A r M eme: Propagandistic Content in A rabic Memes (2024)

Abstract: With the rise of digital communication memes have become a significant medium for cultural and political expression that is often used to mislead audience. Identification of such misleading and persuasive multimodal content become more important among various stakeholders, including social media platforms, policymakers, and the broader society as they often cause harm to the individuals, organizations and/or society. While there has been effort to develop AI based automatic system for resource rich languages (e.g., English), it is relatively little to none for medium to low resource languages. In this study, we focused on developing an Arabic memes dataset with manual annotations of propagandistic content. We annotated \sim6K Arabic memes collected from various social media platforms, which is a first resource for Arabic multimodal research. We provide a comprehensive analysis aiming to develop computational tools for their detection. We made the dataset publicly available for the community.

## introduction social media platforms have enabled people to post and share content online. a significant portion of this content provides valuable resources for initiatives such as citizen journalism, raising public awareness, and supporting political campaigns. however, a considerable amount is posted and shared to mislead social media users and to achieve social, economic, or political agendas. in addition, the freedom to post and share content online has facilitated negative uses, leading to an increase in online hostility, as evidenced by the spread of disinformation, hate speech, propaganda, and cyberbullying (brooke, 2019; joksimovic et al., 1 propaganda is a form of communication designed to influence people's opinions or actions toward a specific goal, employing well-defined rhetorical and psychological techniques (for propaganda @xcite . 2019; @xcite @xcite . a lack of media literacy foot_0 is also a major factor contributing to the spread of misleading information on social media @xcite . this can lead to the uncritical acceptance and sharing of false or misleading content, which can quickly disseminate through social networks. in their study, @xcite highlight the crucial role of media literacy in mitigating the spread of fake news among users of platforms such as instagram and twitter. online content typically consists of different modalities, including text, images, and videos. disinformation, misinformation, propaganda, and other harmful content are shared across all these modalities. recently, the use of internet memes have become very popular on these platforms. a meme is defined as "a collection of digital items that share common characteristics in content, form, or stance, which are created through association and widely circulated, imitated, or transformed over the internet by numerous users" @xcite . memes typically consist of one or more images accompanied by textual content @xcite . while memes are primarily intended for humor, they can also convey persuasive narratives or content that may mislead audiences. to automatically identify such content, research efforts have focused on addressing offensive material @xcite , identifying hate speech across different modalities @xcite , and detecting propaganda techniques in memes @xcite . among the various types of misleading and harmful content, the spread of propagandistic content can significantly distort public perception and hinder informed decision-making. to address this challenge, research efforts have been specifically directed towards defining techniques and tackling the issue in different types of content, including news articles @xcite , tweets @xcite , memes @xcite , and textual content in multiple languages @xcite . most of these efforts have focused on english, with relatively little attention given to arabic. prior research on arabic textual content includes studies presented at wanlp-2022 and arabicnlp-2023 @xcite . however, for multimodal content, specifically memes, there are no available datasets or resources. to address this gap, we have collected and annotated a dataset consisting of approximately 6,000 memes, categorizing them into four categories (as shown in figure 1 ) to identify propagandistic content. below we briefly summarize the contribution of our work. ## related work social media has become one of the main ways of sharing information. its widespread use and reach is also responsible for creating and spreading misinformation and propaganda among users. propagandistic techniques can be found in various types of content, such as fake news and doctored images, across multiple media platforms, frequently employing tools like bots. furthermore, such information is distributed in diverse forms, including textual, visual, and multi-modal. to mitigate the impact of propaganda in online media, researchers have been developing resources and tools to identify and debunk such content. ## results and discussion in for the multimodal models, the model trained with convnext + arabert + svm shows the highest performance (0.659 weighted f1) among the multimodal llms. the performance of gemini is significantly worse than that of the gpt-4 variants. gpt-4o demonstrates higher performance compared to gpt-4 vision. in our experiments all multimodal model are tested using zero-shot setting, therefore, such lower performance compared to the fine-tuned models are expected. ## additional experiments we further conducted experiments using the dataset released as part of the araieval shared task 2 @xcite , focusing on two labels: propaganda and not-propaganda. the dataset statistics are provided in ## conclusions and future work in this study, we introduce a manually annotated dataset for detecting propaganda in arabic memes. we have annotated ∼ 6k memes with four different categories, making it the first such resource for arabic content. to facilitate future annotation efforts for this type of content, we developed annotation guidelines in both english and arabic and are releasing them to the community. our work provides an in-depth analysis of the dataset and includes extensive experiments focusing on different modalities and models, including pre-trained language models (plms), large language models (llms), and multimodal llms. our results indicate that fine-tuned models significantly outperform llms. in future work, we plan to extend the dataset with further annotations that include hateful, offensive, and propagandistic techniques.