An Early Detection of Threads and Fake news in Social Network using Machine Learning Algorithm

Abstract

Social media platforms have come into existence and have grown exponentially, which has changed the way information is created and shared, and now allows information to spread quickly in interconnected circles online communities. This has made information more readily available but has also allowed the spread of incorrect and fabricated information on a mass scale, with serious social, political and economic consequences. Identifying fake news in the early stages of dissemination is still difficult, due to the lack of evidence of propagation, the dynamic nature of user interactions and lack of annotated data. Todefeat the existing problems, presentproposed method suggests an intelligent graph-based automatic framework which takes advantage of the structural feature of social network and the semantic relationship of news content. The proposed approach begins by comprehensive data preprocessing, which encompasses text normalization, noise filtering, building a graph of the text, and extracting features from both content and user profiles, as well as interaction patterns. Processed data is then encoded as attributed graphs, and the learning model is able to learn the complex relationship between user, post and propagation path of information. To create highly discriminative graph representation, a contrastive representation learning strategy is added to maximize the similarity of the semantically consistent samples and disambiguate misleading information from authentic news. These learned representations are then fed into a supervised algorithm based fake news identification model (XGBoost) for better prediction of early fake news identification. It is tested against the proposed model using a set of benchmark social media data sets and a comprehensive set of performance measures. Experimental analysis is expected to prove that the proposed framework has better detection accuracy, good generalization ability in various news domains, better anti-sparse propagation ability, and lower false alarm rate than the traditional text information and network information detection methods. The proposed framework provides a scalable and reliable approach to early detection of misinformation, which can help promote the integrity of online social media ecosystems and ensure the dissemination of trustworthy information.