 ; articles concerned with other regions of the world rather focused on political protest behaviour 59 , 61 , 66 . Other articles considered online political participation 65 , 71 . One study, applying causal mediation analysis to assess a causal mechanism 77 , found that information-oriented social media use affects political participation, mediated or enabled through the user’s online political efficacy 65 . Overall, our evidence synthesis found largely beneficial mobilizing effects for political participation across this set of articles. Our search did not identify any studies that examined causal effects of digital media on political participation in authoritarian regimes in Africa or the Middle East.

Many articles in our sample found detrimental associations between digital media and various dimensions of trust (Fig. 2 ). For example, detrimental associations were found for trust in governments and politics 59 , 60 , 66 , 78 , 79 , 80 , 81 , 82 , trust in media 83 , and social and institutional trust 84 . During the COVID-19 pandemic, digital media use was reported to be negatively associated with trust in vaccines 85 , 86 . Yet the results about associations with trust are not entirely homogeneous. One multinational survey found beneficial associations with trust in science 87 ; others found increasing trust in democracy with digital media use in Eastern and Central European samples 88 , 89 . Nevertheless, the large majority of reported associations between digital media use and trust appear to be detrimental for democracy. While the evidence stems mostly from surveys, results gathered with other methods underpin these findings (Fig. 2 inset).

The majority of articles identifying causal effects also find predominantly detrimental effects of digital media on trust. A field experiment in the United States that set browser defaults to partisan media outlets 37 found a long-term loss of trust in mainstream media. Studies examining social trust as a central component of social capital find consistent detrimental effects of social media use 84 ; in contrast, no effects of broadband internet in general on social trust was found 90 . In authoritarian regimes in Asia, increasing unrestricted internet access decreased levels of trust in the political system 59 , 73 , 91 . This finding confirms the predominant association observed in most other countries. Yet it also illustrates how digital media is a double-edged sword, depending on the political context: by reducing trust in institutions, digital media can threaten existing democracies as well as foster emerging democratic developments in authoritarian regimes.

Political knowledge

The picture was less clear for associations between the consumption of digital media and political knowledge. Still, the majority of associations point in beneficial directions and were found in both cross-sectional surveys 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 and panel surveys 100 , 101 , 102 . Studies linking web-tracking and survey data showed increased learning about politics 103 , but also a turning away from important topics 104 , whereas other experiments demonstrated an overall beneficial effect of digital media on issue salience 105 . These findings, however, stand in contrast to other studies that find a detrimental association between political knowledge and digital media use 106 , 107 , 108 , 109 , 110 .

The body of causal evidence on political knowledge also tends to paint a relatively promising picture. Multiple articles found that engagement with digital media increased political knowledge 67 , 70 , 72 , 74 and that engagement with political content on social media increased political interest among adolescents 111 . In line with these findings, it has been reported that political messages on social media, as well as faster download speed, can increase information-seeking in the political domain 67 , 71 . By contrast, there is evidence for a decrease in political knowledge 112 , which is mediated through the news-finds-me effect: social media users believe that actively seeking out news is no longer required to stay informed, as they expect to be presented with important information.

It is important to note that most of these effects are accompanied by considerable heterogeneity in the population that benefits and the type of digital media. For example, politically interested individuals showed higher knowledge acquisition when engaging with Twitter, whereas the opposite effects emerged for engagement with Facebook 113 . Furthermore, there is evidence that the news-finds-me effect on social media can be mitigated when users consult alternative news sources 112 .

Polarization

Most articles found detrimental associations between digital media and different forms of political polarization 114 , 115 , 116 , 117 , 118 . Our review obtained evidence for increasing outgroup polarization on social media in a range of political contexts and on various platforms 119 , 120 , 121 , 122 . Increasing polarization was also linked to exposure to viewpoints opposed to one’s own on social media feeds 69 , 123 . Articles comparing several political systems found associations that were country-dependent 124 , again highlighting the importance of political context 125 . Nevertheless, high digital media use was for the most part linked to higher levels of polarization, although there was some evidence for balanced online discourse without pronounced patterns of polarization 126 , 127 , 128 , as well as evidence for potentially depolarizing tendencies 129 .

The body of causal articles largely supported the detrimental associations of digital media that emerged, by and large, in the correlational articles. Among established democracies, both social media use and overall internet use increased political polarization 63 , 70 . This was also the case for an experimental treatment that exposed users to opposing views on Twitter 69 . However, some findings run counter to the latter result 130 : in a 2 month field experiment, exposure to counter-attitudinal news on Facebook reduced affective polarization (the authors used opposing news outlets as treatment instead of opinions on social media). Furthermore, one other field experiment did not find evidence that exposure to partisan online news substantively shifted political opinions but found a long-term loss of trust in mainstream media 37 . Still, taking all evidence into account, the overall picture remains largely consistent on the detrimental association between digital media and political polarization, including some but not all causal evidence.

Articles on populism in our review examined either vote share and other popularity indicators for populist parties or the prevalence of populist messages and communication styles on digital media. Overall, articles using panel surveys, tracking data and methods linking surveys to social media data consistently found that digital media use was associated with higher levels of populism. For example, digital platforms were observed to benefit populist parties more than they benefit established politicians 131 , 132 , 133 , 134 . In a panel survey in Germany, a decline in trust that accompanied increasing digital media consumption was also linked to a turn towards the hard-right populist AfD party 80 . This relationship might be connected to AfD’s greater online presence, relative to other German political parties 132 , even though these activities might be partly driven by automated accounts. There is also evidence for an association between increased social media use and online right-wing radicalization in Austria, Sweden and Australia 135 , 136 , 137 . Only a minority of articles found no relationship or the reverse relationship between digital media and populism 138 , 139 , 140 . For instance, in Japan, internet exposure was associated with increased tolerance towards foreigners 141 .

Similarly, most causal inference studies linked increased populism to digital media use. For instance, digital media use in Europe led to increased far-right populist support 63 , 142 , and there was causal evidence that digital media can propagate ethnic hate crimes in both democratic and authoritarian countries 62 , 68 . Leaving the US and European political context, in Malaysia, internet exposure was found to cause decreasing support for the authoritarian, populist government 60 .

Echo chambers and news exposure

The evidence on echo chambers points in different directions depending on the outcome measure. On the one hand, when looking at news consumption, several articles showed that social media and search engines diversify people’s news diets 67 , 143 , 144 , 145 , 146 . On the other hand, when considering social networks and the impact of digital media on homophilic structures, the literature contains consistent reports of ideologically homogeneous social clusters 147 , 148 , 149 , 150 , 151 . This underscores an important point: some seemingly paradoxical results can potentially be resolved by looking more closely at context and specific outcome measurement (see also Supplementary Fig. 2 ). The former observation of diverse news exposure might fit with the beneficial relationship between digital media and knowledge reported in refs. 67 , 74 , 94 , 95 , 102 , and the homophilic social structures could be connected to the prevalence of hate speech and anti-outgroup sentiments 120 , 152 , 153 , 154 , 155 .

Heterogeneity

We now turn to the second part of our research question and analyse the effects of digital media use in light of different political contexts. Figure 4 shows the geographical distribution of effect directions around the globe. Notably, most beneficial effects on democracy were found in emerging democracies in South America, Africa and South Asia. Mixed effects, by contrast, were distributed across Europe, the United States, Russia and China. Similarly, detrimental outcomes were mainly found in Europe, the United States and partly Russia, although this may reflect a lack of studies undertaken in authoritarian contexts. These patterns are also shown in Fig. 4c,d , where countries are listed according to the Liberal Democracy Index. Moderators—variables such as partisanship and news consumption that are sources of effect heterogeneity—displayed in Supplementary Fig. 3 also show slight differences between outcomes. Beneficial outcomes seemed to be more often moderated by political interest and news consumption, whereas detrimental outcomes tended to be moderated by political position and partisanship.

a , Geographical distribution of reported associations for the variables trust, knowledge, participation, exposure and expression. Pie charts show the composition of directions for each country studied. b , Geographic representation of reported associations for the variables hate, polarization, populism, homophily and misinformation. c , Data and variables in a , in absolute numbers of reported associations and sorted along the Liberal Democracy Index 18 . d , Data and variables in b , in absolute numbers of reported associations and sorted along the Liberal Democracy Index.

Furthermore, many causal articles acknowledge that effects differ between subgroups of their sample when including interaction terms in their statistical models. For example, the polarizing effects of digital media differ between Northern and Southern European media systems 142 : while consumption of right-leaning digital media increased far-right votes, especially in Southern Europe, the consumption of news media and public broadcasting in Northern European media systems with high journalistic standards appears to mitigate these effects. Another example of differential effects between subgroups was found in Russia, where the effects of social media on xenophobic violence were only present in areas with pre-existing nationalist sentiment. This effect was especially pronounced for hate crimes with a larger number of perpetrators, indicating that digital media was serving a coordinating function. In summary, a range of articles found heterogeneity in effects for varying levels of political interest 67 , 113 , political orientation 63 , 69 , 70 and different characteristics of online content 111 .

Most authors, particularly those of the causal inference articles in our body of evidence, explicitly emphasized the national, cultural, temporal and political boundary conditions for interpreting and generalizing their results (see, for example, ref. 111 ). By contrast, especially in articles conducted on US samples, the national context and the results’ potential conditionality was often not highlighted. We strongly caution against a generalization of findings that are necessarily bound to a specific political setting (for example, the United States) to other contexts.

Sampling methods and risk of bias

To assess study quality and risk of bias, we additionally coded important methodological aspects of the studies, specifically, the sampling method, sample size and transparency indicators, such as competing interest, open data practices and pre-registrations. In Fig. 5 , we show an excerpt from that analysis. Different sampling methods naturally result in different sample sizes as shown in Fig. 5a,b . Furthermore, behavioural data are much more prevalent for studies that look at detrimental outcomes, such as polarization and echo chambers. Classic surveys with probability samples or quota samples, in contrast, are often used to examine beneficial outcome measures such as trust and participation (Fig. 5c,d ). Overall, however, no coherent pattern emerges in terms of the reported directions of associations. If anything, large probabilistic samples report relatively less beneficial associations for both types of outcomes (Fig. 5 ). Generally, different types of data have different advantages, such as probability and quota samples approximating more closely the ideal of representativeness, whereas the observation of actual behaviour on social media escaping the potential downsides of self-reporting. A potential blind spot in studies working with behavioural data from social media, inaccessible to both us and the original authors of the studies, is the selection of data provided by platforms. Therefore, it is tremendously important for researchers to get unrestricted access or, at least, transparent provision of random samples of data by platforms. The selection of users into the platforms, however, remains an open issue for behavioural data as it is often unclear who the active users are and why they are active online. We find that political outcome measures studied with behavioural data appear to show quite distinct results compared with those studied with large-scale survey data. Combining both data types would probably maximize the chances for reliable conclusions about the impact of digital media on democracy.

a , Sample size vs sampling methods for variables of trust, knowledge, participation, exposure and expression. Each dot represents one measurement, colour coded according to the direction of the reported association. b , Sample size vs sampling method for variables of hate, polarization, populims, network homophily and misinformation. c , More detailed breakdown for the same varibales as in a of sampling methods and their respective counts of reported associations and their direction. d , Breakdown of sampling methods and counts of associations for the same variables as in b .

We found relatively few null effects for some variables. This could be accurate, but it could also be driven by the file-drawer problem—the failure to publish null results. To examine the extent of a potential file-drawer problem, we contacted authors via large mailing lists but did not receive any unpublished work that fitted our study selection criteria. Regarding possible risk of bias, we found that only in 143 out of 354 measurements did authors clearly communicate that no conflict of interest was present (beyond the usual funding statement). However, we did not find a striking imbalance in the distribution of reported associations between those articles that did not explicitly state competing interest and those that did. Of the few associations for which conflicts of interest were stated, 4 pointed in beneficial, 3 in detrimental and 2 reported lack of directionality. In only 79 of 354 measurements did the researchers use open data practices. Considering articles that reported detrimental associations, we did not find a clear difference in the directions between those with and without open data. However, considering articles that reported beneficial outcomes, the numbers of positive findings in the studies without open data are relatively much larger than for the open science studies. Namely, 103 beneficial and 33 detrimental associations were reported in those without open data, while 19 beneficial versus 14 detrimental were reported in studies with open data practices. This observation might be due to the large number of survey-based studies about participation, which often do not follow open data practices. Even fewer of the studies in our sample were pre-registerd, namely, 13 of the 354, where 9 reported detrimental associations, only 3 reported beneficial associations and 1 found no direction of association. To shed light on other potential biases, we additionally examined temporal variations in the directions of reported associations and found, besides the general explosive growth of studies in this domain, a slight trend towards an increasing number of both detrimental directions and null effects over time (Supplementary Fig. 4 ). At the author level, there was no clear pattern in the associations reported by those authors who published the greatest number of articles in our sample; several authors variously reported detrimental and beneficial effects as well as null effects, with a few exceptions (Supplementary Fig. 5 ). Their co-authorship network in Supplementary Fig. 6 , split for the two types of outcomes measures, shows some communities of co-authors; however, no clear pattern of preferred direction of reported association can be spotted. Overall, we did not find evidence of a systematic bias in either direction driven by temporal trends or particular authors.

Regardless of whether they are authoritarian, illiberal, or democratic, governments around the world are concerned with how digital media affect governance and their citizenry’s political beliefs and behaviours. A flurry of recent interdisciplinary research, stimulated in part by new methodological possibilities and data sources, has shed light on this potential interplay.

Although classical survey methods are still predominant, novel ways of linking data types, for example linking URL tracking data or social media data with surveys, permit more complex empirical designs and analyses. Furthermore, digital trace data allow an expansion in sample size. The articles we reviewed included surveys with a few hundred, up to a few thousand participants, but also large-scale social media analyses that included behavioural traces of millions. Yet with computational social science still in its early days, the amount of evidence supporting and justifying causal conclusions is still limited. Causal effects of digital media on political variables are also hard to pin down empirically due to a plethora of complexities and context factors, as well as the highly dynamic technological developments that make predicting the future difficult. While emergent political phenomena are hard to simulate in the lab, the value of estimation and data collection strategies to draw causal inferences from real-life data is enormous. However, the long-established trade-off between internal and external validity still applies, which also highlights the value of high-quality descriptive work.

Taking into account both correlational and causal evidence, our review suggests that digital media use is clearly associated with variables such as trust, participation and polarization. They are critical for the functioning of any political system, in particular democracies. Extant research reports relatively few null effects. However, the trends on each factor mostly converge, both across research methods and across correlative and causal evidence.

Our results also highlight that digital media are a double-edged sword, with both beneficial and detrimental effects on democracy. What is considered beneficial or detrimental will, at least partly, hinge on the political system in question: intensifying populism and network homophily may benefit a populist regime or a populist politician but undermine a pluralistic democracy. For democratic countries, evidence clearly indicates that digital media increase political participation. Less clear but still suggestive are the findings that digital media have positive effects on political knowledge and exposure to diverse viewpoints in news. On the negative side, however, digital media use is associated with eroding the ‘glue that keeps democracies together’ 33 : trust in political institutions. The results indicating this danger converge across methods. Furthermore, our results also suggest that digital media use is associated with increases in hate, populism and polarization. Again, the findings converge across causal and correlational articles.

Alongside the need for more causal evidence, we found several research gaps, including the relationship between trust and digital media and the seeming contradiction between network homophily and diverse news exposure. Methods that link tracking data for measuring news exposure with behavioural data from social media (for example, sharing activities or the sentiment of commenting) are crucial to a better understanding of this apparent contradiction.

Limitations

The articles in our sample incorporate a plethora of methods and measures. As a result, it was necessary to classify variables and effects into broad categories. This is a trade-off we had to make in exchange for the breadth of our overview of the landscape of evidence across disciplines. For the same reason, we could not provide a quantitative comparison across the diverse sample of articles. We believe that digital media research would benefit from more unified measures (for example, for polarization), methods across disciplines to allow for better comparability in the future, a systematic comparison of different types of digital media (that is, Facebook and Twitter are neither of one kind nor, in all likelihood, are their effects) and extensions of outcome measurements beyond certain types of digital media. This follows other recent calls for commensurate measures of political and affective polarization 156 . The breadth of our review and the large number of political outcome measures in particular, made it necessary to be quite restrictive on other ends (see Fig. 6 for our exclusion process and Supplementary Table 1 for the detailed criteria). We explicitly decided to prioritize the selection of causal evidence (see Fig. 7 for an overview of the causal inference techniques that we considered) and other large-sample, quantitative, published evidence. However, following this pre-registered search strategy led to the selection of unequal numbers of studies for different outcome variables. For example, our search query selected considerably more studies examining political participation than political expression or trust, while at the same time, it did not include all studies that are included in other systematic reviews 45 due to stricter exclusion criteria.

a , Keywords included in our search query, run on Web of Science and Scopus, with logical connectors. Focus was on causal inference methods (method column), but also inclusion of descriptive quantitative evidence, relationships between digital media (cause column) and political outcomes (direct effect box) or content features (indirect effect box). b , Flowchart representing the stepwise exclusion process, starting with title-based exclusion, followed by abstract-based exclusion. c , Example illustration of outcome variable extraction from the abstracts. d , Breakdown of the most frequently reported political variables into top 10 categories. Numbers in brackets are counts of measurements in the set.

Fundamental principles of causal inference techniques and statistical strategies used in our sample of causal evidence (excluding field experiments).

The interpretation of our results was in several cases hampered by a lack of appropriate baseline measures. There is no clear measure of what constitutes a reasonable benchmark of desirable political behaviour in a healthy democracy. In addition, there were no means of quantification of some of these behaviours in the past, outside of digital media. This problem is particularly pronounced for factors such as exposure to diverse news, social network homophily, misinformation and hate speech. Measuring these phenomena at scale is possible through digital media (for example, by analysing social network structure); much less is known about their prevalence and dynamics in offline settings. Many articles therefore lacked a baseline. For instance, it is neither clear what level of homophily in social networks is desirable or undesirable in a democratic society, nor is it clear how to interpret the results of certain studies on polarization 69 , 130 , whose findings depend on whether one assumes that social media have increased or decreased exposure to opposing views relative to some offline benchmark. For example, if exposure to opposing views is increased on social media, the conclusion of one study 130 would be that it reduces polarization, but if exposure is decreased, one would come to the opposite conclusion. Notably, in this study, counter-attitudinal exposure was found to be down-ranked by Facebook’s news feed—hence supporting a process that fosters polarization instead of counteracting it. Furthermore, results about populism might be skewed: descriptive evidence on the relative activity and popularity of right-wing populist parties in Europe suggests their over-representation, as in the case of Germany’s AfD, on social media, relative to established democratic parties (see, for example, ref. 132 ). Therefore, it is difficult to interpret even causal effects of digital media use on populist support in isolation from the relative preponderance of right-wing content online.

Our results provide grounds for concern. Alongside the positive effects of digital media for democracy, there is clear evidence of serious threats to democracy. Considering the importance of these corrosive and potentially difficult-to-reverse effects for democracy, a better understanding of the diverging effects of digital media in different political contexts (for example, authoritarian vs democratic) is urgently needed. To this end, methodological innovation is required. This includes, for instance, more research using causal inference methodologies, as well as research that examines digital media use across multiple and interdependent measures of political behaviour. More research and better study designs will, however, also depend on access to data collected by the platforms. This access has been restricted or foreclosed. Yet without independent research that has unhampered access to all relevant data, the effects of digital media can hardly be understood in time. This is even more concerning because digital media can implement architectural changes that, even if seemingly small, can scale up to widespread behavioural effects. Regulation may be required to facilitate this access 157 . Most importantly, we suggest that the bulk of empirical findings summarized here can be attributed to the current status quo of an information ecosystem produced and curated by large, commercial platforms. They have succeeded in attracting a vast global audience of users. The sheer size of their audience as well as their power over what content and how content gets the most attention has led, in the words of the philosopher Jürgen Habermas, to a new structural transformation of the public sphere 16 . In this new public sphere, everybody can be a potential author spontaneously producing content, both right-wing radical networks as well as the courageous Belarusian women standing up for human rights and against a repressive regime. One need not share Habermas’ conception of ‘deliberate democracy’ to see that current platforms fail to produce an information ecosystem that empowers citizens to make political choices that are as rationally motivated as possible. Our results show how this ecosystem plays out to have important consequences for political behaviours and attitudes. They further underscore that finding out which aspects of this relationship are detrimental to democracy and how they can be contained while actively preserving and fostering the emancipatory potential of digital media is, perhaps, one of the most important global tasks of the present. Our analysis hopes to contribute to the empirical basis of this endeavour.

This systematic review follows the MOOSE Guidelines for Meta-Analyses and Systematic Reviews of Observational Studies 158 . The detailed protocol of the review process was pre-registered on the Open Science Framework (OSF) at https://osf.io/7ry4a/ . The repository also contains the completed MOOSE checklist showing where each guideline is addressed in the text.

Figure 6 summarizes the search query that we used on two established academic databases, Scopus and Web of Science (both highly recommended search tools), the resulting number of articles from the query and the subsequent exclusion steps, leading to the final sample size of N  = 496 articles under consideration 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 , 203 , 204 , 205 , 206 , 207 , 208 , 209 , 210 , 211 , 212 , 213 , 214 , 215 , 216 , 217 , 218 , 219 , 220 , 221 , 222 , 223 , 224 , 225 , 226 , 227 , 228 , 229 , 230 , 231 , 232 , 233 , 234 , 235 , 236 , 237 , 238 , 239 , 240 , 241 , 242 , 243 , 244 , 245 , 246 , 247 , 248 , 249 , 250 , 251 , 252 , 253 , 254 , 255 , 256 , 257 , 258 , 259 , 260 , 261 , 262 , 263 , 264 , 265 , 266 , 267 , 268 , 269 , 270 , 271 , 272 , 273 , 274 , 275 , 276 , 277 , 278 , 279 , 280 , 281 , 282 , 283 , 284 , 285 , 286 , 287 , 288 , 289 , 290 , 291 , 292 , 293 , 294 , 295 , 296 , 297 , 298 , 299 , 300 , 301 , 302 , 303 , 304 , 305 , 306 , 307 , 308 , 309 , 310 , 311 , 312 , 313 , 314 , 315 , 316 , 317 , 318 , 319 , 320 , 321 , 322 , 323 , 324 , 325 , 326 , 327 , 328 , 329 , 330 , 331 , 332 , 333 , 334 , 335 , 336 , 337 , 338 , 339 , 340 , 341 , 342 , 343 , 344 , 345 , 346 , 347 , 348 , 349 , 350 , 351 , 352 , 353 , 354 , 355 , 356 , 357 , 358 , 359 , 360 , 361 , 362 , 363 , 364 , 365 , 366 , 367 , 368 , 369 , 370 , 371 , 372 , 373 , 374 , 375 , 376 , 377 , 378 , 379 , 380 , 381 , 382 , 383 , 384 , 385 , 386 , 387 , 388 , 389 , 390 , 391 , 392 , 393 , 394 , 395 , 396 , 397 , 398 , 399 , 400 , 401 , 402 , 403 , 404 , 405 , 406 , 407 , 408 , 409 , 410 , 411 , 412 , 413 , 414 , 415 , 416 , 417 , 418 , 419 , 420 , 421 , 422 , 423 , 424 , 425 , 426 , 427 , 428 , 429 , 430 , 431 , 432 , 433 , 434 , 435 , 436 , 437 , 438 , 439 , 440 , 441 , 442 , 443 , 444 , 445 , 446 , 447 , 448 , 449 , 450 , 451 , 452 , 453 , 454 , 455 , 456 , 457 , 458 , 459 , 460 , 461 , 462 , 463 , 464 , 465 , 466 , 467 , 468 , 469 , 470 , 471 , 472 , 473 , 474 , 475 , 476 , 477 , 478 , 479 , 480 , 481 , 482 , 483 , 484 , 485 , 486 , 487 , 488 , 489 , 490 , 491 , 492 , 493 , 494 , 495 , 496 , 497 , 498 , 499 , 500 , 501 , 502 , 503 , 504 , 505 , 506 , 507 , 508 , 509 , 510 , 511 , 512 , 513 , 514 , 515 , 516 , 517 , 518 , 519 , 520 , 521 , 522 , 523 , 524 , 525 , 526 , 527 , 528 , 529 , 530 , 531 , 532 , 533 , 534 , 535 , 536 , 537 , 538 , 539 , 540 , 541 , 542 , 543 , 544 , 545 , 546 , 547 , 548 , 549 , 550 , 551 , 552 , 553 , 554 , 555 , 556 , 557 , 558 , 559 , 560 , 561 , 562 , 563 , 564 , 565 , 566 , 567 , 568 , 569 , 570 , 571 , 572 , 573 , 574 , 575 .

Study selection criteria

We included only original, empirical work. Conceptual or theoretical work, simulation studies and evidence synthesizing studies were excluded. Articles had to be published in academic journals in English. Unpublished studies for which only the abstract or a preprinted version was available were excluded from the review. We excluded small- N laboratory experiments and small- N student surveys ( N  < 100) from our body of original work due to validity concerns. Although correlational evidence cannot establish a causal direction, we focused on articles that examined effects of digital media on democracy but not the opposite. We therefore excluded, for example, articles that examined ways to digitize democratic procedures. To be included, articles had to include at least two distinct variables, a digital media variable and a political outcome. Articles measuring a single variable were only included if this variable was a feature of digital media (for example, hate speech prevalence, homophily in online social networks, prevalence of misinformation in digital media).

Search strategy, study selection, coding and data extraction

Articles eligible for our study had to be published before 15 September 2021. We sourced our review database from Scopus and Web of Science, as suggested by ref. 159 . The search query (Fig. 6 ) was constructed in consultation with professional librarians and was designed to be as broad as possible to pick up any articles containing original empirical evidence of direct or indirect effects of digital media on democracy (including correlational evidence). We further consulted recent, existing review articles in the field 32 , 39 , 40 to check for important articles that did not appear in the review body. Articles that were included manually are referenced separately in the flowchart (Fig. 6 ). In addition, we contacted authors via large mailing lists of researchers working on computational social science and misinformation but did not receive any unpublished work that fitted our study selection criteria. The query retrieved N  = 3,509 articles. Of these, 1,349 were retained after screening the titles for irrelevant topics. This first coding round, whether an article, based on the title, fits the review frame or not, was split between two coders. Coders could flag articles that are subject to discussion to let the other coder double check the decision. In this round, only clearly not fitting articles were excluded from the sample. A list of exclusion criteria can be found in SuppIementary Information .

The next coding round, whether an article, based on the abstract, fits the review frame, was conducted in parallel by two coders. The inter-coder reliability, after this round of article selection, was Krippendorff’s alpha of 0.66 (87% agreement). After calculating this value, disagreement between coders was solved through discussion. At this stage, we excluded all studies that were not original empirical work, such as other reviews or conceptual articles, simulation studies and purely methodological articles (for example, hate speech or misinformation detection approaches). This coding round was followed by a more in-depth coding round. Here we refined our exclusion decisions; for example, we excluded studies that examined the digitization of government, preprints, small-scale lab experiments, small-scale convenience or student samples and studies that only included one variable (for example, description of online forums) (see Supplementary Table 1 for a detailed list of criteria). A full-text screen was performed in cases where the relevant information could not be retrieved from the abstract and for all articles implying causal evidence.

After both rounds of abstract screening, 474 articles remained in our sample. After cross-checking the results of our literature search against the references from existing reviews, we found and included further N  = 22 articles that met our thematic criteria but were not identified by our search string. Ultimately, a total of 496 articles were selected into the final review sample. Figure 6b summarizes the selection procedure.

The following information was extracted from each article using a standardized data extraction form: variable groups under research (digital media, features of media and/or political outcome variables), the concrete digital media under research, the explicit political outcome variable, the methods used, the country of origin, causal claims, possible effect heterogeneity (moderation) as well as various potential sources of bias. To assess various quality criteria of the studies, the coders had to visit the full text of the articles (for example, to find the declaration of competing interests, pre-registration or data availability statements, or to consider the methods section). Therefore, and facing the large number of articles under consideration, blinding could not be established during this procedure.

When conducting a systematic review with a broad scope, categories of the variables cannot be exhaustively defined before coding. Therefore, variable categories, especially for the digital media variables and the political outcome variables, were chosen inductively. In the first extraction step, coders stuck closely to the phrasing of the authors of the respective study. To reduce redundancy and refine the clustering of the variables, we iteratively generated frequency tables and manually sorted single variables to the best-fitting categories until a small number of clearly distinct categories was selected. After the categories were defined, both coders re-coded 10% of the sample to calculate inter-coder reliabilities for all key variables. We provide a table of inter-coder reliabilities (percentage agreements and Krippendorff’s alphas) (Supplementary Table 2 ).

Data synthesis and analysis

Due to considerable heterogeneity in methods in the articles—including self-report surveys through network analysis of social media data, URL tracking data and field experiments—no calculation of meta-analytic effect sizes was possible. The final table of selected articles with coded variables will be published alongside this article as a major result of this review project. The effect directions of 10 important political outcome variables (4 consistent with liberal democracy, 4 opposing democratic values) are summarized in Fig. 2 . For articles dealing with these political variables, we also assessed the country in which the study was conducted (Fig. 4 ), as well as explicit sources of effect heterogeneity such as demographic characteristics of study participants or characteristics of the digital media platform.

For the overview analysis, which includes both correlational and causal evidence, we mainly restricted ourselves to the evaluation effects reported in the abstracts. Articles making explicit causal claims and/or using causal inference methods (Fig. 7 ) were examined in-depth and summarized as simplified path diagrams with information on mediators, moderators, country of origin and method used (Fig. 3 ).

Deviations from the protocol

The volume of papers our query returned prevented an in-depth analysis of confounding variables. Instead, our assessment of quality relied on the sampling strategy and sample size, the method used, sources of heterogeneity and transparency criteria, such as open data practices and pre-registration. Furthermore, we were able to construct the co-author network by matching the author’s names, but were unable to produce a meaningful co-citation network due to the incompleteness and ambiguity of references in the export format that we used.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The dataset including all originally collected studies with decision stages ( N  = 3,531, ‘full_data.xlsx’), the table including all papers within our review sample ( N  = 496, ‘data_review.xlsx’) and the table including all effects reported within papers dealing with the top ten outcome measures ( N  = 354, ‘data_effects.xlsx’) are available at https://osf.io/7ry4a/ .

Code availability

R scripts for all analyses and figures are available at https://osf.io/7ry4a/ .

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Acknowledgements

We thank S. Munzert for providing his perspective on causal inference and issues specific to political science, D. Ain for editing the manuscript and F. Stock for help in the literature comparison. P.L.-S., S.L. and R.H. acknowledge financial support from the Volkswagen Foundation (grant ‘Reclaiming individual autonomy and democratic discourse online: How to rebalance human and algorithmic decision-making’). S.L. acknowledges support from the Humboldt Foundation through a research award and partial support by an ERC Advanced Grant (PRODEMINFO) during completion of this paper. L.O. acknowledges financial support by the German National Academic Foundation in the form of a PhD scholarship. The authors received no specific funding for this work. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Lorenz-Spreen, P., Oswald, L., Lewandowsky, S. et al. A systematic review of worldwide causal and correlational evidence on digital media and democracy. Nat Hum Behav 7 , 74–101 (2023). https://doi.org/10.1038/s41562-022-01460-1

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Social Media Effects: Hijacking Democracy and Civility in Civic Engagement

Bolane olaniran.

6 Texas Tech University, Lubbock, TX USA

Indi Williams

7 Arizona State University, Tempe, AZ USA

Perceived as an equalizing force for disenfranchised individuals without a voice, the importance of social networks as agents of change cannot be ignored. However, in some societies, social networks have evolved into a platform for fake news and propaganda, empowering disruptive voices, ideologies, and messages. Social networks such as Twitter, Facebook, and Google hold the potential to alter civic engagement, thus essentially hijacking democracy, by influencing individuals toward a particular way of thinking.

Computer-mediated communication , a novel and emerging area just a few decades ago, has evolved from an academic collaboration tool to what is commonly referred to as new and social media. New and social media have been touted as an equalizer for disenfranchised individuals to participate or contribute in civic engagement and to foster democratic ideals. However, the current state of social media and networking sites leave individuals to conclude that these media platforms may be holding democracy hostage instead of leading to the free and equal democratic ideals they were believed to support. Consequently, this chapter emphasizes that it is imperative to figure out a way to maintain sensible dialogues that promote democratic principles.

New and social media are hailed as vehicles for providing a voice to the voiceless. They are also viewed as a way to overcome state-controlled media and content (Bartlett, Birdwell, & Littler, 2011 ) especially in the developing world (Bartlett, Birdwell, & Littler, 2011 ). However, social media platforms are also increasingly being used as a means for empowering disruptive voices, messages, or ideologies (e.g., xenophobia, neo-Nazism, anti-immigration/ globalization, cultural homogeneity, etc.) (Cook, Waugh, Abdipanah, Hashemi, & Rahman, 2014 ; Gleason, 2013 ). The ability of a person or group to overstate an agenda and dominate the conversation is easily accomplished on social media such as Twitter. This is because social media do not subscribe to the same established journalistic rules of vetting and reporting news. Furthermore, the size of a group or an organization pushing a particular message no longer matters.

This chapter explores how social media have become a platform for fake news and propaganda to influence certain audiences toward a particular way of thinking. Consequently, social media outlets and people who consume information through them are putting pressure on the idea of democracy such that democratic societies as we know them may cease to exist. Along this line, this chapter explores how Twitter and Facebook in particular are used in a manner that creates chaos within regions and have arguably become an authoritative vehicle for persuasion (Cook et al., 2014 ; Waters & Williams, 2011 ). The capacity to morph or create multiplier effects suggest that social media messages, such as tweets and retweets of a few minority influencers, can become something considerably larger in terms of support of a person or a particular policy (Cook et al., 2014 ; Wilson, 2011 ). Therefore, this chapter incorporates specific examples and analogies from events such as the Brexit vote and US elections, along with subsequent tweets by the president of the United States.

New and Social Media

New and Social media provide information for individuals in certain networks while they also create multiplier effects as those same individuals attempt to reach others in their networks. Multiplier effects such as these that occur through social media can go on in perpetuity. For instance, it was noted that in 2010 that individuals between 8 and 18 years of age were exposed to a daily average of 10.45 hours of various media technology (see Dahl & Newkirk, 2010 ). However, recently people have been exposed and engaged in what has been termed as a mass-self communication (Castells, 2013 ) that is embedded within ubiquitous computing (Moffitt, 2016 ). Ubiquitous computing is also known as the third wave of computers, in which hand-held devices with Internet wireless technology are widespread and highly accessible. In essence, this dynamic constitutes social media that readily put information and messages in the hands of individuals at a speed never seen before. Therefore, the result is an evolution of electronic communication technology (Castells, 2013 ). This evolution is stimulating new patterns of production, reception, content, and circulation, allowing for new forms of engagement through participation, production, and consumption. Consequently, communication is no longer confined by geographical boundaries, but rather globalized to the extent that it is linked to the “ideology of worldwide communication” (Mattelart, 2002 , p. 591). In other words, social media enable power where an online community or the virtual world has become a dialectical space. It is within this space that people can initiate or perform roles as producers of content, broadcasters, audiences, and political actors (Castells, 2013 ).

Social Media and Political Discourse

The political landscape has been transformed by new and social media. This transformation has resulted in an increased rise of populism around the world. Subsequently, the active role of the audience as made possible by social media has become a great opportunity for populist actors to spread their political messages or agendas (Moffitt, 2016 ). The proliferation of populism through media is not new. Historically in Europe, the populist radical-right parties (PRRPs) and actors have been using media (e.g., TV, radio, print press) as platforms for their messages since World War II (Mudde, 2013 ). However, new and social media reache a larger audience with political content via Facebook, Twitter, YouTube, or Weibo (Moffitt, 2016 ). This audience can now be reached at greater speeds and within a short time span (DeLuca, Lawson, & Sun, 2013 ).

The role of new and social media is central to the populism movement because it represents political strategies in novel and exciting forms (Mudde, 2007 , 2013 ; Moffitt, 2016 ). In this vein, social networks are better suited as a method of creating social webs designed to facilitate the diffusion of desired behavior among groups of people (i.e., Centola & Christakis, 2014 ). However, the nature of social media in a political discourse must be conceptualized within the context of democracy theory. For the most part,  democratic theory subscribes to the idea of human involvement in non-activist decision making, otherwise referred to as participatory democracy (Moote, McClaran, & Chickering, 1997 ). At the core of participatory democracy theory is the role of the public or citizens in rational evaluations of the pros and cons of an issue. This is especially the case when individuals are participating in decision making or offering rewards (Kweit & Kweit, 1987 ; Moote et al., 1997 ). However, with the introduction of social media, affected people are encouraged to voice their opinions even though they do not necessarily engage in the democratic process. More specifically, the coherent discussion of ideas has been substituted with the spread of fragmented ideas, resulting in the spread of populism (Wirth et al., 2016 ). To this end, social media in political discourse are rife with a pathological form of democracy (Betz, 1994 ; Engesser, Ernst, Esser, & Büchel, 2017 ). Similarly, although the spread of populism extends beyond Westernized societies, Mudde ( 2007 ) concurs that populism has become mainstream in Western democratic politics.

Social Media Impact on Voting Turnout

Bond et al. ( 2012 ) found that online political mobilization messages distributed via individual self-expressions and shared through personal social networks (i.e., Facebook or Twitter) lead to self-guided information seeking and, perhaps, self-serving behavior. Consequently, these messages subsequently impact voting turnout behavior. Indeed, the study indicates the powerful effect of online political mobilization. Furthermore, the authors conducted a randomized controlled trial with all users who accessed the Facebook website on 2 November 2010, the day of the US congressional elections. Users were then randomly assigned to a “social message” group ( N  = 60,055,176), an “informational message” group ( N =  611,044), or a control group ( N  = 613,096). The findings suggest that when political mobilizing messages are disseminated by close friends in a given personal social network, the influence is four times more on the total number of validated voters mobilized compared to the informational message group and control group. In other words, social networks have been and continue to be used to impact individuals’ voting turnout behavior (i.e., Kramer, Guillory, & Hancock, 2014 ). Hence, sharing messages in social networks impacts an individual’s emotions, which ultimately results in actual real-world actions. This finding serves to rule out any naïve understanding of social networks as a mere way of contacting “old friends” and family members or in positioning commercial brands.

Populism the Symbolic Frontier

Although there exists haphazard scholarly analysis of populism as an ideology, strategy, discourse, or political logic, Moffitt ( 2016 ) asserts that the best way to conceptualize it is as a political strategy. This strategy entails “the repertoires of embodied, symbolically mediated performance made to audiences that are used to create and navigate the fields of power that comprise the political, stretching from the domain of government to everyday life” (Moffitt, 2016 , p. 38). Furthermore, within this dynamic, societies are politically polarized in two homogeneous and antagonistic groups: “the pure people” versus “the corrupt elite”, “Us” versus “They”, or “citizens” versus “immigrants”. The official political performance reflects people’s general will to forcefully reflect their sovereignty. Consequently, the way in which these groups are formed is through the unsatisfied demand as the minimal unit of political social analysis. This unsatisfied demand, along with other unsatisfied needs, becomes a springboard for people to identify a common antagonist/enemy believed to be the perpetrator even if this entails the use of fake news.

Therefore, the more people can dissociate themselves from the technocratic style of “politics as usual”, the better their appeal (Disch, 2012 ; Saward, 2010 ; Severs, 2010 ). For example, President Donald Trump said during his 2016 presidential campaign that he likes poor and uneducated Americans more than the rich. Subsequently, this situation evinces populist leaders’ performance based on pretending to be “outsiders” in mainstream politics to give perceived distance between their actual experiences as the “elite”. Therefore, populism creates a symbolic frontier among social groups in a way that hegemony is reinvented as a government of the people’s will (Wirth et al., 2016 ). One way this occurs is through the acceptance of a leader who fosters anti-immigrant discourse in the EU and the US, two nation-states where immigrants are treated as outsiders.

Polarized Political Groups Influencing Human Behavior

The use of social media platforms allows people to share messages with a larger audience in a way that was not previously possible. All this sharing can now be accomplished without running the risk of censorship, a common barrier of traditional media outlets. On social media there are active communities (e.g., right wing, racist, neo-Nazi) that seek to disseminate hate messages to their members and distribute propaganda to recruit new membership. These groups rely on platforms such as Twitter, Facebook and YouTube to communicate (O’Callaghan et al., 2013 ). Consequently, messages sent via social media will continue to spread through followers to others. Reciprocation of messages occurs in the same manner.

Perhaps a significant contribution of social media to any ideological or political movement, such as populism, lies in the fact that it helps to influence users’ behavior. An attempt to influence behavior must not only focus on the informational effect, but also on the effect the message will have on the recipients. Additionally, it must increase the likelihood of the various behaviors the message will spur as it transmits from person to person through the social network. This variation is based upon online mobilization as messages spread through strong-tie networks existing offline and in online arenas (Bond et al., 2012 ).

Some research has shown that the organization of community groups online is decentralized, while other research has found that some groups exhibit a more centralized disposition (Chau & Xu, 2007 ; O’Callaghan et al., 2013 ). Nevertheless their construction, the purpose of using new media to further any ideology is to mobilize groups. This includes, but is not limited to, furthing the populist movment. This mobilization was found to be the case in more extremist groups investigated in a conservative movement in the US (Blee & Creasap, 2010 ; Bond et al., 2012 ). Additionally, Bond et al. ( 2012 ) reported that online messages influenced political self-expression and information seeking, along with individual voting behavior. Moreover, online messages influenced not only those who received the messages, but also their friends and friends of friends (Bond et al., 2012 ). This was especially true when there was a strong tie or close friend relationship between individuals.

The Impacts of Social Media in Political Elections

The story of the last two US presidential campaigns focuses on the use of social media. However, each candidate used social media for different reasons and in order to accomplish different goals. The 2008 election focused on disseminating campaign-relevant information based on facts, while the 2016 election focused on propaganda through the deployment of fake news and bots. The research indicated that the election of President Obama brought about an increase in the surge of the white nationalist movement. Specifically, the study showed that the day after Obama was elected president occurred the biggest single increase in membership of Stormfront (a White nationalist organization) and that Trump rode the wave to become president in 2016 (Hinck, 2018 ; Stephens-Davidowitz & Pinker, 2017 ). Using social media as his persuasive tool, Trump’s campaign imbued anger and hyper- partisanship by advocating policies or messages that called for isolation from the world and the closing of the border to establish an immigration policy.

According to Persily ( 2017 ), social media were used in a way to upset established paradigms on how to run and win elections to the extent that President Trump’s campaign broke established norms of politics. However, President Trump and the 2016 election is not the only occurance of populist nationalism that appears to thrive on social media. Other examples include the rise of the Five Star Movement in Italy, the pirate party in Iceland, and the keyboard army of President Duterte in the Philippines. Furthermore, in Europe the successful Brexit referendum revealed that supporters were seven times more active than their opponents on Twitter and five times more active on Instagram (Persily, 2017 ).

Fanaticism and Viral Nature of Social Media

It is important to understand what make social media so powerful as a communication tool. The legacy of traditional media as gatekeepers or campaign mediators is declining in terms of influence and power, with no alternative institutions to fill the void. More importantly, President Trump taped into this void by excessively using social media and Twitter. It was noted that from August 2015 to election day there were more than a billion tweets regarding the presidential election ( Twitter.com , 2016 ; Persily, 2017 ). Furthermore, Trump’s followers on the platform outnumbered Clinton’s followers by 33% (CBSNews, 2016 ). Subsequently, every tweet from Trump or his allies was further retweeted by his loyal followers and supporters. Particularly, it was found that in mid-2016, Trump’s tweets were retweeted three times as much as Clinton’s, while Trump’s Facebook post were re-shared five times more ( Journalism.org , 2016 ; Persily, 2017 ). Persily ( 2017 ) also discovered that despite much lower advertising budgets or spending overall, the Trump campaign spent more on Facebook than the Clinton campaign.

Perhaps the viral nature of information on social media gives it power. This may be because messages (e.g., political) in social networks influence users’ emotions, making social media messages effective tools of persuasion (Kramer et al., 2014 ). The ability to deliver both real and junk news (i.e., propaganda, misinformation) makes the media platform potent. Malicious activities such as harassment, hate speech, and spamming are just a few of the negative ways social media are being used (Howard, Bolsover, Kollanyi, Bradshaw, & Neudert, 2017 ). Bots on social media platforms can quickly send messages and replicate themselves in a way where the messages appear as if sent by a human being. Social media bots are automated accounts that are set up to act as if an actual person is using them. Bots are often used for propagating propaganda from both within and outside the country. Moreover, the notion of sock puppetry denotes that large followings via social media platforms can be easily gained for an insignificant price.

Therefore, social media provide dangerous ways of spreading junk news within social  networks comprised of friends and family. Prior research found that social media favor sensationalist content, regardless of whether the message was fact-checked or not (McCoy, 2016 ; Vicario et al., 2016 ). Notwithstanding, when misinformation is combined with automation such as bots, then social media become a tool for computational propaganda (Howard et al., 2017 ; Kümpel, Karnowski, & Keyling, 2015 ). Cambridge Analytica (part of Trump’s social media digital strategy) claimed that it targeted 13.5 million voters in 16 battleground states to discover hidden Trump supporters that polls had ignored. Also, Cambridge Analytica targeted Clinton supporters (e.g. white liberals, young women, and African Americans) with messages aimed to reduce turnout among those groups (Persily, 2017 ).

Political Polarization and Lack of Censorship

Social media offer a direct connection to people and thus allows for the spread of fragmented ideas such as populism to circumvent journalistic gatekeepers. In this way populists can present uncontested or unvetted ideas directly to their audience and articulate their ideology (Engesser et al., 2017 ). Hence, the rise of new media and political polarization creates a binary political strategy to increase political participation and voting turnout among individuals who see themselves as victims, or powerless, in the democratic process. Notwithstanding, the lack of control and censorship in new and social media has become a niche for extremist groups such as ISIS (Islamic State of Iraq and Syria) or neo-Nazis to spread their ideology. It is within this landscape that traditional media are forced to line up with polarized content in new media in order to keep their audience, while users are caught in the middle or forced to take a side. This dilemma, however, is the antithesis of the tenets of participative democracy (Moote et al., 1997 ). More importantly, traditional media are reinventing what is defined as news to the extent that they are actively mining social media for what they believe their audience wants to view.

The fact remains that social media platforms have become fertile ground for fake news and propaganda as evidenced in the 2016 US presidential election. BuzzFeed found that false election stories from hoax sites and hyper-partisan blogs generated more engagement than content from real news sites during the last three months of the election and post-election. Users shared false stories such as that Pope Francis endorsed Donald Trump and/or that Hillary Clinton sold weapons to ISIS. These stories and others were shared (e.g. retweeted) hundreds of thousands of times. More importantly, another report found that users were not interested in any news that disagreed or deviated from their accepted premises (PBS Newshour, 2016a ). Subsequently, people continued to actively seek and present false information as long as it supported their respective viewpoints.

Furthermore, any group can lend its Twitter support to a particular cause or person such that the control of an ideology or principle can gain an allegiance for a price (Ashton, 2013 ; Cook et al., 2014 ). Similarly, social media are increasingly being used by individuals who want to profit based on the number of clicks. In order to do this, they deliberately spread false and fake news to enrich themselves. Persily ( 2017 ) investigated the profit motive of social media users residing both inside and outside (i.e., Macedonia) the US. These users reported that publishing pro-Trump and anti-Clinton stories on about 140 websites dealing with US politics could earn them a fortune. One Trump supporter commented that he would have been willing to promote Ms. Clinton and smear Trump if the tactic was profitable. However, he discovered that similar Trump supporters were more fanatical and/or emotionally connected to their candidate than Clinton’s supporters (McCoy, 2016 ). Furthermore, he stated that  Trump supporters were more likely to believe anything when compared to Clinton’s supporters. This is because demographically Trump supporters are less educated, open to deep-seated beliefs, and willing to accept conspiracy theories as truth (Persily, 2017 ; Peters, 2017 ; Sides, Tesler, & Vavreck, 2017 ).

Social Media, Politics, and Propaganda

Twitter, for example, has increasingly been used in political elections of nation-states and in the spread of ideologies such as displayed in the Brexit movement and the 2016 US presidential election (PBS Newshour, 2016b ). Additionally, web-based botnets represent a significant number of Twitter traffic (Boshmaf, Muslukhov, Beznosov, & Ripeanu, 2011 ; Cook et al., 2014 ). To this end, propaganda and misinformation appear to be the norm in social media networks such as Twitter and Facebook. Social media bots (i.e., botnets, bots) are designed to manipulate the passage, transfer, and volume of the social narrative, which makes them ideal for the spread of homogeneity, as opposed to diversity, within their message. This inherent functionality is why bots are frequently used to spread beliefs (e.g., populism) and computational propaganda. Message distribution via botnets is popular due to the fanaticism of select users who demonstrate an insatiable desire to consume and redistribute information despite the source. Many of these messages carry divisive narratives that tend to transform civic engagement into dichotomies, pitting one group of people against another without allowing for consensus or compromise. Furthermore, fake news websites and bots attract traffic and drive engagement. Collectively, they aim to influence conversations and demobilize opposition through false support (Howard et al., 2017 ).

The size of a group or an organization does not necessarily have to reflect the level of influence delivered through social media. Twitter has been used in a manner that can create both stability and chaos within regions. Twitter has also arguably become an authoritative vehicle for persuasion (Cook et al., 2014 ; Waters & Williams, 2011 ). For instance, the Pizzagate conspiracy theory, where Michael Flynn Jr. (the son of fired National Security Agency director Michael Flynn) tweeted a false story about Hilary Clinton and her campaign manager being involved in a child sex ring. Unfortunately, the tweet led to a man who believed the theory entering the pizza parlor mentioned in the tweet with a rifle and firing shots before being arrested (Persily, 2017 ). Moreover, Twitter can greatly influence two-party dominated elections such as those in the US, UK and Australia, where prominence is sited on the support for one political leader over another (Cook et al., 2014 ). For example, it has been reported that tweets for Hilary Clinton and Donald Trump by party loyalists using sock puppetry or bots at one point stood at 20% and 33%, respectively (PBS Newshour, 2016b ). Similar results were found in the 2013 Australian federal elections, where large numbers of fake Twitter followers were found for both the incumbent prime minister and the leader of the opposition (Butt & Hounslow, 2013 ; Cook et al., 2014 ).

Prior to the Brexit vote, some of the messages tweeted to sway votes included “We are British not Europeans”, “Immigrants are terrorists”, and “Immigrants have taken away our jobs”. Additionally, Donald Trump’s 2016 campaign slogan “Make America Great Again”, was coupled with Twitter messages referring to Mexicans as rapists, Muslims as Islamic terrorists, and the North American Free Trade Agreement (NAFTA) agreement as the worst trade policy ever. In all cases, the opposition always touted the supporters of such ideologies as a basket of deplorables. Unfortunately however, these extreme viewpoints are now the norm, reality in a post-truth world. New scientific evidence attributes this not to the fact that politicians are more crooked than before, but rather that facts are futile. In other words, it is not that particular negative beliefs are more popular than positive beliefs, but that followers at times become more aggressive at distributing their views over other groups. More importantly, misinformation through social media, once limited to select viewers, has become shareable to all (Peters, 2017 ). For example, ideological extremism, misinformation, and the intention to persuade readers to respect or hate a candidate/policy based on emotional appeals through social media were reported in Michigan during periods leading to the 2016 USA presidential election. This fake news outperformed professional real news, substantiating the claim that truth is relative and based upon a particular political stance and/or belief system (Howard et al., 2017 ).

Implications

In social media, trending, tweeting, and retweeting are key metrics, even though the metrics can be manipulated, bought, or faked to create the impression that a particular issue represents the opinions of the majority. The reality though is that these messages are designed to appear as truth. Thus, political agendas such as populist ideologies, among others, can be manipulated as original or authentic when in fact this is not the case. Quite often, crazy ideas, lies, and conspiracy theories spread more rapidly than facts through social media. Subsequently, by the time information is fact-checked, the damage is already done and remains irreversible (Howard et al., 2017 ; McCoy, 2016 ; Persily, 2017 ; Peters, 2017 ). Therefore, it becomes difficult to engage in a democratic process where everyone can deliberate and consider all points of view. Moreover, the implication for the socio-cultural perspective may be greater especially when hatred, ethnocentrism, and separatism philosophies become the norm, as both the Brexit and the 2016 US elections indicate. The role that social media plays in hijacking democracy is clear in these elections, as the winners in both cases were the minority. For example, President Trump was elected based on the Electoral College vote, when in fact he lost the popular vote by 3 million votes.

User Anonymity and Authenticity

With social media, authenticity and trustworthiness of information, along with a sender’s identity, are hard to discern (Engesser et al., 2017 ). Furthermore, the anonymity facilitated in social media contributes to phony online personas that can be created by users or even botnets. According to PBS NewsHour ( 2016b ), bots can be purchased very cheaply (Ashton, 2013 ), and as a result, they become a critical tool to influence political movement and manipulate metrics. Furthermore, it is hard to verify messages that bots distribute versus messages from a real person. Also, bots contribute to fake tweets, since they are soley designed to sway opinions (i.e., slacktivism) (Cook et al., 2014 ; PBS NewsHour, 2016b ). The danger, however, is that given a significant number of demographics (i.e., millennials) get their news through social media platforms and often from friends, family members, and acquaintances (e.g., social media influencers), they are less likely to do due diligence in questioning the authenticity of messages via tweets, retweets or Facebook postings. The sheer number of followers of a particular message is likely to convince individuals of the need to subscribe to similar beliefs and ideologies being promulgated by a sender even when such ideas may be false or run contrary to an individual’s beliefs or values. This approach to information or message dissemination is contrary to what democracy theory of participation is proposed to accomplish in terms of not functioning or serving activism, as discussed previously. Specifically, the populists attack opponents or blame the elite for whatever problems they see in the democratic process (Engesser et al., 2017 ).

Message Volume

By Twitter’s own estimation in 2013 there were roughly 10.75 million non-genuine Twitter accounts (D’Yonfro, 2013 ) in the form of fake followers, along with accounts associated with individuals with numerous personas (Yarow, 2013 ). The number of messages posted on Facebook or tweeted over Twitter also makes it impossible to censor or discern real news from fake news (PBS, 2016a November). As a matter of fact, it was reported that fake news such as the claim that Pope Francis endorsed Donald Trump and that Hilary Clinton sold weapons to ISIS received a significant level of attention or engagement when compared to real news by the New York Times during the 2016 US presidential election (PBS Newshour, 2016a ). When a person uses multiple online personas constructed to look like an authentic identity (i.e., sock puppetry) (Cook et al., 2014 ), it begs the question of motive. The practice of sock puppetry has one underlying commonality, to self-promote a particular cause. The practice has been linked to online business promotions (Streitfield, 2012 ), political support (Cogburn & Espinoza-Vasquez, 2011 ), and terrorist coercion (Conway, 2012 ). In regard to terrorist coercion, for instance, the ISIS terrorist group has been linked to setting up thousands of fake Twitter accounts to recruit individuals (PBS Newshour, 2016a ).

As new and social media are here to stay, so is the idea of fake news or computational propaganda. The challenge, however, is that with social media it is hard to maintain a sensible and cordial dialogue, which is critical to democracy. One of the challenges with early social networks was that in some cases only like-minded individuals were creating and joining online communities. However, social media are now extending the reach of a few like-minded individuals in a way to shape policy for societies and nations as a whole. For now, populist ideology, the alt-right, alt-truth, and the rest are prevalent. What comes next no one knows. However, if the past is indicative of the present, the future is more likely to be far worse. Not only was the alt-right group able to endorse both President Trump and Brexit, but it has been able to shift public rhetoric from embracing diversity to a homogenous society where, for example, a country rooted in immigrants is closing doors on immigration, leading the way to an anti-immigrant stance. Events following the 2016 election (e.g., the Charlottesville, VA, riots) have intensified conflicts and set back race relations in the US. However, while the president had the opportunity to calm the public, he responded late, with a response that worsened the situation. Similar criticisms were given in regard to President Trump’s response to the COVID-19 pandemic large-scale outbreak accross the US. Another hot political issue surrounded the separation of children from parents who illegally cross the US border from Mexico. However, instead of finding a constructive solution, the current administration, along with President Trump, categorized the problem as simply enforcing the previous administration’s policy. This justification was given despite evidence that there was no such policy from either the Bush or the Obama administrations (Robertson, 2018 ). As a matter of fact, some states and former US attorney generals from both the Bush and Obama administrations have linked child-parent border separations to the current US attorney general’s (i.e., Jeff Session’s) announcement of a zero-tolerance policy in April 2018. The zero-tolerance policy has resulted in around 2000 children being separated from their parents within a six-week period (ALM Media, 2018 ). However, and despite the policy, the current whereabouts of these children remain unknown.

This chapter argues that it is imperative to figure out a way to maintain sensible dialogues that promote democratic principles. However, this must be done not just on Twitter or social media, but in society at large by bridging the gap between proponents and opponents of diverse political parties on certain political ideologies. However, in order for this to succeed, individual citizens will need to confront their own confirmation biases. All parties must demonstrate a willingness to seek opinions that extend beyond their individually held beliefs and ideologies (Rothwell, 2017 ). One way of doing this is to conscientiously seek disconfirming information about issues and policies, to engage people in constructive dialogue, and to listen to the views of individuals a policy might affect. This is especially true when it comes to individuals who may have different opinions, cultures, and/or perspectives. Otherwise, the principle or foundation upon which democracy exists via participatory democracy or inclusive participation as it is now known may cease to exist. This appears to be the case when social media facilitation of propaganda is coined as genuine and truthful information. At the same time, what counts as news and foundations for ethics in news (due to mass media mediation) is already under siege, as traditional news media have lost the battle concerning their roles as mediators of facts and gatekeepers of truth.

Contributor Information

John Jones, Email: [email protected] .

Michael Trice, Email: ude.tim@ecirtm .

Bolane Olaniran, Email: [email protected] .

Indi Williams, Email: [email protected] .

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Columbia University has become the epicenter of a growing showdown between student protesters, college administrators and Congress over the war in Gaza and the limits of free speech.

Nicholas Fandos, who covers New York politics and government for The Times, walks us through the intense week at the university. And Isabella Ramírez, the editor in chief of Columbia’s undergraduate newspaper, explains what it has all looked like to a student on campus.

On today’s episode

Nicholas Fandos , who covers New York politics and government for The New York Times

Isabella Ramírez , editor in chief of The Columbia Daily Spectator

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Inside the week that shook Columbia University .

The protests at the university continued after more than 100 arrests.

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Vanderbilt Unity Poll confirms Trump support declining

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The Vanderbilt Project on Unity and American Democracy released national polling results measuring Americans’ unity and beliefs on government and democracy.

Results from the Vanderbilt Unity Poll released in March show two things about Donald Trump’s support. First, his support has dropped about 5 percentage points from December 2023, a shift consistent with other national polls. Second, a felony conviction would hurt Trump in the November election. When poll respondents are asked whether they will vote for Trump or Biden, the former president beats the current president in our March poll. But if the question mentions a Trump felony conviction, then the race flips, with Biden holding a 1-point lead. In both forms of the question, Robert Kennedy secures about 13% of the vote, 10% claim they will not vote, and another 10% indicate voting for someone other than those three contenders. These results underscore that the race remains very unsettled. If Trump were convicted in the current “hush” money case in New York City, that appears to have a big impact. In addition, the support Kennedy and other third-party candidates now enjoy is very likely to shrink as we approach election day. That reality adds further uncertainty to the contest. November remains a long way off and much could happen.

The amount of unity in this country showed its first uptick in more than a year, with a modest increase of 0.4 from 46.4% to 46.8% in the most recent Vanderbilt Unity Index. This slight increase is somewhat surprising given a contentious Republican primary, a combative Congress and growing uncertainty in the Middle East.

The “unity” measures in the national Vanderbilt Poll suggest that the public continues to feel polarized. So, for example, about 80% of Americans feel divided on “pressing issues” facing the country—a proportion that has held steady for the last three quarters of the Unity Poll. The numbers improve when asking can the country unite to solve important problems. Here about 40% of the public feels we can do so. One notable find is that there is little appetite, despite substantial polarization, to resort to violence to stop the opposition party. Only 2% to 4% of the public believe the other party must be defeated at any cost, including the use of violence. These data suggest that the kinds of actions witnessed on Jan. 6, 2021, have very limited support within the American citizenry.

Additional takeaways from the survey data include:

• There is little confidence in the decision-making of “social influencers,” and this is shared across the partisan divide. 83% of respondents, including both Republicans and Democrats, don’t trust social media influencers to make the right decisions. Among Republicans, 85% share this sentiment, and 82% of Democrats feel the same.
• There is widespread agreement that the U.S. political system needs to be more responsive. 83% of Republicans and 81% of Democrats are not confident that the political system in the United States reflects the public’s views on pressing issues.
• 30% of survey participants think that the candidate’s integrity is most important when considering which candidate to support. For Democrats, that share jumps to 42% and drops to just 13% for MAGA Republicans. Across all respondents, 16% cited a candidate’s willingness to fight as the most important dimension when voting. That jumps to 35% for MAGA Republicans. It is clear there are partisan divides on what partisans are looking for in candidates—yet another indication of our polarized politics.

The Vanderbilt Project on Unity and American Democracy also sponsors the  Vanderbilt Unity Index . “The Vanderbilt Unity Poll should provide regular snapshots of Americans’ sense of national political unity and their faith in the country’s democratic institutions,” said John Geer, head of the Vanderbilt Project on Unity and American Democracy.

SSRS conducted the Vanderbilt Unity Poll on its Opinion Panel Omnibus Platform. Between March 15 and March 17, 2024, 1,033 respondents, ages 18 and older, responded across several platforms in Spanish and English. The poll has a margin of error of +/-3.5 at the 95% confidence level.

Keep Reading

Vanderbilt Poll: State legislature’s approval remains low; bipartisan support for abortion exceptions, gun safety laws; more

Vanderbilt Unity Poll reveals significant approval of legislative compromise despite continued pessimism about national unity

Vanderbilt Unity Poll reveals a Trump conviction could significantly impact centrist voters

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• The Vanderbilt Project on Unity and American Democracy
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ByteDance Denies Media Report of Plan to Sell TikTok

FILE PHOTO: A view shows the office of TikTok in Culver City, California, March 13, 2024. REUTERS/Mike Blake/File Photo

BEIJING (Reuters) - ByteDance has no plan to sell TikTok, the company's official account said in a statement posted on Toutiao, a media platform owned by the China-based firm.

The Information earlier reported that ByteDance is exploring scenarios for selling TikTok's U.S. business without the algorithm that recommends videos to TikTok users.

U.S. President Joe Biden on Wednesday signed into law a bill that bans TikTok in the country if its owner, ByteDance, fails to divest the popular short video app over the next nine months to a year.

(Reporting by Ethan Wang, Ella Cao and Ryan Woo)

Copyright 2024 Thomson Reuters .

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In the brain, bursts of beta rhythms implement cognitive control

Bursts of brain rhythms with “beta” frequencies control where and when neurons in the cortex process sensory information and plan responses. Studying these bursts would improve understanding of cognition and clinical disorders, researchers argue in a new review.

The brain processes information on many scales. Individual cells electrochemically transmit signals in circuits but at the large scale required to produce cognition, millions of cells act in concert, driven by rhythmic signals at varying frequencies. Studying one frequency range in particular, beta rhythms between about 14-30 Hz, holds the key to understanding how the brain controls cognitive processes—or loses control in some disorders—a team of neuroscientists argues in a new review article.

Drawing on experimental data, mathematical modeling and theory, the scientists make the case that bursts of beta rhythms control cognition in the brain by regulating where and when higher gamma frequency waves can coordinate neurons to incorporate new information from the senses or formulate plans of action. Beta bursts, they argue, quickly establish flexible but controlled patterns of neural activity for implementing intentional thought.

“Cognition depends on organizing goal-directed thought, so if you want to understand cognition, you have to understand that organization,” said co-author Earl K. Miller , Picower Professor in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT. “Beta is the range of frequencies that can control neurons at the right spatial scale to produce organized thought.”

Miller and colleagues Mikael Lundqvist, Jonatan Nordmark and Johan Liljefors at the Karolinska Institutet and Pawel Herman at the KTH Royal Institute of Technology in Sweden, write that studying bursts of beta rhythms to understand how they emerge and what they represent would not only help explain cognition, but also aid in diagnosing and treating cognitive disorders.

“Given the relevance of beta oscillations in cognition, we foresee a major change in the practice for biomarker identification, especially given the prominence of beta bursting in inhibitory control processes … and their importance in ADHD, schizophrenia and Alzheimer’s disease,” they write in the journal Trends in Cognitive Sciences .

Experimental studies covering several species including humans, a variety of brain regions, and numerous cognitive tasks have revealed key characteristics of beta waves in the cortex, the authors write: Beta rhythms occur in quick but powerful bursts; they inhibit the power of higher frequency gamma rhythms; and though they originate in deeper brain regions, they travel within specific locations of cortex. Considering these properties together, the authors write that they are all consistent with precise and flexible regulation, in space and time, of the gamma rhythm activity that experiments show carry signals of sensory information and motor plans.

“Beta bursts thus offer new opportunities for studying how sensory inputs are selectively processed, reshaped by inhibitory cognitive operations and ultimately result in motor actions,” the authors write.

For one example, Miller and colleagues have shown in animals that in the prefrontal cortex in working memory tasks, beta bursts direct when gamma activity can store new sensory information, read out the information when it needs to be used, and then discard it when it’s no longer relevant. For another example, other researchers have shown that beta rises when human volunteers are asked to suppress a previously learned association between word pairs, or to forget a cue because it will no longer be used in a task.

In a paper last year, Lundqvist, Herman, Miller and others cited several lines of experimental evidence to hypothesize that beta bursts implement cognitive control spatially in the brain , essentially constraining patches of the cortex to represent the general rules of a task even as individual neurons within those patches represent the specific contents of information. For example, if the working memory task is to remember a pad lock combination, beta rhythms will implement patches of cortex for the general steps “turn left,” “turn right,” “turn left again,” allowing gamma to enable neurons within each patch to store and later recall the specific numbers of the combination. The two-fold value of such an organizing principle, they noted, is that the brain can rapidly apply task rules to many neurons at a time and do so without having to re-establish the overall structure of the task if the individual numbers change (i.e. you set a new combination).

Another important phenomenon of beta bursts, the authors write, is that they propagate across long distances in the brain, spanning multiple regions. Studying the direction of their spatial travels, as well as their timing, could shed further light on how cognitive control is implemented.

New ideas beget new questions

Beta rhythm bursts can differ not only in their frequency, but also their duration, amplitude, origin and other characteristics. This variety speaks to their versatility, the authors write, but also obliges neuroscientists to study and understand these many different forms of the phenomenon and what they represent to harness more information from these neural signals.

“It quickly becomes very complicated, but I think the most important aspect of beta bursts is the very simple and basic premise that they shed light on the transient nature of oscillations and neural processes associated with cognition,” Lundqvist said.“This changes our models of cognition and will impact everything we do. For a long time we implicitly or explicitly assumed oscillations are ongoing which has colored experiments and analyses. Now we see a first wave of studies based on this new thinking, with new hypothesis and ways to analyze data, and it should only pick up in years to come.” 

The authors acknowledge another major issue that must be resolved by further research—How do beta bursts emerge in the first place to perform their apparent role in cognitive control?

“It is unknown how beta bursts arise as a mediator of an executive command that cascades to other regions of the brain,” the authors write.

The authors don’t claim to have all the answers. Instead, they write, because beta rhythms appear to have an integral role in controlling cognition, the as yet unanswered questions are worth asking.

“We propose that beta bursts provide both experimental and computational studies with a window through which to explore the real-time organization and execution of cognitive functions,” they conclude. “To fully leverage this potential there is a need to address the outstanding questions with new experimental paradigms, analytical methods and modeling approaches.”

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