Weibo spammers’ identification algorithm based on Bayesian model - Yan-mei ZHANG; Ying-ying HUANG; Shi-jie GAN; Yi DING; Zhi-long MA (Tongxin xuebao)
In order to distinguish the spammers efficiently,a classifier based on the behavior characteristics was established.By analyzing the previous research,the ratio of followers,total number of blog posts,the number of friends,comprehensive quality evaluation and favorites according to latest data set,the Weibo spammers’ identification algorithm was realized based on Bayesian model and genetic algorithm.The experiment result based on the real-time data of Sina Weibo verify that the Bayesian model recognition algorithm can ensure spammers recognition accuracy without sacrificing recognition rate of non-spammers,and the proposed threshold value matrix proposed optimization can significantly improve recognition accuracy navy.