# Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages arXiv: https://arxiv.org/abs/2112.09301 ยท HTML: https://mlsanity.com/paper/2112.09301/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Builds on 21 of its 86 references that are in the map. - First posted on arXiv 17 Dec 2021. ## Builds on (most cited first, 21 of 21) - [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020) - [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) - [Directions in Abusive Language Training Data: Garbage In, Garbage Out](https://mlsanity.com/paper/2004.01670/) (2020) - [Multilingual Offensive Language Identification with Cross-lingual Embeddings](https://mlsanity.com/paper/2010.05324/) (2020) - [Towards generalisable hate speech detection: a review on obstacles and solutions](https://mlsanity.com/paper/2102.08886/) (2021) - [FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19](https://mlsanity.com/paper/2006.11343/) (2020) - [SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification](https://mlsanity.com/paper/2004.14454/) (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) - [Overview of the HASOC track at FIRE 2020: Hate Speech and Offensive Content Identification in Indo-European Languages](https://mlsanity.com/paper/2108.05927/) (2021) - [An Exploratory Study of COVID-19 Misinformation on Twitter](https://mlsanity.com/paper/2005.05710/) (2020) - [Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi](https://mlsanity.com/paper/2109.03552/) (2021) - [Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News](https://mlsanity.com/paper/2109.12987/) (2021) - [Multilingual Offensive Language Identification for Low-resource Languages](https://mlsanity.com/paper/2105.05996/) (2021) - [Hate and Offensive Speech Detection in Hindi and Marathi](https://mlsanity.com/paper/2110.12200/) (2021) - [Exploring Transformer Based Models to Identify Hate Speech and Offensive Content in English and Indo-Aryan Languages](https://mlsanity.com/paper/2111.13974/) (2021) - [AMUSED: An Annotation Framework of Multi-modal Social Media Data](https://mlsanity.com/paper/2010.00502/) (2020) - [One to rule them all: Towards Joint Indic Language Hate Speech Detection](https://mlsanity.com/paper/2109.13711/) (2021) - [Fine-tuning of Pre-trained Transformers for Hate, Offensive, and Profane Content Detection in English and Marathi](https://mlsanity.com/paper/2110.12687/) (2021) - [Combining Textual Features for the Detection of Hateful and Offensive Language](https://mlsanity.com/paper/2112.04803/) (2021) ## Built on by (most cited first, 17 of 17) - [L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources](https://mlsanity.com/paper/2202.01159/) (2022) - [Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models](https://mlsanity.com/paper/2206.09917/) (2022) - [Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi](https://mlsanity.com/paper/2204.08669/) (2022) - [CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network](https://mlsanity.com/paper/2303.03387/) (2023) - [HaT5: Hate Language Identification using Text-to-Text Transfer Transformer](https://mlsanity.com/paper/2202.05690/) (2022) - [A Modular Taxonomy for Hate Speech Definitions and Its Impact on Zero-Shot LLM Classification Performance](https://mlsanity.com/paper/2506.18576/) (2025) - [Overview of the HASOC Subtrack at FIRE 2022: Offensive Language Identification in Marathi](https://mlsanity.com/paper/2211.10163/) (2022) - [A Review of Challenges in Machine Learning based Automated Hate Speech Detection](https://mlsanity.com/paper/2209.05294/) (2022) - [A Twitter BERT Approach for Offensive Language Detection in Marathi](https://mlsanity.com/paper/2212.10039/) (2022) - [A simple language-agnostic yet very strong baseline system for hate speech and offensive content identification](https://mlsanity.com/paper/2202.02511/) (2022) - [CMLFormer: A Dual Decoder Transformer with Switching Point Learning for Code-Mixed Language Modeling](https://mlsanity.com/paper/2505.12587/) (2025) - [A Feature Extraction based Model for Hate Speech Identification](https://mlsanity.com/paper/2201.04227/) (2022) - [POTATO: exPlainable infOrmation exTrAcTion framewOrk](https://mlsanity.com/paper/2201.13230/) (2022) - [Evaluating the Effectiveness of Natural Language Inference for Hate Speech Detection in Languages with Limited Labeled Data](https://mlsanity.com/paper/2306.03722/) (2023) - [HarmPot: An Annotation Framework for Evaluating Offline Harm Potential of Social Media Text](https://mlsanity.com/paper/2403.11108/) (2024) - [Multilingual Hate Speech and Offensive Content Detection using Modified Cross-entropy Loss](https://mlsanity.com/paper/2202.02635/) (2022) - [Cyberbullying Detection in Hinglish Text Using MURIL and Explainable AI](https://mlsanity.com/paper/2506.16066/) (2025) ## Abstract (arXiv) The widespread of offensive content online such as hate speech poses a growing societal problem. AI tools are necessary for supporting the moderation process at online platforms. For the evaluation of these identification tools, continuous experimentation with data sets in different languages are necessary. The HASOC track (Hate Speech and Offensive Content Identification) is dedicated to develop benchmark data for this purpose. This paper presents the HASOC subtrack for English, Hindi, and Marathi. The data set was assembled from Twitter. This subtrack has two sub-tasks. Task A is a binary classification problem (Hate and Not Offensive) offered for all three languages. Task B is a fine-grained classification problem for three classes (HATE) Hate speech, OFFENSIVE and PROFANITY offered for English and Hindi. Overall, 652 runs were submitted by 65 teams. The performance of the best classification algorithms for task A are F1 measures 0.91, 0.78 and 0.83 for Marathi, Hindi and English, respectively. This overview presents the tasks and the data development as well as the detailed results. The systems submitted to the competition applied a variety of technologies. The best performing algorithms were mainly variants of transformer architectures.