mlsanityData to 14 Oct 2026

A Survey on Automatic Online Hate Speech Detection in Low-Resource Languages

arXiv 2411.19017 ↗First posted 28 Nov 2024cs.CL cs.LGcited by 2

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Abstract (arXiv)

The expanding influence of social media platforms over the past decade has impacted the way people communicate. The level of obscurity provided by social media and easy accessibility of the internet has facilitated the spread of hate speech. The terms and expressions related to hate speech gets updated with changing times which poses an obstacle to policy-makers and researchers in case of hate speech identification. With growing number of individuals using their native languages to communicate with each other, hate speech in these low-resource languages are also growing. Although, there is awareness about the English-related approaches, much attention have not been provided to these low-resource languages due to lack of datasets and online available data. This article provides a detailed survey of hate speech detection in low-resource languages around the world with details of available datasets, features utilized and techniques used. This survey further discusses the prevailing surveys, overlapping concepts related to hate speech, research challenges and opportunities.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 52 of its 150 references that are in the map. First posted on arXiv 28 Nov 2024.

Builds on

52 of 150 references.

References · 52

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38 more
  1. 2020ETHOS: an Online Hate Speech Detection Datasetcited by 66
  2. 2018Spread of hate speech in online social mediacited by 59
  3. 2020Offensive Language Identification in Greekcited by 50
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Citing papers per year: 2026: 2.

2 papers.

  1. 2026Boosting Accuracy and Interpretability in Multilingual Hate Speech Detection Through Layer Freezing and Explainable AIcited by 0
  2. 2026HateXScore: A Metric Suite for Evaluating Reasoning Quality in Hate Speech Explanationscited by 0