# Overview of the 2023 ICON Shared Task on Gendered Abuse Detection in Indic Languages

arXiv: https://arxiv.org/abs/2401.03677 · HTML: https://mlsanity.com/paper/2401.03677/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 8 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [Understanding Abuse: A Typology of Abusive Language Detection Subtasks](https://mlsanity.com/paper/1705.09899/) (2017)
- [Aggression-annotated Corpus of Hindi-English Code-mixed Data](https://mlsanity.com/paper/1803.09402/) (2018)
- [Hate Speech detection in the Bengali language: A dataset and its baseline evaluation](https://mlsanity.com/paper/2012.09686/) (2020)
- [Developing a Multilingual Annotated Corpus of Misogyny and Aggression](https://mlsanity.com/paper/2003.07428/) (2020)
- [Hate and Offensive Speech Detection in Hindi and Marathi](https://mlsanity.com/paper/2110.12200/) (2021)
- [Modeling Trolling in Social Media Conversations](https://mlsanity.com/paper/1612.05310/) (2016)
- [The Uli Dataset: An Exercise in Experience Led Annotation of oGBV](https://mlsanity.com/paper/2311.09086/) (2023)

## Abstract (arXiv)

This paper reports the findings of the ICON 2023 on Gendered Abuse Detection in Indic Languages. The shared task deals with the detection of gendered abuse in online text. The shared task was conducted as a part of ICON 2023, based on a novel dataset in Hindi, Tamil and the Indian dialect of English. The participants were given three subtasks with the train dataset consisting of approximately 6500 posts sourced from Twitter. For the test set, approximately 1200 posts were provided. The shared task received a total of 9 registrations. The best F-1 scores are 0.616 for subtask 1, 0.572 for subtask 2 and, 0.616 and 0.582 for subtask 3. The paper contains examples of hateful content owing to its topic.