Automated Detection of Cyber Bullying
Year: 2024
Type: article
Abstract: It is the most popular channels for communication in social media. But few people use these platforms for evil intent, and "cyberbullying" is a particularly common occurrence. Cyberbullying is particularly common among young people and entails using technological methods to harass or injure others. Therefore,the aimofthisstudy isto suggest a deeplearning algorithm-based model for identifying cyberbullying. The Long Short-Term Memory (LSTM) approach was used to forecast bullying incidents using three datasets from Facebook, Instagram, and Twitter. The outcomes showed thatan efficient model for identifying cyberbullying has been developed, resolving issues with earliermethods of cyberbullying detection. For the Twitter, Instagram, and Facebook datasets, the model's accuracies were roughly 96.64%, 94.49% and 91.26%, respectively.
Authors: Nitya Shree R, Divyashree S, Neha G, Pooja Kulkarni, Poornima K
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949707407
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