FAKE MEDIA DETECTION BASED ON SOCIAL MEDIA USING MACHINE LEARNING

FAKE MEDIA DETECTION BASED ON SOCIAL MEDIA USING MACHINE LEARNING

Dr.M.Manikandan, M.Arjun , S.Siva, P.Ramesh ,M.Manickaraj, K.Gunasekar

ABSTRACT

The proposed solution for detecting false news involves a combination of Natural Language Processing (NLP) techniques, Reinforcement Learning (RL), and blockchain technology. The process begins with the collection of a comprehensive dataset of news articles and their associated metadata, followed by NLP-based pre-processing to clean and tokenize the text. Relevant features, such as word frequencies and readability, are then extracted and used to train an RL agent. The agent is trained to distinguish between true and false news using a reward and punishment system for learning. Once trained, the RL agent can classify new articles as true or false based on their extracted features. Although the potential role of blockchain technology is mentioned, further elaboration is required. This innovative approach is aimed at combating the dissemination of false information and misinformation in the digital news.

Keywords: Natural Language Processing (NLP), Block chain, Fake Media