Marketing Science
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
How and why people Twitter: the role that micro-blogging plays in informal communication at work
Proceedings of the ACM 2009 international conference on Supporting group work
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Randomization tests for distinguishing social influence and homophily effects
Proceedings of the 19th international conference on World wide web
Community-based topic modeling for social tagging
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Like like alike: joint friendship and interest propagation in social networks
Proceedings of the 20th international conference on World wide web
LikeMiner: a system for mining the power of 'like' in social media networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Do all birds tweet the same?: characterizing twitter around the world
Proceedings of the 20th ACM international conference on Information and knowledge management
Following the follower: detecting communities with common interests on twitter
Proceedings of the 23rd ACM conference on Hypertext and social media
Tweets Beget Propinquity: Detecting Highly Interactive Communities on Twitter Using Tweeting Links
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Interest classification of Twitter users using Wikipedia
Proceedings of the 9th International Symposium on Open Collaboration
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One important problem in target advertising and viral marketing on online social networking sites is the efficient identification of communities with common interests in large social networks. Existing methods involve large scale community detection on the entire social network before determining the interests of individuals within these communities. This approach is both computationally intensive and may result in communities without a common interest. We propose an efficient approach for detecting communities that share common interests on Twitter. Our approach involves first identifying celebrities that are representative of an interest category before detecting communities based on linkages among followers of these celebrities. We also study the characteristics of these communities and the effects of deepening or specialization of interest.