Combating Fake News in Social Media by Enabling Deep Convolutional Neural Network-Based Detection and Mitigation Strategies

The extensive transmission of fake information on social media platforms has grown to be a serious social problem that affects freedom of speech, democracy, and individual safety. The amount of fake information available and the constant evolution of approaches have rendered traditional ways of recognizing and controlling disinformation ineffective. As an outcome, this study offers a thorough framework that uses Deep Convolutional Neural Networks to detect and prevent the spread of false information in social media settings. This methodology relies heavily on the Fake and Real News Dataset that we obtained from Kaggle. This dataset is a broad range of news stories that have been classified as "real" or "fake". The DCNN models are able to differentiate between real and fake material using linguistic, semantic, and contextual aspects owing to the foundational dataset that use to train and fine-tune them. Through the testing and training. the effectiveness of the DCNN-driven methodology in precisely detecting false information on diverse social media channels. This framework focused mitigation plans that limit the spread and influence of false information, taking into account the knowledge gained during the detection stage. These tactics include proactive content filtering, user education programs, and algorithmic changes intended to promote the accuracy and integrity of online information. This research contributes to the ongoing initiatives to prevent misleading information in social media contexts by combining cutting-edge machine learning algorithms with proactive mitigation strategies. The proposed DCNN model achieved an accuracy of 98.98% promote a more robust and reliable digital ecosystem where genuine information predominates.