TeamX@DravidianLangTech-ACL2022: A Comparative Analysis for Troll-Based Meme Classification
This work addresses the problem of detecting harmful online content for social media platforms and society, but it is incremental as it builds on existing classification methods.
The study tackled troll-based meme classification by comparing textual, visual, and multimodal approaches, finding improvements over a majority baseline through methods like code-mixed text and dataset combination.
The spread of fake news, propaganda, misinformation, disinformation, and harmful content online raised concerns among social media platforms, 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 \textit{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.