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2022 ACL ACL 2022

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

Abstract

AbstractHarmful 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).

🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
🐣 Hot Topic Early Bird — harmful content detection
🐝 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 Artificial Intelligence > Core AI > Computer Vision Deep Learning > Learning Types > Multimodal Learning

Keywords

computer vision attention mechanism data augmentation multimodal learning meme classification multimodal classification harmful content detection entity detection
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