Detection of Cyberbullying Using Machine Learning and Deep Learning Algorithms

Authors

  • Dipali Pacharane  Nutan Maharashtra Institute of Engineering and Technology, Talegaon(D), Pune, Maharashtra, India
  • Rutuja Pujari  Nutan Maharashtra Institute of Engineering and Technology, Talegaon(D), Pune, Maharashtra, India
  • Niam Sandbhor  Nutan Maharashtra Institute of Engineering and Technology, Talegaon(D), Pune, Maharashtra, India
  • Sharvari Shinde  Nutan Maharashtra Institute of Engineering and Technology, Talegaon(D), Pune, Maharashtra, India
  • Dheeraj Patil  Nutan Maharashtra Institute of Engineering and Technology, Talegaon(D), Pune, Maharashtra, India
  • Chandrakant Kokane  Nutan Maharashtra Institute of Engineering and Technology, Talegaon(D), Pune, Maharashtra, India

Keywords:

Cyberbullying, Machine Learning, Natural Language Processing, Deep Learning

Abstract

Cyberbullying, a pervasive issue in the digital age, poses a significant threat to the well-being of individuals online. This report delves into the critical role of machine learning in addressing this complex problem. Cyberbullying involves the use of electronic communication for abusive, threatening, or intimidating behavior, causing emotional distress and harm to victims. The objective of this research is to develop and implement machine learning models that can automatically detect and flag instances of cyberbullying in digital text content. The report outlines a comprehensive approach, including data collection, preprocessing, model selection, training, and evaluation. Machine learning models are trained to recognize patterns and linguistic cues associated with cyberbullying, with post-processing and continuous monitoring enhancing the detection process. Ethical considerations, privacy, and user education are central to this initiative. Real-world case studies highlight the tangible impact of machine learning in reducing the prevalence of abusive online behavior. Nevertheless, challenges such as bias and fairness persist, demanding ongoing vigilance and research. As we forge ahead, the potential for emerging technologies and interdisciplinary collaboration offers promising avenues for more effective cyberbullying detection. This report underscores the significance of machine learning in promoting a safer and more compassionate online society where individuals can connect and communicate without fear.

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International Journal of Scientific Research in Science and Technology

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Published

2023-12-30

Issue

Volume 10, Issue 6, November-December-2023

Section

Research Articles

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How to Cite

[1]
Dipali Pacharane, Rutuja Pujari, Niam Sandbhor, Sharvari Shinde, Dheeraj Patil, Chandrakant Kokane, " Detection of Cyberbullying Using Machine Learning and Deep Learning Algorithms, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 10, Issue 6, pp.275-282, November-December-2023.