In response to the increasing proliferation of false information in social networks,this paper proposes a social network user influence model based on Bayesian networks.First,in order to deal with the selection bias of fake news and eliminate unobserved negative interactions between users and rumors,it is proposed to use Bayesian network combined with inverse propensity scoring to learn unbiased fake news sharing with positive interactions between users and fake news.Behavior.Secondly,select and calculate the characteristics related to user influence,the relationship between users and the user's own attributes,and establish a social network user influence model.Compared with biased fake news,the Bayesian network that learns unbiased sharing has higher rumor detection accuracy and recall rate,and the obtained user influence ranking is closer to the social platform algorithm.Compared with other traditional algorithms,the classification error of this model is more stable,the practicability is improved,and the influencing factors of the influence ranking are analyzed.
关键词
贝叶斯网络/虚假信息检测/社交网络
Key words
Bayesian network/false information detection/social network
维普
万方数据