Machine Learning: a Tool for Bystanders to Address Cyberbullying

EasyChair Preprint 13038

Abstract

Cyberbullying has become a pervasive issue in today's digital age, affecting individuals of all ages across various online platforms. Bystanders, individuals who witness cyberbullying incidents but are not directly involved, often feel powerless to intervene due to uncertainty about how to effectively address such situations. Machine learning (ML) offers a promising solution to empower bystanders to combat cyberbullying. By leveraging ML algorithms, social media platforms can detect and flag instances of cyberbullying in real-time, alerting bystanders and enabling them to take immediate action. Additionally, ML can be used to develop personalized intervention strategies based on the context of each cyberbullying incident, providing bystanders with actionable steps to support victims and deter further harassment. This paper explores the potential of ML as a tool for bystanders to address cyberbullying, highlighting its role in creating a safer and more inclusive online environment.

Keyphrases: AI, cyberspace, machine learning

@booklet{EasyChair:13038,
  author    = {Abil Robert},
  title     = {Machine Learning: a Tool for Bystanders to Address Cyberbullying},
  howpublished = {EasyChair Preprint 13038},
  year      = {EasyChair, 2024}}