Interactive visualization of news distribution in blog space
New Generation Computing
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MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
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WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
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APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Chinese Blog Clustering by Hidden Sentiment Factors
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Social reader: following social networks in the wilds of the blogosphere
WSM '09 Proceedings of the first SIGMM workshop on Social media
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ACM Computing Surveys (CSUR)
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RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
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ACM Transactions on the Web (TWEB)
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COCOA'11 Proceedings of the 5th international conference on Combinatorial optimization and applications
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Computers & Mathematics with Applications
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KES-AMSTA'12 Proceedings of the 6th KES international conference on Agent and Multi-Agent Systems: technologies and applications
Social reader: towards browsing the social web
Multimedia Tools and Applications
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The rapid growth of blog (also known as "weblog") data provides a rich resource for social community mining. In this paper, we put forward a novel research problem of mining the latent friends of bloggers based on the contents of their blog entries. Latent friends are defined in this paper as people who share the similar topic distribution in their blogs. These people may not actually know each other, but they have the interest and potential to find each other out. Three approaches are designed for latent friend detection. The first one, called cosine similarity-based method, determines the similarity between bloggers by calculating the cosine similarity between the contents of the blogs. The second approach, known as topic-based method, is based on the discovery of latent topics using a latent topic model and then calculating the similarity at the topic level. The third one is two-level similarity-based, which is conducted in two stages. In the first stage, an existing topic hierarchy is exploited to build a topic distribution for a blogger. Then, in the second stage, a detailed similarity comparison is conducted for bloggers that are close in interest to each other which are discovered in the first stage. Our experimental results show that both the topic-based and two-level similarity-based methods work well, and the last approach performs much better than the first two. In this paper, we give a detailed analysis of the advantages and disadvantages of different approaches.