Abstract
The spread of fake news, propaganda, misinformation, disinformation, and harmful content online raised concerns among social mediaplatforms, government agencies, policymakers, and society as a whole. This is because such harmful or abusive content leads to several consequences to people such as physical, emotional, relational, and financial. Among different harmful content trolling-based online content is one of them, where the idea is to post a message that is provocative, offensive, or menacing with an intent to mislead the audience. The content can be textual, visual, a combination of both, or a meme. In this study, we provide a comparative analysis of troll-based memes classification using the textual, visual, and multimodal content. We report several interesting findings in terms of code-mixed text, multimodal setting, and combining an additional dataset, which shows improvements over the majority baseline.- Anthology ID:
- 2022.dravidianlangtech-1.13
- Volume:
- Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- DravidianLangTech
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 79–85
- Language:
- URL:
- https://aclanthology.org/2022.dravidianlangtech-1.13
- DOI:
- 10.18653/v1/2022.dravidianlangtech-1.13
- Cite (ACL):
- Rabindra Nath Nandi, Firoj Alam, and Preslav Nakov. 2022. TeamX@DravidianLangTech-ACL2022: A Comparative Analysis for Troll-Based Meme Classification. In Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages, pages 79–85, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- TeamX@DravidianLangTech-ACL2022: A Comparative Analysis for Troll-Based Meme Classification (Nandi et al., DravidianLangTech 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.dravidianlangtech-1.13.pdf
- Data
- Hateful Memes, Hateful Memes Challenge