ANALYSIS OF METHODS FOR DETECTING MISINFORMATION IN SOCIAL NETWORKS USING MACHINE LEARNING

Social networks have long become an integral part of the life of modern society. For example, in Ukraine, more than 60% of the population regularly use their functionality. For some people, pages in one or another social network have acquired commercial significance and have become a tool for generating income. There are also rare cases of buying and selling accounts or violating copyright with their help. However, the spread of inaccurate information aimed at misleading and causing serious harm is gaining momentum in social networks. Such a process is defined as “disinformation”. In addition to disinformation, the term “false information” is also distinguished. These terms are not synonymous, so they should be distinguished for the validity of the study. Misrepresentation is information that contains inaccurate information resulting from errors, but the term does not include the intent to mislead. In turn, the term “disinformation”, on the contrary, is created for the purpose of deliberately spreading false information with the aim of misleading others. In recent years, the topic of disinformation, as well as its consequences, has attracted a lot of attention. Although disinform