Machine Learning Research Methods for Identifying Inaccurate Content
Artificial intelligence; Authenticity; Data analysis; Deception recognition; Deep learning; Detection; Facial expression; Fake news; Lie detection; Machine learning; Machine learning; Neural networks; Social networks; Technology
Social media, especially when disseminating news, is a valuable information resource. The paper presents methods for detecting fake news, comparing their effectiveness, identifying existing problems, and describes the vectors of further development of this research area. The paper begins with a description of the relevance of the Fake News problem, which clearly describes the negative impact of false news on all spheres of human life. The following is a description of methods for detecting false news, starting from the usual rules of text analysis and ending with complex ML algorithms. In this paper, a comparative analysis of detection methods is carried out, which is based on criteria of efficiency and accuracy. The author identifies the main problems of existing methods related to data quality, changing Fake News formats and the difficulties of automatically determining the reliability of information. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Lapina, Maria; Anita, Mary; Bagautdinova, Alina; Lapin, Vitalii; Rudenko, Marina
Lecture Notes in Networks and Systems;Volume;1295;pp.193-201
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-96-3311-1_16 https://www.scopus.com/pages/publications/105005482773?origin=resultslist
Lapina M., North-Caucasus Federal University, Stavropol, Russian Federation; Anita M., Christ University, Bangalore, India; Bagautdinova A., North-Caucasus Federal University, Stavropol, Russian Federation; Lapin V., North-Caucasus Federal University, Stavropol, Russian Federation; Rudenko M., V. I. Vernadsky Crimean Federal University, Simferopol, Russian Federation