# Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages

arXiv: https://arxiv.org/abs/2204.12543 · HTML: https://mlsanity.com/paper/2204.12543/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 26 Apr 2022.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [Multilingual Offensive Language Identification with Cross-lingual Embeddings](https://mlsanity.com/paper/2010.05324/) (2020)
- [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)
- [Hate Speech detection in the Bengali language: A dataset and its baseline evaluation](https://mlsanity.com/paper/2012.09686/) (2020)
- [Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi](https://mlsanity.com/paper/2109.03552/) (2021)
- [The Effect of Extremist Violence on Hateful Speech Online](https://mlsanity.com/paper/1804.05704/) (2018)
- [You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insights](https://mlsanity.com/paper/2108.00524/) (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)
- [Hate Speech Detection in Roman Urdu](https://mlsanity.com/paper/2108.02830/) (2021)
- [A Data Bootstrapping Recipe for Low Resource Multilingual Relation Classification](https://mlsanity.com/paper/2110.09570/) (2021)
- [Abusive and Threatening Language Detection in Urdu using Boosting based and BERT based models: A Comparative Approach](https://mlsanity.com/paper/2111.14830/) (2021)

## Built on by (most cited first, 23 of 23)

- [HateMM: A Multi-Modal Dataset for Hate Video Classification](https://mlsanity.com/paper/2305.03915/) (2023)
- [HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter](https://mlsanity.com/paper/2411.15462/) (2024)
- [ToxVidLM: A Multimodal Framework for Toxicity Detection in Code-Mixed Videos](https://mlsanity.com/paper/2405.20628/) (2024)
- [Uncovering Political Hate Speech During Indian Election Campaign: A New Low-Resource Dataset and Baselines](https://mlsanity.com/paper/2306.14764/) (2023)
- [Evaluating ChatGPT's Performance for Multilingual and Emoji-based Hate Speech Detection](https://mlsanity.com/paper/2305.13276/) (2023)
- [Low-Resource Counterspeech Generation for Indic Languages: The Case of Bengali and Hindi](https://mlsanity.com/paper/2402.07262/) (2024)
- [Which one is more toxic? Findings from Jigsaw Rate Severity of Toxic Comments](https://mlsanity.com/paper/2206.13284/) (2022)
- [Breaking Boundaries: Investigating the Effects of Model Editing on Cross-linguistic Performance](https://mlsanity.com/paper/2406.11139/) (2024)
- [Hate Speech Detection in Limited Data Contexts using Synthetic Data Generation](https://mlsanity.com/paper/2310.02876/) (2023)
- [NLPineers@ NLU of Devanagari Script Languages 2025: Hate Speech Detection using Ensembling of BERT-based models](https://mlsanity.com/paper/2412.08163/) (2024)
- [Qualitative Analysis of a Graph Transformer Approach to Addressing Hate Speech: Adapting to Dynamically Changing Content](https://mlsanity.com/paper/2301.10871/) (2023)
- [My Boli: Code-mixed Marathi-English Corpora, Pretrained Language Models and Evaluation Benchmarks](https://mlsanity.com/paper/2306.14030/) (2023)
- [Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges](https://mlsanity.com/paper/2401.09244/) (2024)
- [A Survey on Automatic Online Hate Speech Detection in Low-Resource Languages](https://mlsanity.com/paper/2411.19017/) (2024)
- [A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in English](https://mlsanity.com/paper/2504.08609/) (2025)
- [Are you sure? Measuring models bias in content moderation through uncertainty](https://mlsanity.com/paper/2509.22699/) (2025)
- [RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers](https://mlsanity.com/paper/2310.14261/) (2023)
- [Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection](https://mlsanity.com/paper/2311.02025/) (2023)
- [PACO: Provocation Involving Action, Culture, and Oppression](https://mlsanity.com/paper/2303.12808/) (2023)
- [The Unappreciated Role of Intent in Algorithmic Moderation of Social Media Content](https://mlsanity.com/paper/2405.11030/) (2024)
- [Mitigating Extrinsic Gender Bias for Bangla Classification Tasks](https://mlsanity.com/paper/2411.10636/) (2024)
- [Decoding Memes: Benchmarking Narrative Role Classification across Multilingual and Multimodal Models](https://mlsanity.com/paper/2506.23122/) (2025)
- [Conditional Reliability of Toxicity Signals for Multilingual and Code-Mixed Abuse Detection](https://mlsanity.com/paper/2607.15861/) (2026)

## Abstract (arXiv)

Abusive language is a growing concern in many social media platforms. Repeated exposure to abusive speech has created physiological effects on the target users. Thus, the problem of abusive language should be addressed in all forms for online peace and safety. While extensive research exists in abusive speech detection, most studies focus on English. Recently, many smearing incidents have occurred in India, which provoked diverse forms of abusive speech in online space in various languages based on the geographic location. Therefore it is essential to deal with such malicious content. In this paper, to bridge the gap, we demonstrate a large-scale analysis of multilingual abusive speech in Indic languages. We examine different interlingual transfer mechanisms and observe the performance of various multilingual models for abusive speech detection for eight different Indic languages. We also experiment to show how robust these models are on adversarial attacks. Finally, we conduct an in-depth error analysis by looking into the models' misclassified posts across various settings. We have made our code and models public for other researchers.