Method and system for identifying abnormal microblog users
A user and abnormal technology, applied in the field of social network security, can solve problems such as incompleteness and inaccurate detection results, and achieve the effect of high accuracy, low memory cost, and easy implementation.
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[0121] The second embodiment is a specific application of the first embodiment, and the data set used is the original data of the Sina Weibo platform, the largest domestic microblog platform, and all microblogs published by a total of 201,780 microblog users, including marketing and advertising accounts. After training the model for 98,100 authenticated users, the time to obtain the corresponding normal user behavior time feature vector is about 11 minutes. Using the vector to identify 201,780 users, the identification time is about 4 minutes. Among them, 5,089 machine users were detected, most of them were Advertising, marketing accounts. Therefore, in terms of training time, detection efficiency and detection quality, this method can meet the requirements of most of the current microblog platforms.
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