The Journal of Machine Learning Research
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Topic modeling with network regularization
Proceedings of the 17th international conference on World Wide Web
iTopicModel: Information Network-Integrated Topic Modeling
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
You are where you tweet: a content-based approach to geo-locating twitter users
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Information credibility on twitter
Proceedings of the 20th international conference on World wide web
Who says what to whom on twitter
Proceedings of the 20th international conference on World wide web
Empirical study of topic modeling in Twitter
Proceedings of the First Workshop on Social Media Analytics
Comparing twitter and traditional media using topic models
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
TWITOBI: A Recommendation System for Twitter Using Probabilistic Modeling
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Community detection in content-sharing social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Graph-based informative-sentence selection for opinion summarization
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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In recent years, social media websites, such as Epinions, Twitter, and Google+, have gained in popularity and have become ubiquitous in our daily lives, where rich user-generated texts are propagated through social networks. Topic models, such as Latent Dirichlet Allocation (LDA), have been proposed and shown to be useful for text analysis. The existing topic models focus on traditional document collections, which consist of a relatively small number of long and high-quality documents. However, user-generated texts tend to be shorter and noisier than traditional content. Besides, the social networks have two novel features: context information on nodes, such as user features, and edges, such as relationship, which have not been considered by the existing topic models. In this paper, we pose the problem of finding user topics in large-scale collection of documents from online social networks. We propose a comprehensive Feature based and a Social based Topic model, taking into account the user features and social networks. We demonstrate that our models have better performance than a baseline LDA in the Epinions, Twitter, and Google+ data sets.