Twitter toxic comment identification in digital media and advertising using NLP and optimized classifiers

Jelena Gajic; Lazar Drazeta; Lepa Babic; Jelena Kaljevic; Dejan Jovanovic; Luka Jovanovic

doi:10.2991/978-94-6463-482-2_12

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Twitter toxic comment identification in digital media and advertising using NLP and optimized classifiers

Authors

Jelena Gajic¹, Lazar Drazeta¹, Lepa Babic¹, Jelena Kaljevic¹, Dejan Jovanovic², Luka Jovanovic¹^{, *}

¹Singidunum University, Belgrade, Serbia

²College of Academic Studies “Dositej”, Belgrade, Serbia

^*Corresponding author. Email: ljovanovic@singidunum.ac.rs

Corresponding Author

Luka Jovanovic

Available Online 23 August 2024.

DOI: 10.2991/978-94-6463-482-2_12 How to use a DOI?
Keywords: Cyberbullying; Twitter; Toxic comments; Machine learning; XGBoost; Swarm intelligence; BOA metaheuristics
Abstract: Cyberbullying is a form of harassing, intimidating and harming other people through electronic media like social networks or messaging platforms. Typical forms of cyberbullying include messages containing harmful text, photos or videos that will embarrass the target, and excluding the individual from groups and chats. Unfortunatelly, it may lead to sincere psychological problems of the target, including disorders like depression, anxious behavior, lack of self-esteem, or even worse, suicidal thoughts and self-hurting. The research presented herein proposes a hybrid approach that includes natural language processing and machine learning XGBoost model optimized by an altered variant of Botox optimization metaheuristics for classification of toxic tweets on a real-world dataset. The experimental results have shown considerable prospect of application of machine learning models in solving this serious and important problem.
Copyright: © 2024 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 2nd International Conference on Innovation in Information Technology and Business (ICIITB 2024)
Series: Advances in Computer Science Research
Publication Date: 23 August 2024
ISBN: 978-94-6463-482-2
ISSN: 2352-538X
DOI: 10.2991/978-94-6463-482-2_12 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.

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ris enw bib

TY  - CONF
AU  - Jelena Gajic
AU  - Lazar Drazeta
AU  - Lepa Babic
AU  - Jelena Kaljevic
AU  - Dejan Jovanovic
AU  - Luka Jovanovic
PY  - 2024
DA  - 2024/08/23
TI  - Twitter toxic comment identification in digital media and advertising using NLP and optimized classifiers
BT  - Proceedings of the 2nd International Conference on Innovation in Information Technology and Business (ICIITB 2024)
PB  - Atlantis Press
SP  - 171
EP  - 187
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-482-2_12
DO  - 10.2991/978-94-6463-482-2_12
ID  - Gajic2024
ER  -

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