Machine Learning Methods for detection of bystanders: A Survey

Bystanders Detection Survey

  • Anamika Gupta SSCBS, University of Delhi
  • Khushboo Thakkar
  • Vibhor Mathur
  • Aman Tiwari
Keywords: Cyberbullying, Bystanders, machine learning

Abstract

The number of users on social media networks is increasing day by day as their popularity increases. The users are sharing their photos, videos, daily life, experiences, views, and status updates on different social networking sites. Social networking sites give great possibilities for young people to interact with others, but they also make them more subject to unpleasant phenomena such as online harassment and abusive language, which leads to cyberbullying. Cyberbullying is a prevalent social problem that inflicts detrimental consequences to the health and safety of victims such as psychological distress, anti-social behavior, and suicide. To minimize the impact of Cyberbullying, the Bystander role is very important. In this paper, a review of the cyberbullying content on the Internet, the classification of cyberbullying categories, classifying author roles (harasser, victim, bystander-defender, bystander-assistant), data sources containing cyberbullying data for research, and machine learning techniques for cyberbullying detection are overviewed. 

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References

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Published
2023-08-12
How to Cite
Gupta, A., Thakkar, K., Mathur, V., & Tiwari, A. (2023). Machine Learning Methods for detection of bystanders: A Survey. International Journal of Advanced Computer Technology, 12(4), 06-14. Retrieved from http://www.ijact.org/index.php/ijact/article/view/134