Developing a Multilingual Annotated Corpus of Misogyny and Aggression

The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse

In this paper, we discuss the development of a multilingual dataset annotated with a hierarchical, fine-grained tagset marking different types of aggression and the "context" in which they occur. The context, here, is defined by the conversational thread in which a specific comment occurs and also the "type" of discursive role that the comment is performing with respect to the previous comment. The initial dataset, being discussed here (and made available as part of the ComMA@ICON shared task), consists of a total 15,000 annotated comments in four languages - Meitei, Bangla, Hindi, and Indian …

An Empirical Evaluation of Text Representation Schemes on Multilingual Social Web to Filter the Textual Aggression

This paper attempt to study the effectiveness of text representation schemes on two tasks namely: User Aggression and Fact Detection from the social media contents. In User Aggression detection, The aim is to identify the level of aggression from the contents generated in the Social media and written in the English, Devanagari Hindi and Romanized Hindi. Aggression levels are categorized into three predefined classes namely: `Non-aggressive`, `Overtly Aggressive`, and `Covertly Aggressive`. During the disaster-related incident, Social media like, Twitter is flooded with millions of posts. In su…

使用开源文本嵌入检测德语在线报纸评论中的性别歧视（GDA团队，GermEval2024共享任务1：GerMS-Detect，子任务1和2，封闭赛道）

Detecting Sexism in German Online Newspaper Comments with Open-Source Text Embeddings (Team GDA, GermEval2024 Shared Task 1: GerMS-Detect, Subtasks 1 and 2, Closed Track)

本文评估单语与多语开源文本嵌入，用于检测德语报纸评论中的性别歧视，并在GermEval 2024 GerMS-Detect两个子任务中取得第4和第2名。