Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)

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A Survey on Detecting Hate Speech and Misogyny in Native and Code-Mixed Texts in Social Media

Authors
S. Karishma1, *, V. Akila1
1Department of Computer Science and Engineering, Puducherry Technological University, Puducherry, 605014, India
*Corresponding author. Email: 16karish@ptuniv.edu.in
Corresponding Author
S. Karishma
Available Online 31 March 2026.
DOI
10.2991/978-94-6239-616-6_48How to use a DOI?
Keywords
Code-mixed; Hate Speech; Misogyny; Multilingual; Social Media
Abstract

The wide usage of social media has transformed global communication, but also amplified the dissemination of hate speech and misogynistic content that is prone to severe threats to online safety and societal harmony. The usage of code-mixed texts, where users mix English with regional languages, has increased in multilingual countries like India. The complex semantics, phonological variances, and sociolinguistic indicators included in such multilingual expressions are mostly failed by traditional hate speech detection methods. This review article presents a complete insvestigation of existing approaches for detecting hate speech and misogyny in native and code-mixed languages. The study explores various embedding approaches, such as static and contextual transformer-based multilingual embeddings and recurrent neural networks. Attention mechanisms are used for better semantic understanding in low-resource languages, which is also discussed. This study promotes the importance of developing robust, inclusive, and linguistically adaptive models for effective detection of hate and misogyny in multilingual online ecosystems.

Copyright
© 2026 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 International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
31 March 2026
ISBN
978-94-6239-616-6
ISSN
1951-6851
DOI
10.2991/978-94-6239-616-6_48How to use a DOI?
Copyright
© 2026 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.

Cite this article

risenwbib
TY  - CONF
AU  - S. Karishma
AU  - V. Akila
PY  - 2026
DA  - 2026/03/31
TI  - A Survey on Detecting Hate Speech and Misogyny in Native and Code-Mixed Texts in Social Media
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 641
EP  - 650
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_48
DO  - 10.2991/978-94-6239-616-6_48
ID  - Karishma2026
ER  -