Anomaly detection method of social media user information based on data mining

Aiming at the problems of low detection accuracy, recall and F1 value of traditional social media user information anomaly detection methods, a social media user information anomaly detection method based on data mining is proposed. Firstly, we clean the social media data and eliminate the invalid and missing values in the data. Then, we filter the abnormal user information in the social media data through the unsupervised k-means algorithm in data mining. Finally, according to the screening results, we calculate the text word segmentation of user information, obtain the similarity of word frequency, and complete the detection of abnormal user information. The method provided by social media has the highest accuracy of 97.5%, which is the highest exception detection rate of 97.5% and the highest exception detection rate of social media. Copyright © 2024 Inderscience Enterprises Ltd.