Detecting and Understanding Harmful Memes: A Survey
Published in IJCAI’22 (Survey), 2022
Recommended citation: Sharma, Shivam and Alam, Firoj and Akhtar, Md. Shad and Dimitrov, Dimitar and Da San Martino, Giovanni and Firooz, Hamed and Halevy, Alon and Silvestri, Fabrizio and Nakov, Preslav and Chakraborty, Tanmoy. (2015). "Detecting and Understanding Harmful Memes: A Survey.” Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22. https://doi.org/10.24963/ijcai.2022/781
The paper addresses the challenge of identifying harmful online content, specifically harmful memes that often mix text, visuals, and audio. It introduces a new typology for harmful memes and highlights gaps in research, like the lack of suitable datasets for some types of harmful memes. The study also discusses challenges in understanding multimodal content and the need for further research in this area.
Recommended citation: Sharma, Shivam and Alam, Firoj and Akhtar, Md. Shad and Dimitrov, Dimitar and Da San Martino, Giovanni and Firooz, Hamed and Halevy, Alon and Silvestri, Fabrizio and Nakov, Preslav and Chakraborty, Tanmoy. (2015). “Detecting and Understanding Harmful Memes: A Survey.” Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22.