Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Lapina, Maria; Anita, Mary; Bagautdinova, Alina; Lapin, Vitalii; Rudenko, Marina Title A name given to the resource Machine Learning Research Methods for Identifying Inaccurate Content Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1295;pp.193-201 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-3311-1_16" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-3311-1_16</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105005482773?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105005482773?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource 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 Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981963310-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online