title: ÐÐ±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÑÐ°ÑÐ¿ÑÐ¾ÑÑÑÐ°Ð½ÐµÐ½Ð¸Ñ Ð½ÐµÐ´Ð¾ÑÑÐ¾Ð²ÐµÑÐ½Ð¾Ð³Ð¾ Ð½Ð¾Ð²Ð¾ÑÑÐ½Ð¾Ð³Ð¾ ÐºÐ¾Ð½ÑÐµÐ½ÑÐ° Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° ÑÐ¿ÐµÑÐ¸Ð°Ð»Ð¸ÑÑÐ°
titleType: Subtitle
title: Detection of fake news spread using machine learning
Creators: name: ÐÑÑÑÐ½Ð¾Ð²Ð°, ÐÐ½Ð°ÑÑÐ°ÑÐ¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÑÐµÐ¹ÐºÐ¾Ð²ÑÑ Ð½Ð¾Ð²Ð¾ÑÑÐµÐ¹
ÑÐ°ÑÐ¿ÑÐ¾ÑÑÑÐ°Ð½ÐµÐ½Ð¸Ðµ Ð½ÐµÐ´Ð¾ÑÑÐ¾Ð²ÐµÑÐ½Ð¾Ð³Ð¾ ÐºÐ¾Ð½ÑÐµÐ½ÑÐ°
Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐµÐºÑÑÐ¾Ð²
ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸Ñ Ð½Ð¾Ð²Ð¾ÑÑÐ½Ð¾Ð³Ð¾ ÐºÐ¾Ð½ÑÐµÐ½ÑÐ°
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð½Ð¾Ð²Ð¾ÑÑÐµÐ¹
Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° ÐµÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ·ÑÐºÐ°
fake news detection
disinformation spread
text analysis
news content filtering
news classification
natural language processing

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description: The purpose of the study is to detect false information accompanied by text, multimedia content, and information about disseminators. The subject of the research is the approaches to detecting fake news content. The research set the following goals: 1. Investigation of modern approaches to analyzing textual and multimedia news content for their credibility, and identifying the most promising ones. 2. Adaptation of the selected approaches to the Russian-language media space. 3. Formulation of a combined method for detecting unreliable news content, ensuring the analysis of textual and multimedia data, as well as propagation paths. 4. Development and implementation of machine learning models that collectively ensure the realization of the proposed method. 5. Evaluation of the accuracy of each model and the overall effectiveness of the proposed method. During the work, a comprehensive approach to detecting fake news content was proposed, considering accompanying media materials and information about the disseminator. Modern approaches to detecting unreliable news content were explored, and a method combining several approaches for simultaneous analysis of textual, multimedia, and propagation paths was proposed. These approaches were adapted for the Russian-language media space, and the accuracy and effectiveness of the proposed method were demonstrated. The results can be used to identify fake news content.