# Overview of the HASOC track at FIRE 2020: Hate Speech and Offensive Content Identification in Indo-European Languages

arXiv: https://arxiv.org/abs/2108.05927 · HTML: https://mlsanity.com/paper/2108.05927/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 12 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://mlsanity.com/paper/1905.12516/) (2019)
- [FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19](https://mlsanity.com/paper/2006.11343/) (2020)
- [Offensive Language Identification in Greek](https://mlsanity.com/paper/2003.07459/) (2020)
- [Offensive Language and Hate Speech Detection for Danish](https://mlsanity.com/paper/1908.04531/) (2019)
- [An Exploratory Study of COVID-19 Misinformation on Twitter](https://mlsanity.com/paper/2005.05710/) (2020)
- [AMUSED: An Annotation Framework of Multi-modal Social Media Data](https://mlsanity.com/paper/2010.00502/) (2020)

## Built on by (most cited first, 30 of 38)

- [A New Generation of Perspective API: Efficient Multilingual Character-level Transformers](https://mlsanity.com/paper/2202.11176/) (2022)
- [Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation](https://mlsanity.com/paper/2307.01680/) (2023)
- [Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://mlsanity.com/paper/2204.12543/) (2022)
- [Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases](https://mlsanity.com/paper/2112.07868/) (2021)
- [Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features](https://mlsanity.com/paper/2310.06458/) (2023)
- [Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages](https://mlsanity.com/paper/2112.09301/) (2021)
- [ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information](https://mlsanity.com/paper/2409.14740/) (2024)
- [Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language](https://mlsanity.com/paper/2109.10255/) (2021)
- [Multilingual Content Moderation: A Case Study on Reddit](https://mlsanity.com/paper/2302.09618/) (2023)
- [Cyberbullying Detection for Low-resource Languages and Dialects: Review of the State of the Art](https://mlsanity.com/paper/2308.15745/) (2023)
- [Hidden behind the obvious: misleading keywords and implicitly abusive language on social media](https://mlsanity.com/paper/2205.01374/) (2022)
- [Evaluating ChatGPT's Performance for Multilingual and Emoji-based Hate Speech Detection](https://mlsanity.com/paper/2305.13276/) (2023)
- [Overview of Abusive and Threatening Language Detection in Urdu at FIRE 2021](https://mlsanity.com/paper/2207.06710/) (2022)
- [Overview of the HASOC Subtrack at FIRE 2022: Offensive Language Identification in Marathi](https://mlsanity.com/paper/2211.10163/) (2022)
- [On the Risk of Evidence Pollution for Malicious Social Text Detection in the Era of LLMs](https://mlsanity.com/paper/2410.12600/) (2024)
- [The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse](https://mlsanity.com/paper/2111.10390/) (2021)
- [Human-in-the-Loop Hate Speech Classification in a Multilingual Context](https://mlsanity.com/paper/2212.02108/) (2022)
- [Combining Textual Features for the Detection of Hateful and Offensive Language](https://mlsanity.com/paper/2112.04803/) (2021)
- [TOT: Topology-Aware Optimal Transport For Multimodal Hate Detection](https://mlsanity.com/paper/2303.09314/) (2023)
- [Reducing Target Group Bias in Hate Speech Detectors](https://mlsanity.com/paper/2112.03858/) (2021)
- [POTATO: exPlainable infOrmation exTrAcTion framewOrk](https://mlsanity.com/paper/2201.13230/) (2022)
- [IIITDWD-ShankarB@ Dravidian-CodeMixi-HASOC2021: mBERT based model for identification of offensive content in south Indian languages](https://mlsanity.com/paper/2204.10195/) (2022)
- [Cross-Platform and Cross-Domain Abusive Language Detection with Supervised Contrastive Learning](https://mlsanity.com/paper/2211.06452/) (2022)
- [Sociocultural knowledge is needed for selection of shots in hate speech detection tasks](https://mlsanity.com/paper/2304.01890/) (2023)
- [Evaluating the Effectiveness of Natural Language Inference for Hate Speech Detection in Languages with Limited Labeled Data](https://mlsanity.com/paper/2306.03722/) (2023)
- [Studying Socially Unacceptable Discourse Classification (SUD) through different eyes: "Are we on the same page ?"](https://mlsanity.com/paper/2308.04180/) (2023)
- [HarmPot: An Annotation Framework for Evaluating Offline Harm Potential of Social Media Text](https://mlsanity.com/paper/2403.11108/) (2024)
- [COT: A Generative Approach for Hate Speech Counter-Narratives via Contrastive Optimal Transport](https://mlsanity.com/paper/2406.12304/) (2024)
- [Fine-Tuning Large Language Models with QLoRA for Offensive Language Detection in Roman Urdu-English Code-Mixed Text](https://mlsanity.com/paper/2510.03683/) (2025)
- [X-MuTeST: A Multilingual Benchmark for Explainable Hate Speech Detection and A Novel LLM-consulted Explanation Framework](https://mlsanity.com/paper/2601.03194/) (2026)

## Abstract (arXiv)

With the growth of social media, the spread of hate speech is also increasing rapidly. Social media are widely used in many countries. Also Hate Speech is spreading in these countries. This brings a need for multilingual Hate Speech detection algorithms. Much research in this area is dedicated to English at the moment. The HASOC track intends to provide a platform to develop and optimize Hate Speech detection algorithms for Hindi, German and English. The dataset is collected from a Twitter archive and pre-classified by a machine learning system. HASOC has two sub-task for all three languages: task A is a binary classification problem (Hate and Not Offensive) while task B is a fine-grained classification problem for three classes (HATE) Hate speech, OFFENSIVE and PROFANITY. Overall, 252 runs were submitted by 40 teams. The performance of the best classification algorithms for task A are F1 measures of 0.51, 0.53 and 0.52 for English, Hindi, and German, respectively. For task B, the best classification algorithms achieved F1 measures of 0.26, 0.33 and 0.29 for English, Hindi, and German, respectively. This article presents the tasks and the data development as well as the results. The best performing algorithms were mainly variants of the transformer architecture BERT. However, also other systems were applied with good success