Authors : Vishrutha S. N.; Dr. J. Vimala Devi

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/22yp4nn8

Scribd : https://tinyurl.com/4w7p2kau

DOI : https://doi.org/10.38124/ijisrt/25nov1206

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In today’s online world, cyberbullying is an escalating problem that can trigger deep emotional pain and isolation from others. There is an increasing necessity to regulate content shared on online platforms. Cyberbullying material can spread quickly across online platforms and may remain visible for a long time, sometimes without ever being removed. Such persistent exposure to harmful content can take a serious toll on the mental, emotional, and psychological health of young people. In extreme situations, ongoing harassment in digital spaces can contribute to thoughts of self-harm or suicide. The proposed system utilizes Long Short-Term Memory (LSTM) networks to assess user-generated text and compute a bullying likelihood for every sentence. This system monitors user activity in real time and decreases a reputation score whenever harmful or bullying content is identified. Once the score falls below a set limit, the user is automatically prevented from continuing interactions on the platform. By merging deep learning with a reputation- based penalty method, the design works to curb cyberbullying while ensuring moderation remains both fair and proactive. The approach is adaptable, efficient, and capable of supporting healthier online spaces.

Keywords : Cyberbullying; Deep Learning; LSTM.

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