Detecting the Role of an Entity in Harmful Memes: Techniques and their Limitations

Rabindra Nath Nandi, Firoj Alam, Preslav Nakov


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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.constraint-1.6.pdf
Video:
 https://aclanthology.org/2022.constraint-1.6.mp4
Code
 robi56/harmful_memes_block_fusion
Data
Hateful MemesHateful Memes Challenge