ARTIFICIAL INTELLIGENCE FOR DETECTING FAKE INFORMATION: INTERNATIONAL TRENDS AND IMPLEMENTATION OPPORTUNITIES

Purpose. The aim of the article is to analyse the current international trends in the use of artificial intelligence (AI) to detect fake information, study the main methods and technologies used for this, as well as the assessment of the possibilities of implementing such solutions in the systems of information security. Methodology of research. The theoretical and methodological foundation of the study is based on scientific works by researchers on the application of artificial intelligence for detecting fake information, as well as studies in machine learning and the analysis of textual and multimedia data. General scientific and special methods were used during the research process. In particular, literature analysis and research reviews enabled the study of current international trends in the application of AI for detecting fake news. Comparative analysis of existing solutions allowed for the assessment of the effectiveness of methods such as text analysis, machine learning, anomaly detection, multimedia content analysis, and bot detection in combating disinformation. Findings. The content of the concept of “AI-based fake information detection systems” was defined, understood as an integrated system that employs automated algorithms to analyse and verify information aimed at combating disinformation. The main task of such a system is to identify fake news through the use of machine learning methods, text analysis, anomaly tracking, and multimedia content analysis, which enhances the accuracy and speed of identifying fake messages. The main challenges associated with the spread of disinformation were identified, requiring new approaches in the application of AI: the growing volume of data, the emergence of new forms of fakes, the development of disinformation campaigns, and the necessity for rapid response to them. An analysis of international practices in applying artificial intelligence for detecting fake information was conducted, resulting in the generalization of the main principles for using algorithms for automatic data analysis, model training, bot detection, and tracking anomalous behavioural patterns. Originality. The technological content of the concept of “AI-based fake information detection systems” has been further developed, encompassing the use of machine learning, text analysis, anomaly tracking, and multimedia content analysis for effective disinformation detection. The main areas of application of artificial intelligence for identifying fake news were substantiated, and their effectiveness in addressing information security issues and combating disinformation campaigns at the international level was determined. Practices for detecting fake news were generalized, which can be adapted for different contexts, making the system more flexible and adaptive. Practical value. The research results have significant practical implications and can be used to develop new innovative solutions in the field of fake information detection. This will allow media platforms, government, and private organizations to enhance the accuracy and speed of disinformation identification. Reducing the spread of fake news will contribute to ensuring information security. The research may serve as an important resource for developing government strategies in combating disinformation and could also be useful for companies involved in monitoring information flows and cybersecurity in their efforts to prevent disinformation and protect society from negative informational impacts. Key words: AI, fake information, machine learning, text analysis, bot detection, anomaly tracking, information security, disinformation, multimedia content, international trends.