A Deep Learning Model for Detecting Bullying Comments on Online Social Media

Authors

  • Renetha J B, Bhagya J, Deepthi P S

Keywords:

Hate speech, bully, word embeddings, accuracy

Abstract

Youth dominate the online world today and the vast majority access social networks. Around the world, cyberbullying is rampant on social media sites and it has become a serious issue for people of all age groups. The bullying content detection by analyzing textual data in social media dataset is one of the most important parts of this work. The use of Deep Learning in Natural Language Processing has become very prevalent for handling the problem of cyberbullying. A large real-world Twitter dataset is collected for cyberbullying analysis. This work aims to analyze cyberbullying across the social media platform using a deep learning model Long Short-Term Memory Recurrent Neural Network or LSTM RNN and to evaluate its performance. The cyberbullying analysis on Twitter dataset using LSTM RNN gives an accuracy of 86%.

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Published

26.03.2024

How to Cite

Renetha J B, Bhagya J, Deepthi P S. (2024). A Deep Learning Model for Detecting Bullying Comments on Online Social Media. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 827–833. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5479

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

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