Paper Key : IRJ************966
Author: Mrunali Kate ,Anushka Amale ,Shreya Ramatkar ,Kajal Yerone ,Vedanti Kadu ,Vijay Gulhane
Date Published: 23 Mar 2025
Abstract
In the digital age, social media platforms serve as vital communication channels but are also susceptible to the dissemination of malicious content, including hate speech, misinformation, and cyberbullying. This paper presents a novel detection system designed to identify and classify malicious posts across various social media platforms. Utilizing advanced machine learning algorithms, natural language processing (NLP) techniques, and a comprehensive dataset of labeled posts, the system effectively analyzes text features, user behavior, and contextual information to detect harmful content in real-time. The proposed system achieves high accuracy and low false-positive rates, demonstrating its potential as a reliable tool for enhancing user safety and promoting a healthier online environment.
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