Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Online Communities: Designing Usability and Supporting Socialbilty
Online Communities: Designing Usability and Supporting Socialbilty
Modern Information Retrieval
Linked
The Journal of Machine Learning Research
Sensing and Modeling Human Networks using the Sociometer
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Group and topic discovery from relations and text
Proceedings of the 3rd international workshop on Link discovery
Topic and role discovery in social networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Modeling Collaborations Content in Social Network Analysis
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Content-Based Social Network Analysis
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Extracting multi-dimensional relations: a generative model of groups of entities in a corpus
Proceedings of the 20th ACM international conference on Information and knowledge management
Bursty event detection from collaborative tags
World Wide Web
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In this paper, we study the problem of content-based social network discovery among people who frequently appear in world news. Google news is used as the source of data. We describe a probabilistic framework for associating people with groups. A low-dimensional topic-based representation is first obtained for news stories via probabilistic latent semantic analysis (PLSA). This is followed by construction of semantic groups by clustering such representations. Unlike many existing social network analysis approaches, which discover groups based only on binary relations (e.g. co-occurrence of people in a news article), our model clusters people using their topic distribution, which introduces contextual information in the group formation process (e.g. some people belong to several groups depending on the specific subject). The model has been used to study evolution of people with respect to topics over time. We also illustrate the advantages of our approach over a simple co-occurrence-based social network extraction method.