Abstract
Harmful or abusive online content has been increasing over time and it has been raising concerns among social media platforms, government agencies, and policymakers. Such harmful or abusive content has a significant negative impact on society such as cyberbullying led to suicides, COVID-19 related rumors led to hundreds of deaths. The content that is posted and shared online can be textual, visual, a combination of both, or a meme. In this paper, we provide our study on detecting the roles of entities in harmful memes, which is part of the CONSTRAINT-2022 shared task. We report the results on the participated system. We further provide a comparative analysis on different experimental settings (i.e., unimodal, multimodal, attention, and augmentation).- Anthology ID:
- 2022.constraint-1.6
- Volume:
- Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Tanmoy Chakraborty, Md. Shad Akhtar, Kai Shu, H. Russell Bernard, Maria Liakata, Preslav Nakov, Aseem Srivastava
- Venue:
- CONSTRAINT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 43–54
- Language:
- URL:
- https://aclanthology.org/2022.constraint-1.6
- DOI:
- 10.18653/v1/2022.constraint-1.6
- Cite (ACL):
- Rabindra Nath Nandi, Firoj Alam, and Preslav Nakov. 2022. Detecting the Role of an Entity in Harmful Memes: Techniques and their Limitations. In Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations, pages 43–54, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Detecting the Role of an Entity in Harmful Memes: Techniques and their Limitations (Nandi et al., CONSTRAINT 2022)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2022.constraint-1.6.pdf
- Code
- robi56/harmful_memes_block_fusion
- Data
- Hateful Memes, Hateful Memes Challenge