Microblogs have become an important platform for people to publish,transform information and acquire knowledge.This paper focuses on the problem of discovering user interest in microblogs.In this paper,we propose a topic mining model based on Latent Dirichlet Allocation(LDA) named user-topic model.For each user,the interests are divided into two parts by different ways to generate the microblogs:original interest and retweet interest.We represent a Gibbs sampling implementation for inference the parameters of our model,and discover not only user's original interest,but also retweet interest.Then we combine original interest and retweet interest to compute interest words for users.Experiments on a dataset of Sina microblogs demonstrate that our model is able to discover user interest effectively and outperforms existing topic models in this task.And we find that original interest and retweet interest are similar and the topics of interest contain user labels.The interest words discovered by our model reflect user labels,but range is much broader.
With the development of online social networks,a special group of online users named organized posters(or Internet water army,Internet paid posters in some literatures) have fl ooded the social network communities. They are organized in groups to post with specific purposes and sometimes even confuse or mislead normal users.In this paper,we study the individual and group characteristics of organized posters. A classifier is constructed based on the individual and group characteristics to detect them. Extensive experimental results on three real datasets demonstrate that our method based on individual and group characteristics using SVM model(IGCSVM) is effective in detecting organized posters and better than existing methods. We take a first look at finding the promoters based on the detected organized posters of our IGCSVM method. Our experiments show that it is effective in detecting promoters.
WANG XiangZHANG ZhilinYU XiangJIA YanZHOU BinLI Shasha
