@inproceedings{2b4fa19145194bb2996d89e7a43bf321,
title = "ArMeme: Propagandistic Content in Arabic Memes",
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 ∼ 6K 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.",
author = "Firoj Alam and Abul Hasnat and Fatema Ahmad and Hasan, {Md Arid} and Maram Hasanain",
note = "Publisher Copyright: {\textcopyright} 2024 Association for Computational Linguistics.; 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024 ; Conference date: 12-11-2024 Through 16-11-2024",
year = "2024",
language = "English",
series = "EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "21071--21090",
editor = "Yaser Al-Onaizan and Mohit Bansal and Yun-Nung Chen",
booktitle = "EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference",
address = "United States",
}