Hybrid Information Technology for Automatic Detection of Disinformation and Inauthentic Behaviour of Chat Users based on NLP, Machine Learning, and Graph Analysis

In the context of the growth of information exchange in social networks, messengers, and chats, the problem of spreading disinformation and coordinated inauthentic behaviour by users is becoming increasingly relevant and poses a threat to the state's information security. Traditional manual monitoring methods are ineffective due to the scale and speed of information dissemination, necessitating the development of intelligent automated countermeasures. The paper proposes a hybrid information technology for the automatic detection of disinformation, its sources of spread, and inauthentic behaviour among chat users, combining methods of natural language processing (NLP), machine learning, stylistic and linguistic analysis of texts, and graph analysis of social interactions. Within the study, datasets of authentic and fake messages were compiled, and mathematical models and algorithms for identifying disinformation sources were developed using metrics of graph centrality, clustering, and bigram Laplace smoothing. Experimental studies using TF-IDF, BERT, MiniLM, ensemble methods, and transformers confirmed the effectiveness of the proposed approach. The achieved accuracy in disinformation classification is up to 89.5%, and integrating content, network, and behavioural analysis significantly improves the quality of detecting coordinated information attacks. The results obtained are both scientifically novel and of practical value. They can be used to create systems for monitoring information threats, supporting cybersecurity decision-making, fact-checking, and protecting Ukraine's information space.