Abstract
Computational Meme Understanding, which concerns the automated comprehension of memes, has garnered interest over the last four years and is facing both substantial opportunities and challenges. We survey this emerging area of research by first introducing a comprehensive taxonomy for memes along three dimensions – forms, functions, and topics. Next, we present three key tasks in Computational Meme Understanding, namely, classification, interpretation, and explanation, and conduct a comprehensive review of existing datasets and models, discussing their limitations. Finally, we highlight the key challenges and recommend avenues for future work.- Anthology ID:
- 2024.emnlp-main.1184
- 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:
- 21251–21267
- Language:
- URL:
- https://aclanthology.org/2024.emnlp-main.1184
- DOI:
- 10.18653/v1/2024.emnlp-main.1184
- Cite (ACL):
- Khoi P. N. Nguyen and Vincent Ng. 2024. Computational Meme Understanding: A Survey. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 21251–21267, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Computational Meme Understanding: A Survey (Nguyen & Ng, EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.emnlp-main.1184.pdf