research paper on propaganda techniques

Span identification and technique classification of propaganda in news articles

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• Published: 08 May 2021
• Volume 8 , pages 3603–3612, ( 2022 )

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• Wei Li   ORCID: orcid.org/0000-0003-2738-4350 1 ,
• Shiqian Li 1 ,
• Chenhao Liu 1 ,
• Longfei Lu 1 ,
• Ziyu Shi 1 &
• Shiping Wen 2  

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Propaganda is a rhetorical technique designed to serve a specific topic, which is often used purposefully in news article to achieve our intended purpose because of its specific psychological effect. Therefore, it is significant to be clear where and what propaganda techniques are used in the news for people to understand its theme efficiently during our daily lives. Recently, some relevant researches are proposed for propaganda detection but unsatisfactorily. As a result, detection of propaganda techniques in news articles is badly in need of research. In this paper, we are going to introduce our systems for detection of propaganda techniques in news articles, which is split into two tasks, Span Identification and Technique Classification. For these two tasks, we design a system based on the popular pretrained BERT model, respectively. Furthermore, we adopt the over-sampling and EDA strategies, propose a sentence-level feature concatenating method in our systems. Experiments on the dataset of about 550 news articles offered by SEMEVAL show that our systems perform state-of-the-art.

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Introduction

Recently, with the development of related models in the field of natural language processing, research on propaganda detection also goes ahead, which originates from document level [ 1 ], then develops to sentence level [ 6 , 21 ] and now to fragment level [ 13 , 26 ]. At present, identifying those specific fragments which contain at least one propaganda technique and identifying the applied propaganda technique in the fragment are main tasks of the fragment-level propaganda detection. As an extension of text classification task in the field of natural language processing, there are many relevant advanced algorithms [ 8 , 10 , 12 , 19 , 27 ] which can be used for reference.

The corpus of news articles which have been retrieved with the newspaper 3k library and sentence splitting has been performed automatically with NLTK sentence splitter

SEMEVAL, the most influential and largest semantic evaluation competition all over the world, provides a news article corpus in which fragments containing one out of 14 propaganda techniques [ 14 , 18 ] have been annotated as shown in Fig. 1 . Based on this dataset, numerous researchers have sprung up putting forward a large quantity of algorithms to search the usages of propaganda techniques. Among the algorithms, the great mass of them are based on the popular language models such as ELMO [ 16 ], GPT [ 17 ] and especially BERT [ 3 ]. As shown in Fig.  2 , BERT Model raised by Google outperforms previous methods in 11 NLP tasks. Undoubtedly, it has achieved state-of-the-art performance on multiple NLP benchmarks [ 22 ]. In our systems, we choose BERT as our basic model as well.

In this work, we introduce our systems for span identification and technique classification of propaganda in news articles. As for the span identification task, we have set forth two of architectures working on it. The first is the BERT-based binary classifier, and the other one is the BERT-based three-token type classifier. The latter is our second-to-none system. Besides joining the most popular BERT model, we have also optimized the sampling [ 2 ] process, combined EDA [ 23 , 24 ] to prevent the overfitting of our system and adopted the sentence-level feature concatenating (SLFC), in which case our model can learn characteristics better. As for the technique classification of propaganda task, we have designed a BERT-based architecture with a dimensionality reduction Full Connected (FC) layer and a linear classifier. Same as SI task, we have utilized EDA strategy in the data loading process. The final result in “Experiment and results” shows that it is very meaningful of our optimizing and improving of the pre-trained BERT model. At last, both of our systems for SI and TC have exceeded most of the existed models and made a breakthrough.

The contributions of our paper are as follows:

We fine-tune the BERT with Linear layers and devise two accurate systems for the span identification and technique classification of propaganda in news articles.

We change the binary sequence tagging task SI into a three-way classification task by adding ’invalid’ token type and compare the binary tagging method with the three-token type method.

We propose SLFC approach in SI system. To our best knowledge, it is the first work to integrate sentence-level classification features into each word.

For our systems, we have obtained the optimal network parameters through experiments and comparative analysis.

Related work

The followings are the history and the correlative approaches about propaganda detection in news articles.

• Propaganda detection

Propaganda techniques detection is born in the process of fake news detection. Some of the earlier workers judge a news article as authentic or not only according to its origin. As we can imagine that this approach is unscientific. Recently, with the rise of artificial intelligence and machine learning, propaganda detection has attracted researcher’s eyes which promotes it to become a standalone research field.

In the early days of NLP neural networks, a bidirectional long-term short-term memory (BiLSTM) [ 5 ] layer was proposed to capture the semantic features of human language. Gradually, people began to utilize it to detect the using of propaganda in news articles. Initially, a corpus has been created for news articles automatically annotated with a novel multi-granularity neural network which is superior to some powerful BERT-based baselines [ 14 ]. Simultaneously, Proppy [ 1 ], a system to unmask propaganda in online news, has appeared for document-level propaganda detection, which works by analysing various representations, from writing style and readability level to the presence of certain keywords. Later, to further improve the accuracy of detection, researchers began to pay attention to the detection of sentence level. Hou et al. proposed a model for sentence-level detecting which could understand semantic features of language better by constructing context-dependent input pairs (sentence-title pair and sentence-context pair) [ 6 ]. After the NLP4IF workshop, fragment-level classification (FLC) of propaganda occurred. For instance, different neural architectures (e.g., CNN, LSTM-CRF, and BERT) have been explored to further improve the effect of neural networks [ 15 ].

BERT-based model

In our experiment, we have been designing models specifically for SI and TC tasks based on BERT [ 3 , 4 ] model architecture, which incorporates the strength of the other language models. As shown in Fig. 2 , BERT utilizes the transformer’s attention mechanism [ 20 ] to decode the input word vector. Unlike the previous NLP models, BERT is able to run in parallel. More uniquely, the pre-training process of BERT includes two tasks, Masked Language Model (MLM) and Next Sentence Prediction (NSP), which make the BERT model more suitable for NLP tasks. After completing different pre-training and fine-tuning for different tasks, BERT has made great progress on many NLP tasks. Many researchers have discovered the huge potential of the two-stage new model (pre-training and then fine-tuning) on BERT. As a consequence, in recent years, based on BERT, many improved models occurred, such as MT-DNN [ 7 ], XLNET [ 25 ], ALBERT [ 11 ], etc.

The architecture of the pre-trained BERT model with the Word Embedding layer for the gain of word vectors and 12-layer Encoders making up of parallel transformers for the fusion of semantic

In our system, using BERT is mainly for word feature extraction, thanks to that BERT adopts the popular feature extractor transformer, and also implements a bidirectional language model. It is the core concept of BERT to convert word into word vector input, which is added by Token Embedding, Segment Embedding, and Position Embedding to integrate the whole sentence semantics into each word in the same sentence. For our SI task, we process the obtained feature vector from BERT generator by incorporating sentence-level features into each word vector and then put it to a multi-class (prop, non-prop, invalid) classifier layer. To fit our TC task, we truncate the valid fragments and pad it for the latter FC layer and the classifier. Since there are two versions of BERT, taking our SI and TC tasks into account we use the 12-layer BERT pre-trained model as our basis.

In this section, we will introduce the details of our solutions and show the model architectures designed for the span identification (SI) and technique classification (TC) tasks.

Data process

S On account of that only a small portion of the texts use propaganda in SI and some of the techniques rarely appear in the given fragments in TC which lead to the imbalance of dataset, we have proposed two methods aimed at these two problems.

Over-sampling In the task of SI, we utilize the over-sampling (OS) [ 2 , 26 ] method to get more balanced and suitable dataset for training. Since sentences with propaganda techniques are relatively few, we sample them with a higher probability, and the number of non-propaganda ones is correspondingly reduced considering the whole training process. Nevertheless, during our experiment, we find that if over-sampling is overused, the labeled part will be too much in the sample which will cause overfitting, and the F1-score will decline to an undesirable level as a result. Therefore, when training our model, we take the strategy that the first half of the epoch uses the over-sampling and the latter part uses the sequential sampling. While TC is merely a classification task and each fragment in the given dataset corresponds to a specific propaganda technique, over-sampling is superfluous.

Data augmentation Since the pre-trained BERT model is easy to overfit, we have adopted a data augmentation scheme to improve the generalization ability and robustness [ 24 ] of the model. In the task of SI, we apply EDA Synonym Replacement (SR) [ 9 ] and Random Swap (RS) [ 23 ] to our model. Namely, each word has equal probability of being swapped or replaced by its synonyms without changing the label. Compared to short sentences, long sentences absorb more noise, which can better balance the dataset. After processing, sentences different from before are added to the training dataset. While in TC task, the data augmentation strategy is the same as that in SI which is a random process, initially. However, some of the techniques still cannot be detected such as Appeal_to_Authority, Bandwagon, Reductio_ad_hitlerum and Black-and-White_Fallacy. Aiming at this problem, on the basis of random data augmentation, we compulsively add them into the set to be data augmented. In this way, the purpose of increasing the valid noise of the training dataset is achieved. Meanwhile, the training time gets shortened as well.

Approach of span identification (SI)

The architecture of Span Identification task adopting over-sampling, data augmentation and sentence-level feature concatenating. The Concat means adding the classification feature of the sentence to its every word vector

To deal with the SI task, we first defined it as a binary classification task [ 13 ], but after experiment we found Precision and F1-score of this solution were unexpected. After analyzing the cause and effect of this issue, we propose a three-classification model to classify each word in the news articles into three token types. The concrete architecture of our model is shown in Fig. 3 . Two of them are ‘prop’ and ‘non-prop’; the other one is ‘invalid’ which means the label of some invalid words like ‘CLS’, ‘SEP’ [ 3 ] and those used to ensure the input sentence with the same length. Classifying these invalid words into a ’invalid’ token type reduces the noise and improves Precision and F1-score. Furthermore, we have utilized sampling skill and EDA to optimize the dataset.

Due to that the labels of the plain-text document offered by SEMEVAL are at char level, converting them into word level for word embedding in pre-trained BERT model is the first step. Before inputting them into the classifier, we combine the word vectors in each sentence with the feature vector of the sentence where they are. Then the word vectors with semantic integration of the sentence are normalized for the last classifier layer. As shown in Figure 3 on the right, the concatenating process generates the new concatenated vectors by placing the sentence vector in front of the word vectors. The following formula ( 1 ) shows the concatenating process mathematically:

where \(s_1,s_2\) represent the elements in a sentence vector which contains the classifying result of sentence-level prediction. And the right matrix ( \(768\times 200\) ) contains 200 word vectors (768 dim) on behalf of one sentence. By concatenating, the input matrix ( \(770\times 200\) ) of the final classifier is made as the below one. This concatenating step is reasonable considering that sentence-level prediction is more undemanding and accurate than word-level prediction. The result also shows that concatenating step plays a key role in the promotion of word-level prediction accuracy. Finally, by merging the successive words with identical propaganda technique, those specific fragments which include at least one propaganda technique are identified.

Approach of technique classification (TC)

For the multi-class classification task TC, we have utilized a Full Connection layer and a linear classifier based on BERT model, as shown in Fig. 4 . Since the dimension of the valid fragment vector is large, we utilize the former Full Connection layer for dimensionality reduction, and the second for classifying them into 14 classes. And we handle those propaganda techniques that rarely appear in the dataset by utilizing EDA so as to solve the imbalance of dataset. Comparing our model without and with EDA, the latter gets an improvement of around 4 points in F1 score as shown in Table 3 .

The architecture of Technique Classification task with segmentation and padding operations, an FC layer and a linear classifier layer

For details of TC task, we take the given text fragment identified as propaganda and its document context as the input of the pre-trained BERT generator. Different from SI task which is a word-level classification task, the TC task is fragment-level. Hence, incorporating sentence-level features into each word vector is ineffectual for TC task. As for the fragment which belongs to several sentences, we divide it into different sentences in the training process, while evaluating we treat it as a whole. Then for the sake of obtaining the valid fragment with propaganda techniques, we make segmentation of the output of BERT and pad it with invalid zero vector to a settled length (120). With the dimensionality reduction of our Full Connection layer, a linear classifier is used for 14 token types classification.

Experiment and results

In this section, we will show the experiment details and the achieved experiment results by comparing our surpassing systems respectively for SI and TC to several other attempts.

Experiment details

In our experiment, we have trained our models parallelly with 4 Nvidia GTX 1080Ti GPUs to reduce the time required. Based on the PyTorch Framework and CrossEntropy Optimizer [ 28 ] (after trying the focal loss), we have fine-tuned the pre-trained BERT model for our SI and TC tasks.

Dataset The datasets for both of the SI and TC tasks are news articles in plain text format, including train-articles, dev-articles and the label files. To begin with, we have split each article into individual sentences to reduce parameters of our model. And before the experiment, we divided the annotated corpus of about 550 articles into 80% train set for model training and 20% test set for model evaluation, respectively. By calculating the instances of each technique, we find that the dataset for TC is imbalanced as shown in Table 1 . Some of the techniques such as “Loaded_Language” has a high proportion of 34.64%, while some of the techniques such as “Black-and-White_Fallacy”, “Slogans” and “Whataboutism, Straw_Men, Red_Herring” show up less often. What is worse, neither “Bandwagon, Reductio_ad_hitlerum” nor “Thought-terminating_Cliches” has no more than 80 instances which may badly influence the training process. During training, in order to enhance the generalization capability of our model, we utilized EDA to make train set extended and more well-proportioned. Besides, particularly for SI task, we adopted the over-sampling strategy for tagged sentences.

The comparison of SI training process between our systems with and without SLFC

Evaluation metric So as to make a fair comparison, we use different evaluation criteria in different comparison experiment. For both of SI and TC tasks, we adopt the F1-score (F1) as the main metric. In addition, the general Precision (P) and Recall (R) are the secondary metric for SI task. The F1-score is denoted by the following formula:

Results: span identification (SI)

For the purpose of achieving the SI task, we have presented two diverse architectures and optimized one of them with over-sampling, EDA, and sentence-level feature concatenating (SLFC). As is shown in Table 2 , our top perform system is three-token type classification system with Over-sampling, EDA and SLFC. We have contrasted our SI system with BERT-based Binary classifier model, and BERT-based Three-token type classifier model.

As we have seen, the BERT-based three-token type classifier reaching 40.8815% F1-score, 40.1099% Precision and 41.6834% Recall behaves better than baseline which is merely BERT-based with no fine tuning and the Binary classifier model. We owe this success to the ’invalid’ token type which impairs the noise of the invalid words by classifying the irrelevant words individually. Besides, after using EDA, it only took two epochs or so to reach the peak of the Recall, without which it took about six epochs to reach the peak and the results were not as expected. Ultimately, our SI system, based on our three-token type classifier and utilizing our strategies of over-sampling, EDA and SLFC, prevails over others, which scores 44.1732% of F1-score on the test set.

Next, we will give a deep analysis of the usage of SLFC and how it benefits our system on Recall and F1-score shown in Fig. 5 . Generally speaking, the word-level prediction requires more accurate detection and there is a bigger margin of error than sentence-level prediction, which is why we give each word more information about whether the sentence it is in has propaganda with the aid of SLFC. Namely, the sentence classification prediction provides reference for the word prediction. If a sentence is propagandistic, it is of high probability containing propaganda fragments. On the contrary, if a sentence is non-propagandistic, the words in it are not of propagandistic as well. Based on this knowledge, we successively apply SLFC to our model, which does increase the F1-score by around three points and the Recall by around four points, respectively. Meanwhile, the precision does not decrease significantly. All in all, compared with no SLFC, our system identifies the propaganda spans more accurate which consequently promotes the F1-score and Recall.

Results: technique classification (TC)

To better complete the TC task, we have presented two architectures, one without EDA and another with EDA. Comparing them with the baseline which is merely BERT-based with no fine tuning, both of our systems with EDA or not have reached a new high state, improving the F1-score by two times approximately as shown in Table 3 . During our experiment, we have made an experimental comparison and analysis for our strategy of utilizing EDA in the data loading process of our TC system. The final result has indicated that our TC system with EDA improved F1-score by around 3% compared to the absent EDA system. It stands to reason that our TC system reaches the state-of-the-art in the end, which scores 57.5729% of F1-score on the test set.

The respective promotions with EDA strategy in F1-score for each of propaganda technique are shown in Table 4 . Compared with our no EDA model, in spite of the fact that three of techniques (‘Doubt’, ‘Flag-Waving’, and ‘Whataboutism, Straw_Men, Red_Herring’) have slightly decreased to some extent, more than half of the techniques have made progress in F1-score. For details, most of them have gotten about eight-point improvement on average, such as ‘Appeal_to_fear-prejudice’, ‘Exaggeration, Minimisation’, ‘Repetition’ and so on. What is worth mentioning is that the techniques named ‘Causal_Oversimplification’ and ‘Thought-terminating_Cliches’ have gotten about 14-point improvement. Thus, our TC system makes many breakthroughs on the whole, giving the credit to EDA which can enhance the data set, prompt the model to converge faster and improve the generalization ability and robustness of the model.

Parameter analysis

After a series of experiments, we have given a set of optimal parameters [epoch, learning rate (lr), sentence length (sent-len)] for the models of the two tasks. The optimal parameter combinations are shown in bold in Table 5 .

For the sentence length, which is the length of the single input into the BERT and is usually set to 256, we have set it to 200 and 210 for our SI and TC tasks, respectively. In SI task, it is attributed to that the whole sentences in dataset do not exceed 200 in length, and too much padding will lead to greater classification error. As for TC task, the maximum length of valid fragments in the dataset is 210, so we choose it as the limit for padding. In terms of learning rate, both of our choices are 3 \(\times 10^{-5}\) because our valid dataset is small. Through the analysis of the SLFC method for SI task in Sect. 4.2 , we have found that the model began to converge around the epoch 7, so we set the training epoch to 8 to prevent overfitting in our SI system. Besides, in the experiment process of TC task, we have found the epoch parameter greater than 15 caused F1-score decreased, so we set it as the best choice for our TC system. Based on the above optimal parameters, our SI and TC systems finally obtained the F1-score of 0.441732 and 0.575729, and both of the training processes have taken around 2.5 h using 4 Nvidia GTX 1080Ti graphics cards (i.e. around 10 GPU hours).

Conclusion and future work

Based on the BERT model, we have set forth two specific systems for Span Identification and Technique Classification of Propaganda in news articles. In the data loading process, we have tried two strategies, over-sampling in SI task and EDA in both of SI and TC tasks, in order to deal with the imbalance between data with and without tags and enlarge our training dataset. For SI task, we have afresh defined it as a three-token type sequence tagging task with our SI system, and adopted sentence-level feature concatenating method. For TC task, we have devised a system based on BERT with a dimensionality reduction FC layer and a linear classifier. Ultimately, we have achieved two efficient and accurate systems for propaganda detection in news articles. And the final result also confirmed that our research further perfects the BERT model.

In the future, we are going to improve the Precision, Recall and F1-score further by drawing lessons from the SpanBERT model, which may perform better. Namely, in the process of masking, we would like to cover consecutive words randomly instead of scattered words. And we are thinking about searching for a more suitable architecture of BERT adopting the popular Neural Architecture Search (NAS). Besides, we hope our model can be compressed to some extent. For instance, we can prune the classifier layer, quantify or share the parameters of our model. In these cases our model can be applied widely and conveniently in our daily lives.

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Li, W., Li, S., Liu, C. et al. Span identification and technique classification of propaganda in news articles. Complex Intell. Syst. 8 , 3603–3612 (2022). https://doi.org/10.1007/s40747-021-00393-y

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The Oxford Handbook of Propaganda Studies

Jonathan Auerbach, University of Maryland

Russ Castronovo is Dorothy Draheim Professor of English at the University of Wisconsin-Madison. He is author of three books: Fathering the Nation: American Genealogies of Slavery and Freedom; Necro Citizenship: Death, Eroticism, and the Public Sphere in the Nineteenth-Century United States; and Beautiful Democracy: Aesthetics and Anarchy in a Global Era. He is also editor of Materializing Democracy: Toward a Revitalized Cultural Politics (with Dana Nelson) and States of Emergency: The Object of American Studies (with Susan Gillman).

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This handbook includes 23 essays by leading scholars from a variety of disciplines, divided into three sections: (1) Histories and Nationalities, (2) Institutions and Practices, and (3) Theories and Methodologies. In addition to dealing with the thorny question of definition, the handbook takes up an expansive set of assumptions and a full range of approaches that move propaganda beyond political campaigns and warfare to examine a wide array of cultural contexts and practices.

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Please note you do not have access to teaching notes, propaganda analysis in social media: a bibliometric review.

Information Discovery and Delivery

ISSN : 2398-6247

Article publication date: 27 January 2021

Issue publication date: 18 February 2021

This paper aims to examine the trends in research studies in the past decade which address the use and analysis of propaganda in social media using natural language processing. The purpose of this study is to conduct a comprehensive bibliometric review of studies focusing on the use, identification and analysis of propaganda in social media.

Design/methodology/approach

This work investigates and examines the research papers acquired from the Scopus database which has huge number of peer reviewed literature and also provides interfaces to access required for bibliometric study. This paper has covered subject papers from 2010 to early 2020 and using tools such as VOSviewer and Biblioshiny.

This bibliometric survey shows that propaganda in social media is more studied in the area of social sciences, and the field of computer science is catching up. The evolution of research for propaganda in social media shows positive trends. This subject is primarily rooted in the social sciences. Also this subject has shown a recent shift in the area of computer science. The keyword analysis shows that the propaganda in social media is being studied in conjunction with issues such as fake news, political astroturfing, terrorism and radicalization.

Research limitations/implications

The lack of highly cited papers and co-citation analysis implies intermittent contributions by the researchers. Propaganda in social media is becoming a global phenomenon, and ill effects of this are evident in developing countries as well. This denotes a great deal of scope of work for researchers in other countries focusing on their territorial issues. This study was conducted in the confines of data captured from the Scopus database. Hence, it should be noted that some vital publications in recent times could not be included in this study.

Originality/value

The uniqueness of this work is that a thorough bibliometric analysis of the topic is demonstrated using several forms such as mind map, co-occurrence, co-citations, Sankey plot and topic dendrograms by using bibliometric tools such as VOSviewer and Biblioshiny.

• Social media
• Social media analysis
• Bibliometrics analysis
• Propaganda detection

Chaudhari, D.D. and Pawar, A.V. (2021), "Propaganda analysis in social media: a bibliometric review", Information Discovery and Delivery , Vol. 49 No. 1, pp. 57-70. https://doi.org/10.1108/IDD-06-2020-0065

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Copyright © 2020, Emerald Publishing Limited

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Special Issue: Propaganda

This essay was published as part of the Special Issue “Propaganda Analysis Revisited”, guest-edited by Dr. A. J. Bauer (Assistant Professor, Department of Journalism and Creative Media, University of Alabama) and Dr. Anthony Nadler (Associate Professor, Department of Communication and Media Studies, Ursinus College).

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“A most mischievous word”: Neil Postman’s approach to propaganda education

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Before there was a term called media literacy education, there was an interdisciplinary group of writers and thinkers who taught people to guard themselves against the manipulative power of language. One of the leaders of this group was Neil Postman, known for his best-selling book published in 1985, Amusing Ourselves to Death: Public Discourse in the Age of Show Business. Early in his career, Postman promoted a pedagogy of teaching and learning about language, media, and culture. In defining propaganda as “a most mischievous word,” Postman aimed to heighten learners’ attention on the abstracting function of language and its capacity to reshape attitudes, beliefs, and knowledge. 

Harrington School of Communication and Media, University of Rhode Island, USA

Research Questions

• What concepts and instructional practices did Neil Postman use to help people learn to critically analyze contemporary propaganda? 
• How does Postman’s exploration of language and meaning fit into the larger history of media literacy education? 

Essay Summary

• Postman defines propaganda as intentionally designed communication that invites people to respond emotionally, immediately, and in an either-or manner, emphasizing its capacity to undo more reasoned habits of mind. By defining propaganda in relation to its form, context, and impact on audiences, Postman acknowledges that propaganda is present in many forms of contemporary media, including entertainment, information, and persuasion. 
• Postman’s pedagogy builds upon literary close reading practices, and he uses comparison contrast to examine an example of emotion-laden propaganda and compare it with another form of expression that purports to be more informational. Transparent and emotionally evocative propaganda is not to be feared, Postman explains. But when propaganda is not transparent about its aims, when it uses language in ways that distort reality, it can be harmful, even when its intentions are well-meaning and designed to support a worthy cause. 
• Through the strategic selection of propaganda artifacts, educators may provoke learners in ways that enable dialogue and discussion to contribute towards the building of a community of inquiry. From this, learners gain awareness of the value of encountering multiple, diverse, and conflicting interpretations of media messages. As a result, pedagogies rooted in discussion and dialogue contribute to civic education. 
• Although Postman advocated for dialogue and discussion as a primary pedagogy, he acknowledged the importance of students learning to use the power of information and communication to make a difference in the world. By creating propaganda, students learn about the social responsibilities of digital authorship. 

Implications 

As an effort to help learners of all ages navigate increasingly complex media and information ecosystems, the pedagogy of media literacy has a long intellectual history. Although the term “media literacy” only became widely used during the 1990s, ideas underpinning its practice were germinating during the early part of the 20 th  century, when many philosophers, writers, critics, and academics were exploring the difficulties of living in a symbolic world replete with mass media and communication. Scholars including Kenneth Burke, Aldous Huxley, Alfred North Whitehead, Ludwig Wittgenstein, C. S. Peirce, John Dewey, Ernest Cassirer, Edward Sapir, and I. A. Richards all offered ideas about the relationship between expression, media, education, and democracy that influenced the work of later educators and scholars who developed and used the term media literacy (Hobbs, 2016). 

In the 1930s, as fascism grew in Europe and around the world, scholars noted that although humans’ use of language enabled vast innovation, it also put people at tremendous risk from the harmful propaganda of demagogues and dictators. Educators were fascinated with the challenge of teaching about contemporary propaganda in the years leading up to World War II, as film and radio offered new ways to combine entertainment, information, and persuasion. The Institute for Propaganda Analysis (IPA) offered monthly publications to educators who were urged to help people recognize the rhetorical strategies used by propagandists (Miller & Edwards, 1936). Based in New York City, the organization had active correspondence with high school teachers from across the region and across the nation. More than 1 million students participated in IPA learning activities on the topic of propaganda. Although the IPA folded at the onset of American involvement in the war, many teachers continued to teach students how to recognize “glittering generalities,” “card stacking,” and “bandwagon” and other rhetorical appeals (Hobbs & McGee, 2014). Although we don’t know for certain, Neil Postman himself may have learned to identify propaganda techniques as a high school student in New York City public schools. 

Neil Postman, known for his best-selling book published in 1985,  Amusing Ourselves to Death: Public Discourse in the Age of Show Business,  influenced a generation of media literacy educators with his insights on inquiry learning in and out of schools, the role of technology in shaping culture and values, and the narratives that underpin the aims of education. Early in his career, Postman promoted a pedagogy of teaching and learning about language, media, and culture that focused on the systematic analysis and exploration of modes of communication (Postman, 1974a), which he termed media ecology (Postman, 1974b). But how does Postman’s work on propaganda fit into the history of media literacy and propaganda education? 

Actually, Postman’s interest in propaganda was incidental to a much larger narrative, situated at the blurry intersections of the humanities, media studies, and education. Well before he became a media scholar, Postman was a teacher and teacher educator (Postman, 1958; 1961). Postman’s work demonstrates the central practice of the critical analysis of language (Postman, 1976), using specific media texts or artifacts of popular culture. In examining Postman’s approach to teaching propaganda in the 1970s in the years leading up to his formulation of the scholarly practice of media ecology, there are some themes in his work that have implications for how propaganda education is currently conceptualized within contemporary dialogues about media literacy education. When media artifacts are strategically chosen by the instructor, they may provoke learners into genuine thinking (Postman, 1979). The resulting dialogue, discussion, and creative expression in the classroom enable students to recognize the active process of meaning-making and interpretation. Such pedagogies may cultivate communities of inquiry that embody the collaborative practices of engaged citizenship (Kahne & Bowyer, 2019). For these reasons, Postman’s close analysis of 20 th -century propaganda offers some value for today’s educators seeking to help learners thrive in a culture saturated with new forms of digital propaganda.

The pedagogy of media literacy education is rooted in the practices of critical reading and creative media production, where a focus on media and popular culture enables rich connections between classroom and contemporary culture (Hobbs, 2010). These practices were foundational to Neil Postman’s pedagogy and stemmed from his background in English education (Thaler, 2003). Following in the footsteps of Marshall McLuhan (1960), Postman emphasized the value of using topics, issues, and materials that were relevant to children and young people (Postman, 1995). Like McLuhan, Postman included examples from advertising, news, music, and even fashion, conflating city and classroom (Mason, 2016). By emphasizing the   interconnectedness of technology, communication, art, and symbolic forms, both Postman and McLuhan wanted to help people better “understand the past, make sense out of the present, and provide us with the best hope of anticipating and planning for the future” (Strate, 2017, p. 245). 

Because propaganda comes in many diverse genres and forms (including public service announcements, political campaigns, news media, movies, memes, and social media, just to name a few) it provides a rich array of opportunities for learners to engage in sense-making using strategies of reasoning and interpretation. Sadly, the scholarly literature on literacy education still makes little acknowledgement of the fact that advertising and propaganda are persuasive genres that demand different types of critical reading practices than texts whose purpose is primarily informational (Hobbs, 2020a). To interpret persuasive genres, learners must be attentive to the emotional dimensions of messages as they make inferences about audience interpretation and authorial intent. They must imagine the potential impact and consequences of messages upon different viewers, readers, or listeners. By identifying the target audience and rhetorical appeals used to construct a message, learners come to appreciate how propaganda engages the active participation of audiences, whose hopes, fears, and dreams are addressed through symbolic expression. 

Long before terms such as implicit bias and confirmation bias were formulated, Postman articulated how dialogue and discussion activities increase learners’ awareness of how their own beliefs and prior knowledge might lead them to differentially interpret the meaning, quality, utility, and value of propaganda that can be found in information, entertainment, and persuasion. Moreover, as learners interpret and analyze propaganda, conversations inevitably get into deeper terrain, opening up ethical issues including the changing nature of knowledge, the limits of human freedom, and the role of propaganda in gaining and maintaining social and institutional power (Hobbs, 2020b). 

Postman understood that the motives of the propagandist were inherently unknowable and that even propaganda that is designed to support or advance a worthy cause can be harmful when it distorts people’s understanding of social reality. Building on the work of Jacques Ellul (1979), Postman recognized that moral and ethical judgments about the relative benefits and potential harms of propaganda are baked into the interpretation process. For this reason, people need advanced skills of interpretation and analysis because of the linguistic and epistemic mischief caused by propaganda, which can create “a thicket of unreality” (Boorstin, 1961, p. 3). 

Writing at a time before email and the Internet were becoming ubiquitous, Postman recognized that information technologies were creating a culture “without moral foundation” by altering our understanding of what is real (Postman, 1994). He noted that every tool has an ideological bias, a predisposition to construct the world as one thing rather than another. For today’s learners, understanding the propaganda function of algorithmic personalization may lead to a deeper consideration of texts that tap into audience values for aesthetic, commercial, and political purposes (Hobbs, 2020a). But these competencies and skills cannot merely be transmitted through a teacher’s lecture. They must be cultivated through active participation in a discourse community. 

Recently, there has been a call for media literacy education to focus less on knowledge and skills and more on “connecting humans, embracing differences” through relational activities, where the process matters as much as the outcome (Mihailidis & Viotty, 2017, p. 451). As my analysis of Postman’s lesson reveals, media literacy education has long been conceptualized as a dimension of civic education; indeed, much of Postman’s writing about education emphasizes its role in the construction of community, where the critical analysis of media messages is explicitly presented as a collaborative practice of citizenship, designed to advance the exercise of democratic rights and civil responsibilities. For example, in a brilliantly titled book,  Teaching as a Subversive Activity  (1969), Postman and Weingartner explain how inquiry-learning pedagogies advance learner confidence and autonomy by empowering students to take responsibility for their own interpretations of the symbolic environment. In their formulation of inquiry learning, the teacher rarely tells students a personal opinion about a particular social or political issue and does not accept a single statement as an answer to a question. The teacher encourages student-to-student interaction as opposed to student-to-teacher interaction and the teacher generally avoids acting as a mediator or judge. Lessons develop from the interests and responses of students and not from a predetermined curriculum. 

Such discourse alters the nature of the authority relationship between teacher and students, putting students in the driver’s seat. When students have more control over their own learning, motivation and engagement improve. Postman knew that these ideas about the value of activating critical questions in the classroom would appeal to students at a time when they were increasingly questioning the “establishment.” He also knew that this way of teaching would be off-putting to traditionalists who cling to the power hierarchies embedded in teaching. He also knew it would be off-putting to protectionists who see media culture as the enemy of education. He gently provoked English teachers by noting, “If these questions strike you as politically dangerous, I would remind you that there is nothing more dangerous to the future of our country than curriculums which keep students playing with sentence diagrams while the languages of reality go swirling, uncomprehended, around their ears” (Postman, 1967, p. 1165). 

This type of admonishment continues into the present era, as in the position statement developed by the National Council of Teachers of English (2019), which advocates for a new vision of English education. It urges English teachers to promote pedagogy and scholarly curricula in English and related subjects that instruct students in civic and critical literacy, helping students to “analyze and evaluate sophisticated persuasive techniques in all texts, genres, and types of media, current and yet to be imagined.” Teachers are also encouraged to “model civic literacy and conversation” where students can have an informed discussion and engage with current events and civic issues “while staying mindful and critical of the difference between the intent and impact of their language” (NCTE, 2019, p. 1).

Postman wanted teachers to feel deeply responsible for educating students who would be capable of democratic self-governance (Ross, 2009). Like Ellul (1973), he recognized that there is a moral and ethical dimension in resisting the technologies and the forms of propaganda that perpetuate illusions. But even more important than building people’s resistance to propaganda is the practice of restoring the public sphere “by reclaiming participation in political debate and action” (Cunningham, 2002, p. 190). Media literacy activities function as civic education because they increase learners’ awareness of the epistemic value of encountering multiple, diverse, and even conflicting perspectives  ( Kahne et al., 2015).

Contemporary media literacy education emphasizes composing media, not just analyzing it. While he did not use the term media literacy, Postman did use the term multimedia literacy, referring specifically to a broadened conceptualization of the expressive function of literacy. According to Postman, students should express what they know through a wide range of communication skills beyond merely reading and writing. Educators should place equal importance on “speaking, listening, filming, audio-taping, video-taping, painting, and other possibilities” (Postman, 1974, p. 61). When students create propaganda for social causes that matter to them, they recognize that propaganda can be beneficial. Young people crave opportunities to develop civic identities as change agents (Hobbs, 2020b). Through the study of propaganda, learners inevitably reflect on the ethical obligations of the people who create media, those who provide digital platforms to distribute content, and those who not only make choices and interpret messages, but respond, remix, and share content.

To better understand how Postman’s ideas about propaganda embodied some fundamental practices of what would later be called media literacy education, I first consider Postman’s unique definition of propaganda, presented as part of a “lesson plan” in his 1979 essay entitled “Propaganda.” This work was excerpted from his 1976 book,  Crazy Talk, Stupid Talk.  In this work, Postman’s identity as a teacher is quite evident. His exploration of propaganda focuses on both the dangers of either–or framing to short-circuit critical thinking and the many ways in which language (and other symbol systems) shape reality. These two ideas form a bedrock foundation for the later arguments he developed concerning the biases of technologies and their impact on culture and values. In analyzing propaganda, Postman shows how language limits critical thinking about the true complexity of the world and he offers a deceptively simple instructional practice that promotes engaged dialogue and discussion with the goal of fostering critical autonomy and civic participation in learners of all ages.

Postman’s definition of propaganda

In his 1979 essay, Postman defines propaganda in a unique way, as “language that invites us to respond emotionally, emphatically, more or less immediately, and in an either-or manner” (p. 130). Postman’s definition centers on the form and context of propaganda and its impact on readers, viewers, or listeners. This is a definition whose purpose seems aligned with the goals of an educator who wants people to be able to recognize and resist propaganda. As a definition, it also offers strategic insights for those who wish to create propaganda as a means to accomplish their activist goals. 

Other definitions of propaganda of the time period seem to have different goals. For example, consider Ellul’s definition, where propaganda is distinguished by its reliance on technology, widespread dissemination, and embeddedness in institutions of power (Ellul, 1973). Writing at about the same time as Postman, Altheide and Johnson (1980, p. 23) offer a definition of propaganda as a communication strategy that uses truth to maintain an organization’s apparent legitimacy. Definitions of propaganda evolve and change over time because those who write definitions are responsive to the context and situation of a particular era (Cunningham, 2002). Today, new terms like computational propaganda have emerged to explain how power/knowledge structures are embodied in technologies, platforms, algorithms, and code (Woolley & Howard, 2018). 

It is noteworthy that in his definition, Postman refuses to demonize or use metaphorical language that conceptualizes propaganda as a dangerous weapon. By including the memorable phrase about cultivating either-or thinking, he aims to foreground how language simplifies the natural complexity of the world. He is concerned that dichotomies like good/bad and true/false may activate tribal loyalties and identities that may lead people to bypass critical thinking. Postman resists the easy tendency to use propaganda as a “smear word” (McKenzie, 1942) because doing so would interfere with the principal insight that he aims to convey: the inevitable ways that language constructs and shapes social reality.

Language is not merely a set of rhetorical strategies, but a way that we humans interact with our environment. Through languages, art forms, symbol systems, technologies, and platforms, people relate to the environment as interdependent parts of an ecosystem. Propaganda simplifies complex information through abstracting, which is an active cognitive process where we take into ourselves something from the outside environment, using perception, information, and ideas “which provide us with a necessarily incomplete and selective summary, or map of our environment” (Strate, 2010, p. 35). Through abstracting, we collaboratively create and accumulate knowledge. But in this process, a lot is left out, because every choice must be a particular choice. Every word, graphic display, or numerical symbol is a particular, limited, and partial one. 

Postman points out that because all language is essentially persuasive, “the distinction between persuasion and other types of talking does not seem to be very useful” (1979, p. 132). One of his first graduate students, Terence Moran, recalls that one of the first axioms Postman presented to the class was that “words themselves have no meanings, that only people have meanings which they try to express through words” (Moran, 2004, p. 26). For Postman, language does much more than merely describe events and things in the world; language also tells us what we should notice, who we should ignore, and what we should treasure or despise. Because the words people use have embedded ideologies, Postman appreciated Alfred Korzybski’s point that “Whatever we say something is, it is not” (Postman, 2003, p. 358). 

Comparison–contrast pedagogy 

Postman’s short essay offers a comparison–contrast activity designed to illustrate some key ideas about how the language of propaganda may short-circuit or hijack critical thinking. Postman describes an activity that is designed to promote dialogue and discussion. While he does not explicitly identify it as a lesson plan, his didactic use of language makes it evident throughout that the presentation of examples is designed as a learning activity. Using content about both the Vietnam War and the Black Power movement, Postman intentionally chooses topics certain to appeal to adolescents and young adult learners of the late 1960s and early 1970s. In modeling the use of contemporary news media in the English classroom, he shares two quite different forms of news propaganda that offer ideologically distinctive stances that support both causes. 

The first artifact that Postman analyzes is a letter to the editor published in the  Indianapolis Star  in 1968, with the headline, “A Letter from a War Veteran.” The letter takes on the fictionalized first-person voice of a dead soldier who died in the Vietnam War, referencing the loss of family members, and pulling out all the emotional stops to arouse patriotic feelings and support for the war effort. Postman notes that this work was “constructed to evoke Indianapolis passions in favor of the war” (Postman, 1979, p. 130). Letters to the editor may function as propaganda. They are transparent in their persuasive purpose and designed to unify a group of people and build social consensus.

Then he presents a brief, close analysis of an informative paragraph on the life of George Jackson, the founder of an African American Marxist–Leninist revolutionary prison activist group called the Black Guerilla Family. Postman’s voice simply drips with sarcasm as he explains that the propaganda about George Jackson circulated among intellectuals in New York City “when it was the fashion to elevate revolutionaries to sainthood” (p. 131). In analyzing how the passage presents a hagiographic version of Jackson’s criminal history, making a violent man seem like a choirboy, Postman’s tone is alternatively incredulous, lighthearted, and playful. He calls the reader’s attention to the way narrative structure and language choices seem to minimize the scope of Jackson’s criminal behavior. By representing Jackson in a heroic way, the author of the passage misleads readers, distorting reality. Postman does take pains to point out that he has no complaints about the man whose story is being told. What’s arouses his ire is propaganda “that attempts to conceal itself as information” (p. 133). Postman also fears that people will become habituated to the emotional pull of propaganda, building on the work of Gustave LeBon in noting that propaganda can turn groups into intoxicated, mindless crowds.

Omitted from the short essay is any reflection on how learners may encounter such a lesson. We can easily imagine how students’ engaged dialogue would revolve around their different allegiances towards the content of the two artifacts. Students who hate the Vietnam War might disagree with Postman’s appreciation of the honest transparency of the pro-war letter writer, resenting the author’s strategy for activating strong emotion and the depiction of sympathy for veterans. Students who see themselves as stakeholders in the fight against racism might be offended when Postman problematizes the warm-and-fuzzy depiction of the life history of an African-American prison activist. It’s easy to imagine the lively response of learners to such an exercise as they find themselves with increased awareness of how their own beliefs, allegiances, values, and prior knowledge might lead them to differentially interpret the utility and validity of these two different forms of propaganda. 

In selecting and analyzing these two examples of propaganda in news, Postman’s goal here seems to provoke: he is challenging learners out of their quick and easy assumptions about good/bad and true/false. This instructional practice also enables Postman to show the value of comparison contrast in helping students recognize how authors can use language in ways that transparently reveal or strategically disguise their purposes, intentions, and goals. 

In calling propaganda a most mischievous word, Postman heightens learners’ attention on the abstracting function of language and its capacity to reshape attitudes, beliefs, and knowledge. Postman refuses to demonize sentimental, patriotic propaganda and he examines the potential harms of propaganda that misrepresents reality in order to promote a worthy cause. In doing so, Postman points out the power of language to activate strong emotions while simultaneously elucidating how well-meaning individuals can produce compelling unrealities in support of their causes.

Methods 

Historical research methods were implemented in this study to examine Neil Postman’s conceptualization of propaganda in relation to media literacy education. To help readers comprehend the textured complexities of the past, I provide a close analysis of a single comparison–contrast activity published in 1979 that demonstrates one lesson in the pedagogy of propaganda analysis. This work is explicated through document analysis, a qualitative research method used for contextualizing research within a subject or field (Bowen, 2009). 

I focused my attention on understanding the context of Neil Postman’s early work in education (published before 1980) to better understand significant influences on his pedagogical approach to the study of propaganda. Primary source materials include Postman’s published books, journal articles, book reviews, magazine pieces for educational publications, and interviews. Postman’s works that examined general semantics were included, but I did not focus on the body of work on media ecology which has been extensively reviewed by communication scholars. Secondary source materials included published works about Neil Postman by Lance Strate (2006), Terence Moran (2017, 2004) Thom Gencarelli (2000), and Peter Thaler (2003) who wrote about Postman’s identity as a teacher and human being. 

Of course, I also draw upon my own relationship with Postman and our occasional professional conversations about media literacy education. Document analysis has limitations: It does not provide all of the necessary information required to answer research questions. But this method helped me identify certain concepts and instructional practices that Postman used to help people learn to critically analyze contemporary propaganda well before the term “media literacy” was in wide circulation. Through my close reading of texts from the past, this paper shows how Postman’s work on language and meaning fits into the larger history of media literacy and propaganda education.

• / Media Literacy
• / Propaganda

Cite this Essay

Hobbs, R. (2021). “A most mischievous word”: Neil Postman’s approach to propaganda education. Harvard Kennedy School (HKS) Misinformation Review . https://doi.org/10.37016/mr-2020-65

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The author did not receive any funding for this paper.

Competing Interests

The author has no conflict of interests to disclose.

This research did not involve human subjects and thus was not subject to approval from an institutional review board. The use and copyright restrictions of all archived and published materials were followed.

This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are properly credited.

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