2022/05/09 by Rabindra Nath Nandi, Nandi, Rabindra Nath, Firoj Alam +3
Computer Science · Social Sciences · #68T50 #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #I.2.7 #Misinformation and Its Impacts #Multimedia (cs.MM) #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2205.04404
openalex publication_date 2022/05/09 · openalex created_date 2022/05/22 · openalex updated_date 2026/07/28
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 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.