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2022 ACL ACL 2022

TeamX@DravidianLangTech-ACL2022: A Comparative Analysis for Troll-Based Meme Classification

Abstract

AbstractThe 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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Rabindra Nath Nandi , Firoj Alam , Preslav Nakov

Topics

Artificial Intelligence > Core AI > Multimodal Learning Machine Learning > Core Methods > Classification Natural Language Processing > Applications > Text Classification Deep Learning > Learning Types > Multi-Modal Learning Deep Learning > Learning Types > Classification

Keywords

text classification multimodal learning meme classification code-mixed text multimodal classification hate speech detection comparative analysis troll meme classification
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