Online harassment detection on online data science platforms optimized by metaheuristic

Nebojsa Bacanin; Milos Kabiljo; Lepa Babic; Vuk Gajic; Jelena Kaljevic; Milos Dobrojevic

doi:10.2991/978-94-6463-482-2_9

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Online harassment detection on online data science platforms optimized by metaheuristic

Authors

Nebojsa Bacanin¹^{, *}, Milos Kabiljo², Lepa Babic¹, Vuk Gajic¹, Jelena Kaljevic¹, Milos Dobrojevic¹

¹Singidunum University, Belgrade, Serbia

²Department for Information Systems and Technologies, Belgrade Academy for Business and Arts Applied Studies, Belgrade, Serbia

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

Corresponding Author

Nebojsa Bacanin

Available Online 23 August 2024.

DOI: 10.2991/978-94-6463-482-2_9 How to use a DOI?
Keywords: Cyberbullying; Harassment detection; Machine learning; XGBoost; Swarm intelligence; metaheuristics optimization; sine cosine algorithm
Abstract: Cyberbullying denotes one of the recent pervasive problems, mostly found on social networks, that poses a considerable challenge to keep safe and inclusive environment. It can lead to serious psychological problems for the victim. As one of possible responses, artificial intelligence emerged as a powerful option to identify cases of cyberbullying, and it has garnered considerable attention. This paper suggest using a combination of natural language processing, paired with machine learning XGBoost classifier tuned by an altered variant of the sine cosine metaheuristics to classify and identify the cases of cyberbullying in data collected from a variety of social networks including Kaggle, Twitter and Youtube. The obtained simulation outcomes suggest considerable potential of machine learning models to address this 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_9 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  - Nebojsa Bacanin
AU  - Milos Kabiljo
AU  - Lepa Babic
AU  - Vuk Gajic
AU  - Jelena Kaljevic
AU  - Milos Dobrojevic
PY  - 2024
DA  - 2024/08/23
TI  - Online harassment detection on online data science platforms optimized by metaheuristic
BT  - Proceedings of the 2nd International Conference on Innovation in Information Technology and Business (ICIITB 2024)
PB  - Atlantis Press
SP  - 121
EP  - 136
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-482-2_9
DO  - 10.2991/978-94-6463-482-2_9
ID  - Bacanin2024
ER  -

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