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Hate speech, toxicity detection in online social media: a recent survey of state of the art and opportunities

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Abstract

Information and communication technology has evolved dramatically, and now the majority of people are using internet and sharing their opinion more openly, which has led to the creation, collection and circulation of hate speech over multiple platforms. The anonymity and movability given by these social media platforms allow people to hide themselves behind a screen and spread the hate effortlessly. Online hate speech (OHS) recognition can play a vital role in stopping such activities and can thus restore the position of public platforms as the open marketplace of ideas. To study hate speech detection in social media, we surveyed the related available datasets on the web-based platform. We further analyzed approximately 200 research papers indexed in the different journals from 2010 to 2022. The papers were divided into various sections and approaches used in OHS detection, i.e., feature selection, traditional machine learning (ML) and deep learning (DL). Based on the selected 111 papers, we found that 44 articles used traditional ML and 35 used DL-based approaches. We concluded that most authors used SVM, Naive Bayes, Decision Tree in ML and CNN, LSTM in the DL approach. This survey contributes by providing a systematic approach to help researchers identify a new research direction in online hate speech.

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Data availability statements

Data generated or analyzed during this study are included in this published article.

Notes

  1. https://hatespeechdata.com/.

  2. https://semeval.github.io/SemEval2021/tasks.html.

  3. https://hasocfire.github.io/hasoc/2020/index.html.

  4. https://swisstext-and-konvens-2020.org/shared-tasks/.

  5. https://sites.google.com/view/trac2/live?authuser=0.

  6. https://ai.Facebook.com/blog/hateful-memes-challenge-and-data-set/.

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    Anjum & Rahul Katarya

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Anjum, Katarya, R. Hate speech, toxicity detection in online social media: a recent survey of state of the art and opportunities. Int. J. Inf. Secur. 23, 577–608 (2024). https://doi.org/10.1007/s10207-023-00755-2

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