articles

The problem of unreliable information is currently the most critical in the field of information dissemination in the Internet environment. The global transition of information sources to the Internet has led to the fact that information is distributed too quickly, and it is quite difficult to verify the accuracy of the information. This topic is raised when talking about the media, social networks, blogs, and other sources of information. The transmission of information has ceased to be a matter only for the media. Any Internet user can be a source of information. The development of free sources of information and the digitalization of sources have led to a loss of confidence in the official media. The consequence of this is the development of methods for automatically detecting false information. The objectives of this work are to study the possibility of building a model for automatically determining the level of trust in a message in a social network in Russian language and determine the most influential parameters. The considered method is aimed at a multi-sided analysis of the post, including parameters obtained from the text of the message, user data and the distribution of the message on the social network. To work with machine learning methods, a data sample was collected and marked up, on which machine learning models were trained. The data sample underwent a balancing process to obtain stable results. After training the models, five models were obtained trained on both balanced and conventional data samples. The results were obtained for models with a restriction on parameters to identify the most influential parameters. The results were machine learning models with high readings of metric values on test data and the most influential parameters were identified, which included parameters unique to the Russian language.