Paper Bullets: Modeling Propaganda with the Help of Metaphor (2023)

Abstract: Propaganda aims to persuade an audience by appealing to emotions and using faulty reasoning, with the purpose of promoting a particular point of view. Similarly, metaphor modifies the semantic frame, thus eliciting a response that can be used to tune up or down the emotional volume of the message. Given the close relationship between them, we hypothesize that, when modeling them computationally, it can be beneficial to do so jointly. In particular, we perform multi-task learning with propaganda identification as the main task and metaphor detection as an auxiliary task. To the best of our knowledge, this is the first work that models metaphor and propaganda together. We experiment with two datasets for identifying propaganda techniques in news articles and in memes shared on social media. We find that leveraging metaphor improves model performance, particularly for the two most common propaganda techniques: loaded language and name-calling.

## introduction propaganda aims to influence an audience. it is a type of information that, whether true or false, tries to promote a particular agenda @xcite by appealing to emotions or by using faulty reasoning @xcite . although this communication strategy comes in many forms, it is conveyed using specific persuasion techniques that exploit our psychology to sell us an idea or a point of view @xcite . in figure 1 , we can see an example of such techniques used in a meme shared on social media. another rhetorical device at the heart of many successful communication strategies is metaphor. postulated as a primordial mechanism to conceptualize what we think and experience @xcite , metaphor works by mapping a concept in one domain (often a physical domain) to another domain (usually an abstract one) by means of a systematic figure 1 : meme containing propagandistic techniques @xcite . these techniques are highlighted with bounding boxes for illustration purposes. association. for instance, the term "paper bullets" foot_0 connects the domains of information and war, illustrating the weaponization of information. in the same way that propaganda can exploit automatic shortcuts our brain uses to process information (e.g., stereotypes) @xcite , metaphors can affect how we reason about a particular situation or issue by evoking a different semantic frame @xcite . research shows that characterizing crime as a beast delivered more punishment-oriented strategies to fight crime @xcite . con-versely, referring to crime as a virus gathered a more significant number of preventive measures to cure it. as a persuasive device, framing has successfully been used in politics @xcite @xcite to shift the public opinion about a particular topic. moreover, the use of metaphors by politicians in their posts on social media increases engagement with their electorate @xcite . some propagandist techniques and metaphors can exhibit a similar intention by the author. for instance, the most common technique is the use of loaded language to increase the emotional response of the audience (e.g., "... disastrous [nuclear deal]"). likewise, metaphor can also elicit an emotionally charged reaction @xcite . the following example combines both: "the ruinous reforms". similarly, name-calling connects the object of the propaganda campaign with terms the target audience sees positively or negatively @xcite . this technique seeks a love or hate emotional response, and it could also alter the semantic frame (e.g., "crooked hillary" or "deep state officials"). other salient examples where different propagandist techniques employ metaphor can be found in the propaganda techniques corpus (ptc) @xcite , including exaggeration ("a tsunami of lies and smear"), appeal to fear ("[bubonic plague in madagascar] could even spill over into neighboring countries and beyond"), doubt ("why is the u.s. singling out iran ...") and flag-waving ("it is time to take our government back ..."), among others. we explore how metaphor detection can aid propaganda technique classification under the multitask learning paradigm. computational modeling for propaganda detection was initially studied as a document-level classification task in news articles @xcite @xcite . more recently, annotation efforts produced datasets that identify the text spans where particular forms of propaganda are used. our work builds upon the most extensive corpus of fragment-level propaganda techniques to date @xcite and on shared task 6 from semeval-2021 @xcite to identify persuasive techniques in both news articles and internet memes, respectively. we analyze how a multi-task learning approach that leverages metaphor detection can improve results in propa-ganda identification. to our knowledge, this is the first study of the role of metaphor in computational propaganda identification. we produce the first models that combine the two phenomena and analyze their predictive capability, both quantitatively and qualitatively. our findings show that metaphor detection can increase performance for certain types of propaganda. we see improvements across multiple tasks covering both datasets. the gains are more pronounced for name-calling, with significant results for the news domain. furthermore, our models' predictions suggest that propagandist content uses figurative language more extensively than nonpropagandist text. current state-of-the-art textual metaphor detection is powered by large pre-trained neural network models @xcite @xcite that have been trained using datasets of billions of words. these models can leverage word representations that carry context-sensitive semantic information. as the latest shared task on metaphor detection highlights (acl 2020) @xcite , more than half of the participants used bert @xcite or its variants, widely successful pre-trained models that perform well on downstream tasks. in addition, metaphor detection has successfully been used as an auxiliary task in multi-task learning (mtl) @xcite for emotion classification @xcite , political perspective, affiliation, and framing @xcite ; and aspect-based sentiment analysis @xcite , among others. the mtl approach builds on the idea that the same model can encode valuable features for different tasks that would help each other's performance. as metaphor is extensively used in everyday language and dramatically influences the expressiveness of the message, it can help in a significant number of semantic tasks. ## tasks and datasets in this work, we examine six tasks for fragmentlevel propagandist technique identification. half of them use labeled data from news articles, while the others use textual information from memes shared on social media. for each domain, we perform a multi-label classification task -to identify all propagandist techniques in the dataset -and two single-label classification tasks to detect the two most common persuasive techniques: loaded language and name-calling. the single-label tasks ignore the rest of the labels in the dataset while using the same textual input as the multi-label tasks. in addition, mtl models include metaphor detection as an auxiliary task. this task aims to detect all content words used as metaphors in a given text. ## analysis and discussion given the shared traits between the use of metaphor and specific propagandist techniques, we hypothesized that it can be beneficial to model them jointly. we split the analysis into two subsections discussing quantitative and qualitative aspects. ## conclusion and future work in this work, we explored the influence of metaphor detection on propaganda technique identification in a multi-task learning setup. joint modelling of metaphor and propaganda was performed using two propaganda datasets from different domains: news articles and internet memes. we experimented with six different propaganda detection tasks, including multi-label propaganda technique identification and single-label tasks for the two most common propagandist techniques: name-calling and loaded language, for each dataset. incorporating metaphor detection yielded performance improvements in five of the six tasks considered, with the highest improvements observed for the name-calling technique. moreover, the different datasets showed similar patterns in performance changes. we supplemented the task performance results with an analysis of the prevalence of metaphor in the propaganda corpora and qualitatively examined a range of examples of metaphorical language use in propagandist fragments. we are the first to investigate the interaction of these two phenomena and our promising results encourage further research in this direction. in future work, we plan to extend our analysis to other propaganda techniques. in view of the emergence of datasets for other languages, such as an the arabic propaganda detection shared task at wanlp'2022 and the multilingual semeval-2023 task 3 subtask 3 on propagada detection in english, french, german, italian, polish, and russian, we plan future multi/cross-lingual experiments.