A Zombie Fans Recognition Model for Microblog Combining Text Sentiment Analysis
摘要
The social risk caused by prevalence of zombie fans brings significant threat to the credibility of social platforms.To effectively recognize these zombie fans,this paper proposes a zombie fans recognition model,Zat-NN,based on neural network.First,the social behavior of zombie fans on microblog is analyzed to obtain behavior features of high-level zombie fans.Second,the cumulative distribution function is used to study the behavior feature differences between zombie fans and normal users.Then Convolutional Neural Network(CNN) and Long Short Term Memory(LSTM) network are combined to strengthen the sentiment analysis of microblog texts.At the same time,the number of daily forwarded microblogs,blogging tools and microblog emotion features are added as user features to improve the recognition accuracy and robustness of the Zat-NN model.Experimental results on the user dataset of Sina microblog show that the Zat-NN model can effectively recognize high-level zombie fans,improving user experience of social network.