Trawling the Web for emerging cyber-communities
WWW '99 Proceedings of the eighth international conference on World Wide Web
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 15th international conference on World Wide Web
Finding community structure in mega-scale social networks: [extended abstract]
Proceedings of the 16th international conference on World Wide Web
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
Community evolution in dynamic multi-mode networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Combining link and content for community detection: a discriminative approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Uncoverning Groups via Heterogeneous Interaction Analysis
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
Probabilistic model for discovering topic based communities in social networks
Proceedings of the 20th ACM international conference on Information and knowledge management
Using content and interactions for discovering communities in social networks
Proceedings of the 21st international conference on World Wide Web
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Automatic detection of communities (or cohesive groups of actors in social network) in online social media platforms based on user interests and interaction is a problem that has recently attracted a lot of research attention. Mining user interactions on Twitter to discover such communities is a technically challenging information retrieval task. We present an algorithm - iTop - to discover interaction based topic centric communities by mining user interaction signals (such as @-messages and retweets) which indicate cohesion. iTop takes any topic as an input keyword and exploits local information to infer global topic-centric communities. We evaluate the discovered communities along three dimensions: graph based (node-edge quality), empirical-based (Twitter lists) and semantic based (frequent n-grams in tweets). We conduct experiments on a publicly available scrape of Twitter provided by InfoChimps via a web service. We perform a case study on two diverse topics - 'Computer Aided Design (CAD)' and 'Kashmir' to demonstrate the efficacy of iTop. Empirical results from both case studies show that iTop is successfully able to discover topic-centric, interaction based communities on Twitter.