NLP BASED NEWS ANALYSIS SYSTEM

Internet platforms have become breeding grounds for the dissemination of fake news, posing a significant challenge to information quality. The sheer volume of data on the web often overwhelms experts, impeding their ability to rectify inaccurate or false content effectively. To address this issue, a new safeguarding system is imperative. While traditional fake news detection systems focus solely on content-based features, recent models have shifted towards analyzing the social aspects of news dissemination. This paper introduces a novel machine learning model leveraging Natural Language Processing (NLP) techniques to detect fake news. By incorporating both content-based and social features of news articles, the proposed model has demonstrated impressive performance, achieving an average accuracy of 90.62% and an F1 Score of 90.33% on a standard dataset.In essence, this paper presents a pioneering approach to fake news detection, bridging the gap between content-based and social feature analysis. By harnessing NLP techniques, the model effectively evaluates the textual content of news articles while also considering how they are disseminated across social networks. The integration of these two dimensions allows for a more comprehensive understanding of the fake news phenomenon, leading to improved detection accuracy. With its remarkable results, the proposed model offers a promising solution to the pervasive issue of fake news proliferation on online platforms.