ArMeme: Propagandistic Content in Arabic Memes

Firoj Alam, Abul Hasnat, Fatema Ahmad, Md. Arid Hasan, Maram Hasanain


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.
Anthology ID:
2024.emnlp-main.1173
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
21071–21090
Language:
URL:
https://aclanthology.org/2024.emnlp-main.1173
DOI:
10.18653/v1/2024.emnlp-main.1173
Bibkey:
Cite (ACL):
Firoj Alam, Abul Hasnat, Fatema Ahmad, Md. Arid Hasan, and Maram Hasanain. 2024. ArMeme: Propagandistic Content in Arabic Memes. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 21071–21090, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
ArMeme: Propagandistic Content in Arabic Memes (Alam et al., EMNLP 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.emnlp-main.1173.pdf
Software:
 2024.emnlp-main.1173.software.zip
Data:
 2024.emnlp-main.1173.data.zip