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The increasing sophistication of social bots and their ability to mimic human behaviour online presents a significant challenge in distinguishing them from genuine users. This paper proposes a novel approach for detecting and estimating the parameters of such bots within the VKontakte social network. This method involves creating a dataset by using controlled attacks on ‘honeypot’ accounts to measure bot activity. This process allows us to assess these bots' cost, quality, and speed. Additionally, we evaluate how well users trust bots by using a Turing test, which tests users' ability to identify bots. This dataset is then used within conventional machine learning techniques, leveraging features extracted from interaction graphs, text content, and statistical distributions. The evaluation of the proposed approach shows that it effectively detects bots and predicts their behaviour with considerable accuracy. It can work well …