Machine Learning Approaches for Detection of Cyberbullying in Code-mixing Languages on Social Media Platforms: A Review

Niral Jadav; Maitri Patel; Brijesh Jajal

doi:10.2991/978-94-6463-978-0_26

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Machine Learning Approaches for Detection of Cyberbullying in Code-mixing Languages on Social Media Platforms: A Review

Authors

Niral Jadav¹^{, *}, Maitri Patel¹, Brijesh Jajal¹

¹School of Computing and Technology, Institute of Advanced Research, Gandhinagar, India

^*Corresponding author. Email: niraljadav@gmail.com

Corresponding Author

Niral Jadav

Available Online 31 December 2025.

DOI: 10.2991/978-94-6463-978-0_26 How to use a DOI?
Keywords: Cyber bullying; social media; Low resource languages; Machine learning algorithms
Abstract: Social networking plays important part of our everyday life. Social media sites are widely used for community development, post sharing, and communication. Growing use of social media may lead to online harassment. The use of the Internet to harass someone is known as unintentional cyberbullying. It is quite challenging to identify cyberbullying in code-mixed language on social media platforms. Since many users prefer to talk in their local tongue with English subtitles, code mixing is common on social media. This article provides a systematic evaluation of the challenges faced by low-resource code-mixing languages, as well as a comparative analysis of existing methods for detecting cyberbullying content. Among the important research needs identified in the report are code-mixing language with restricted database access, regional linguistic differences, and a proposed algorithm for identifying cyberbullying writings on social media platforms.
Copyright: © 2025 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
Series: Advances in Engineering Research
Publication Date: 31 December 2025
ISBN: 978-94-6463-978-0
ISSN: 2352-5401
DOI: 10.2991/978-94-6463-978-0_26 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Niral Jadav
AU  - Maitri Patel
AU  - Brijesh Jajal
PY  - 2025
DA  - 2025/12/31
TI  - Machine Learning Approaches for Detection of Cyberbullying in Code-mixing Languages on Social Media Platforms: A Review
BT  - Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
PB  - Atlantis Press
SP  - 282
EP  - 292
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-978-0_26
DO  - 10.2991/978-94-6463-978-0_26
ID  - Jadav2025
ER  -

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