Detecting Harmful Memes and Their Targets
Published in ACL’21 (Findings), 2021
Recommended citation: Shraman Pramanick, Dimitar Dimitrov, Rituparna Mukherjee, Shivam Sharma, Md. Shad Akhtar, Preslav Nakov, and Tanmoy Chakraborty. 2021. Detecting Harmful Memes and Their Targets. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 2783–2796, Online. Association for Computational Linguistics. https://aclanthology.org/2021.findings-acl.246/
This study explores the use of internet memes in social media, particularly their rise in conveying political and socio-cultural opinions. Harmful memes, often complex and satirical, have become a concern. The study introduces two tasks: detecting harmful memes and identifying their target (individual, organization, etc.). We present HarMeme, a dataset with COVID-19-related memes, and emphasize the importance of multimodal models for these tasks while acknowledging existing limitations and the need for more research.
Recommended citation: Shraman Pramanick, Dimitar Dimitrov, Rituparna Mukherjee, Shivam Sharma, Md. Shad Akhtar, Preslav Nakov, and Tanmoy Chakraborty. 2021. Detecting Harmful Memes and Their Targets. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 2783–2796, Online. Association for Computational Linguistics.