Abstract
Social bot often publish content that has a high degree of similarity in text or semantics. Combining this feature, this paper designs a method to detect social bot. First, the user-published text is used as the input to use sentiment analysis and data cleaning to predict the content. Processing and classification, and then adding the quantum similarity algorithm in the emerging quantum discipline in recent years to the structural system of social bot detection to perform similarity clustering, which greatly improves the operating efficiency of the system, and then obtains the corresponding user attribute characteristics for artificial intelligence. Intelligent algorithm classification, on the collected real dataset, marked two social bot groups for machine learning classification and detection, and listed two traditional similarity algorithms for comparison, the results show that the quantum similarity results Compared with the two traditional similarity algorithms, it has improved, and the average accuracy improvement was about 2%.
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