论文标题
Teamx@dravidianlangtech-ACL2022：基于巨魔的模因分类的比较分析
TeamX@DravidianLangTech-ACL2022: A Comparative Analysis for Troll-Based Meme Classification
论文作者
论文摘要
假新闻，宣传，错误信息，虚假信息和有害内容在线的传播引起了社交媒体平台，政府机构，政策制定者和整个社会的关注。这是因为这种有害或虐待的内容会给身体，情感，关系和财务带来几种后果。在不同的有害内容\ textit {基于拖钓}的在线内容是其中之一，其中的想法是发布一个挑衅，令人反感或险恶的信息，以误解观众。内容可以是文本，视觉，两者的组合或模因的组合。在这项研究中，我们使用文本，视觉和多模式内容对基于巨魔的模因进行了比较分析。我们报告了几个有趣的发现，以代码混合文本，多模式设置以及组合附加的数据集，显示出比大多数基线的改进。
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.
