Hate Speech Detection Using Machine Learning: A Survey

Authors

  • Seble, H., Cyber Security Research Division, Data Science, Information Network Security Administration (INSA), Addis Ababa, Ethiopia
  • Muluken, S., Cyber Security Research Division, Data Science, Information Network Security Administration (INSA), Addis Ababa, Ethiopia
  • Edemealem Kingawa Information Network Security Administration
  • Kafte, T., Cyber Security Research Division, Data Science, Information Network Security Administration (INSA), Addis Ababa, Ethiopia
  • Terefe, F., Cyber Security Research Division, Data Science, Information Network Security Administration (INSA), Addis Ababa, Ethiopia
  • Mekashaw, G., Cyber Security Research Division, Data Science, Information Network Security Administration (INSA), Addis Ababa, Ethiopia
  • Abiyot, B. Cyber Security Research Division, Data Science, Information Network Security Administration (INSA), Addis Ababa, Ethiopia
  • Senait, T. Cyber Security Research Division, Data Science, Information Network Security Administration (INSA), Addis Ababa, Ethiopia

Keywords:

Afaan Oromo Hate speech detection, Amharic Hate speech, Deep learning approach, Hate speech review, Deep Neural Network

Abstract

Currently hate speech is a growing challenge for society, individuals, policymakers, and researchers, as social media platforms make it easy to anonymously create and grow online friends and followers, and provide an online forum for debate about specific issues of community life, culture, politics, and others. Despite, research on identifying and detecting hate speech is not satisfactory performance, and this is why future research on this issue is constantly called for. This paper provides a systematic review of literature in this field, with a focus on approaches like word embedding techniques, machine learning, deep learning technologies, hate speech terminology, and other state-of-the-art technologies with challenges. In this paper, we have made a systematic review of the last 6 years of literature from Research Gate and Google Scholar. Furthermore, limitations, along with algorithm selection and use challenges, data collection, and cleaning challenges, and future research directions are discussed in detail.

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Published

2023-09-30

Issue

Vol. 17 No. 1 (2023): Academy Journal of Science and Engineering (AJSE)

Section

Review

License

Copyright (c) 2023 Seble, H.,, Muluken, S.,, Edemealem Kingawa, Kafte, T.,, Terefe, F.,, Mekashaw, G.,, Abiyot, B., Senait, T.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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