mlsanityData to 14 Oct 2026

ArMeme: Propagandistic Content in Arabic Memes

arXiv 2406.03916 ↗First posted 6 Jun 2024cs.CL cs.AI cs.CVcited by 5

Not on the map. Why?

Abstract (arXiv)

With the rise of digital communication, memes have become a significant medium for cultural and political expression that is often used to mislead audiences. Identification of such misleading and persuasive multimodal content has become more important among various stakeholders, including social media platforms, policymakers, and the broader society as they often cause harm to individuals, organizations, and/or society. While there has been effort to develop AI-based automatic systems 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 will make them publicly available for the community.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 28 of its 93 references that are in the map. First posted on arXiv 6 Jun 2024.

Builds on

28 of 93 references.

References · 28

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  5. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  6. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  7. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,222
  8. 2019VisualBERT: A Simple and Performant Baseline for Vision and Languagecited by 1,296
  9. 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 736
  10. 2020The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memescited by 490
  11. 2022Multimodal Learning with Transformers: A Surveycited by 400
  12. 2020AraBERT: Transformer-based Model for Arabic Language Understandingcited by 362
16 more
  1. 2019Fine-Grained Analysis of Propaganda in News Articlescited by 133
  2. 2019Supervised Multimodal Bitransformers for Classifying Images and Textcited by 130
  3. 2019Exploring Hate Speech Detection in Multimodal Publicationscited by 103
  4. 2022Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusioncited by 94
  5. 2023Prompting for Multimodal Hateful Meme Classificationcited by 69
  6. 2022Detecting and Understanding Harmful Memes: A Surveycited by 58
  7. 2020Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Armscited by 41
  8. 2021Pre-Training BERT on Arabic Tweets: Practical Considerationscited by 39
  9. 2017Argotario: Computational Argumentation Meets Serious Gamescited by 30
  10. 2024Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Modelscited by 26
  11. 2019Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detectioncited by 24
  12. 2024Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articlescited by 21
  13. 2023LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarkingcited by 20
  14. 2020Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Responsecited by 19
  15. 2023ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Textcited by 19
  16. 2019Multilingual Multimodal Digital Deception Detection and Disinformation Spread across Social Platformscited by 7

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 2.

5 papers.