This system utilizes Long Short-Term Memory (LSTM) networks and Bi-GRUs to analyze the sequential context of social media comments. By training on datasets of labeled toxic speech, the model identifies various forms of cyberbullying, including harassment and hate speech. The system is designed to detect subtle aggression and derogatory context rather than just specific keywords. Practical applications include automated content moderation for social platforms and parental control tools that provide real-time alerts on harmful digital interactions.
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AI / ML Projects, Fraud Detection & Cybersecurity, Security Projects
Intelligent Cyberbullying Detection on Social Media Using Deep Learning
Original price was: ₹6,999.00.₹3,999.00Current price is: ₹3,999.00. inc GSTs*
The scale of digital interaction makes manual moderation of cyberbullying impossible. Standard keyword filters are easily bypassed by slang or sarcasm, requiring deep learning models capable of understanding context and aggressive intent within toxic online behavior to protect vulnerable users.

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