The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Identifying the influential bloggers in a community
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 6th International Conference on Semantic Systems
Extracting events and event descriptions from Twitter
Proceedings of the 20th international conference companion on World wide web
Twitinfo: aggregating and visualizing microblogs for event exploration
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Finding and assessing social media information sources in the context of journalism
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Social media is inarguably a powerful medium for mobilizing support for various real-life events be it for social, political, or economic transformation. Further, in contrast to the generic information obtained from the mainstream media, novel and specific information available at social media sites makes them valuable sources for event analysis. However, due to the power law distribution of the Internet, these overwhelmingly large number of sources are buried in the Long Tail making it extremely challenging to identify the quality sources among them. In this research, we propose an evolutionary mutual reinforcement model to confront these challenges. Due to absence of ground truth, a novel evaluation strategy is introduced. The results indicate tremendous potential. 25% to 130% information gain is obtained with the proposed approach when compared against the state-of-the-art baselines, viz. Google blog search and Icerocket blog search. Further, our ranking methodology is capable of identifying the highly informative sources much earlier than the aforementioned baselines. The proposed model affords an apparatus for micro and macro event analysis.