Blog Community Discovery and Evolution Based on Mutual Awareness Expansion

• Published in: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence pp. 48 - 56
• Main Authors: Lin, Yu-Ru, Sundaram, Hari, Chi, Yun, Tatemura, Junichi, Tseng, Belle L.
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 02.11.2007
• Series: ACM Conferences
• Subjects: Human-centered computing > Collaborative and social computing; Information systems > Information retrieval; Information systems > Information retrieval > Evaluation of retrieval results; Mathematics of computing > Discrete mathematics > Graph theory > Graph algorithms
• ISBN: 0769530265; 9780769530260
• DOI: 10.1109/WI.2007.30

There are information needs involving costly decisions that cannot be efficiently satisfied through conventional web search engines. Alternately, community centric search can provide multiple viewpoints to facilitate decision making. We propose to discover and model the temporal dynamics of thematic communities based on mutual awareness, where the awareness arises due to observable blogger actions and the expansion of mutual awareness leads to community formation. Given a query, we construct a directed action graph that is time-dependent, and weighted with respect to the query. We model the process of mutual awareness expansion using a random walk process and extract communities based on the model. We propose an interaction space based representation to quantify community dynamics. Each community is represented as a vector in the interaction space and its evolution is determined by a novel interaction correlation method. We have conducted experiments with a real-world blog dataset and have promising results for detection as well as insightful results for community evolution.