# L3Cube-MahaHate: A Tweet-based Marathi Hate Speech Detection Dataset and BERT models arXiv: https://arxiv.org/abs/2203.13778 ยท HTML: https://mlsanity.com/paper/2203.13778/ ## Key facts - Cited by 21 papers in the map (data through 14 Oct 2026). - Builds on 15 of its 30 references that are in the map. - First posted on arXiv 25 Mar 2022. ## Builds on (most cited first, 15 of 15) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019) - [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017) - [Detecting Online Hate Speech Using Context Aware Models](https://mlsanity.com/paper/1710.07395/) (2017) - [L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources](https://mlsanity.com/paper/2202.01159/) (2022) - [Hate Speech detection in the Bengali language: A dataset and its baseline evaluation](https://mlsanity.com/paper/2012.09686/) (2020) - [Hostility Detection Dataset in Hindi](https://mlsanity.com/paper/2011.03588/) (2020) - [L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset](https://mlsanity.com/paper/2103.11408/) (2021) - [Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi](https://mlsanity.com/paper/2109.03552/) (2021) - [Deep Learning for Hindi Text Classification: A Comparison](https://mlsanity.com/paper/2001.10340/) (2020) - [Experimental Evaluation of Deep Learning models for Marathi Text Classification](https://mlsanity.com/paper/2101.04899/) (2021) - [Hate and Offensive Speech Detection in Hindi and Marathi](https://mlsanity.com/paper/2110.12200/) (2021) - [Evaluation of Deep Learning Models for Hostility Detection in Hindi Text](https://mlsanity.com/paper/2101.04144/) (2021) - [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) ## Built on by (most cited first, 21 of 21) - ["HOT" ChatGPT: The promise of ChatGPT in detecting and discriminating hateful, offensive, and toxic comments on social media](https://mlsanity.com/paper/2304.10619/) (2023) - [L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages](https://mlsanity.com/paper/2211.11418/) (2022) - [L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library](https://mlsanity.com/paper/2205.14728/) (2022) - [Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi](https://mlsanity.com/paper/2204.08669/) (2022) - [On Limitations of LLM as Annotator for Low Resource Languages](https://mlsanity.com/paper/2411.17637/) (2024) - [BanTH: A Multi-label Hate Speech Detection Dataset for Transliterated Bangla](https://mlsanity.com/paper/2410.13281/) (2024) - [L3Cube-MahaSent-MD: A Multi-domain Marathi Sentiment Analysis Dataset and Transformer Models](https://mlsanity.com/paper/2306.13888/) (2023) - [L3Cube-MahaNews: News-based Short Text and Long Document Classification Datasets in Marathi](https://mlsanity.com/paper/2404.18216/) (2024) - [Chain-of-Translation Prompting (CoTR): A Novel Prompting Technique for Low Resource Languages](https://mlsanity.com/paper/2409.04512/) (2024) - [Spread Love Not Hate: Undermining the Importance of Hateful Pre-training for Hate Speech Detection](https://mlsanity.com/paper/2210.04267/) (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) - [mahaNLP: A Marathi Natural Language Processing Library](https://mlsanity.com/paper/2311.02579/) (2023) - [My Boli: Code-mixed Marathi-English Corpora, Pretrained Language Models and Evaluation Benchmarks](https://mlsanity.com/paper/2306.14030/) (2023) - [MahaSQuAD: Bridging Linguistic Divides in Marathi Question-Answering](https://mlsanity.com/paper/2404.13364/) (2024) - [A Survey on Automatic Online Hate Speech Detection in Low-Resource Languages](https://mlsanity.com/paper/2411.19017/) (2024) - [L3Cube-MahaEmotions: A Marathi Emotion Recognition Dataset with Synthetic Annotations using CoTR prompting and Large Language Models](https://mlsanity.com/paper/2506.00863/) (2025) - [Harnessing Pre-Trained Sentence Transformers for Offensive Language Detection in Indian Languages](https://mlsanity.com/paper/2310.02249/) (2023) - [BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources](https://mlsanity.com/paper/2604.18423/) (2026) - [Leveraging Parameter Efficient Training Methods for Low Resource Text Classification: A Case Study in Marathi](https://mlsanity.com/paper/2408.03172/) (2024) - [Non-Contextual BERT or FastText? A Comparative Analysis](https://mlsanity.com/paper/2411.17661/) (2024) ## Abstract (arXiv) Social media platforms are used by a large number of people prominently to express their thoughts and opinions. However, these platforms have contributed to a substantial amount of hateful and abusive content as well. Therefore, it is important to curb the spread of hate speech on these platforms. In India, Marathi is one of the most popular languages used by a wide audience. In this work, we present L3Cube-MahaHate, the first major Hate Speech Dataset in Marathi. The dataset is curated from Twitter, annotated manually. Our dataset consists of over 25000 distinct tweets labeled into four major classes i.e hate, offensive, profane, and not. We present the approaches used for collecting and annotating the data and the challenges faced during the process. Finally, we present baseline classification results using deep learning models based on CNN, LSTM, and Transformers. We explore mono-lingual and multi-lingual variants of BERT like MahaBERT, IndicBERT, mBERT, and xlm-RoBERTa and show that mono-lingual models perform better than their multi-lingual counterparts. The MahaBERT model provides the best results on L3Cube-MahaHate Corpus. The data and models are available at https://github.com/l3cube-pune/MarathiNLP .