An Investigation into the Identification of False News via Machine Learning Classification Methods

Abstract

This research endeavors to employ machine learning methodologies for the detection of false news. The study encompasses an introduction, a historical overview, and a definition of the problem of false news identification. Identifying False Information has become increasingly challenging in the contemporary landscape. A primary challenge lies in the early detection of such news, compounded by the scarcity of labeled data necessary for training detection models. Subsequently, the paper discusses False Information detection, addressing its impacts, diverse forms of news data, categories of False Information, types of False Information, and the inherent difficulties in its detection. Furthermore, the exploration extends to detection techniques, including knowledge-based, social context-based, and context-based approaches. The classification of False Information across. The task of discerning truth from falsehood in news articles is significantly aided by the application of ML and DL algorithms, enabling the automation of news classification across diverse categories and the filtering of detrimental or inaccurate information.