Summary of A Survey on Automatic Online Hate Speech Detection in Low-resource Languages, by Susmita Das et al.
A Survey on Automatic Online Hate Speech Detection in Low-Resource Languages
by Susmita Das, Arpita Dutta, Kingshuk Roy, Abir Mondal, Arnab Mukhopadhyay
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive survey on hate speech detection in low-resource languages, highlighting the growing concern of online hate speech globally. With increasing online communication in native languages, hate speech detection tools are needed for these languages, but current approaches focus mainly on English. The authors review existing datasets, features, and techniques used for hate speech detection, identifying research challenges and opportunities. The paper aims to provide a detailed overview of the state-of-the-art methods and potential solutions for detecting hate speech in low-resource languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hate speech is a growing problem on social media, where people can easily spread harmful messages. Researchers have been working on ways to identify hate speech, but most efforts focus on English-speaking countries. As more people use their native languages online, hate speech detection tools are needed for these languages too. This paper looks at the current state of hate speech detection in low-resource languages, including what datasets and techniques are being used. |
