Machine Learning Research Methods for Identifying Inaccurate Content

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 desc...

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Главные авторы:	Lapina, M. A., Лапина, М. А., Lapin, V. A., Лапин, В. А., Bagautdinova, A. R.
Другие авторы:	Багаутдинова, А. Р.
Формат:	Статья
Язык:	English
Опубликовано:	Springer Science and Business Media Deutschland GmbH 2025
Темы:	Artificial intelligence Social networks Authenticity Data analysis Deception recognition Deep learning Facial expression Lie detection Fake news Machine learning Neural networks
Online-ссылка:	https://dspace.ncfu.ru/handle/123456789/30519
Метки:	Добавить метку Нет меток, Требуется 1-ая метка записи!

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spelling	ir-123456789-305192025-06-18T12:13:10Z Machine Learning Research Methods for Identifying Inaccurate Content Lapina, M. A. Лапина, М. А. Lapin, V. A. Лапин, В. А. Bagautdinova, A. R. Багаутдинова, А. Р. Artificial intelligence Social networks Authenticity Data analysis Deception recognition Deep learning Facial expression Lie detection Fake news Machine learning Neural networks 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. 2025-06-18T12:05:59Z 2025-06-18T12:05:59Z 2025 Статья Lapina M., Anita M., Bagautdinova A., Lapin V., Rudenko M. Machine Learning Research Methods for Identifying Inaccurate Content // Lecture Notes in Networks and Systems. - 2025. - 1295. - pp. 193 - 201. - DOI: 10.1007/978-981-96-3311-1_16 https://dspace.ncfu.ru/handle/123456789/30519 en Lecture Notes in Networks and Systems application/pdf Springer Science and Business Media Deutschland GmbH
institution	СКФУ
collection	Репозиторий
language	English
topic	Artificial intelligence Social networks Authenticity Data analysis Deception recognition Deep learning Facial expression Lie detection Fake news Machine learning Neural networks
spellingShingle	Artificial intelligence Social networks Authenticity Data analysis Deception recognition Deep learning Facial expression Lie detection Fake news Machine learning Neural networks Lapina, M. A. Лапина, М. А. Lapin, V. A. Лапин, В. А. Bagautdinova, A. R. Machine Learning Research Methods for Identifying Inaccurate Content
description	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.
author2	Багаутдинова, А. Р.
author_facet	Багаутдинова, А. Р. Lapina, M. A. Лапина, М. А. Lapin, V. A. Лапин, В. А. Bagautdinova, A. R.
format	Статья
author	Lapina, M. A. Лапина, М. А. Lapin, V. A. Лапин, В. А. Bagautdinova, A. R.
author_sort	Lapina, M. A.
title	Machine Learning Research Methods for Identifying Inaccurate Content
title_short	Machine Learning Research Methods for Identifying Inaccurate Content
title_full	Machine Learning Research Methods for Identifying Inaccurate Content
title_fullStr	Machine Learning Research Methods for Identifying Inaccurate Content
title_full_unstemmed	Machine Learning Research Methods for Identifying Inaccurate Content
title_sort	machine learning research methods for identifying inaccurate content
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2025
url	https://dspace.ncfu.ru/handle/123456789/30519
work_keys_str_mv	AT lapinama machinelearningresearchmethodsforidentifyinginaccuratecontent AT lapinama machinelearningresearchmethodsforidentifyinginaccuratecontent AT lapinva machinelearningresearchmethodsforidentifyinginaccuratecontent AT lapinva machinelearningresearchmethodsforidentifyinginaccuratecontent AT bagautdinovaar machinelearningresearchmethodsforidentifyinginaccuratecontent
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Machine Learning Research Methods for Identifying Inaccurate Content

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