Discovering communities in complex networks
Proceedings of the 44th annual Southeast regional conference
Marketing Science
Community detection in large-scale social networks
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
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
Characterizing social cascades in flickr
Proceedings of the first workshop on Online social networks
Comparison of online social relations in volume vs interaction: a case study of cyworld
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
Conversational tagging in twitter
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Outtweeting the twitterers - predicting information cascades in microblogs
WOSN'10 Proceedings of the 3rd conference on Online social networks
Understanding retweeting behaviors in social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Information credibility on twitter
Proceedings of the 20th international conference on World wide web
Proceedings of the 20th international conference on World wide web
Following the follower: detecting communities with common interests on twitter
Proceedings of the 23rd ACM conference on Hypertext and social media
Finding twitter communities with common interests using following links of celebrities
Proceedings of the 3rd international workshop on Modeling social media
Interest classification of Twitter users using Wikipedia
Proceedings of the 9th International Symposium on Open Collaboration
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Many community detection algorithms have been developed to detect communities on Online Social Networks (OSN). However, these algorithms are based only on topological links and researchers have observed that many topological links do not translate to actual user interaction. As such, many members of the detected communities do not communicate frequently to each other. This inactivity creates a problem in targeted advertising and viral marketing which requires the community to be highly active so as to allow the diffusion of product/service information. We propose an approach to detect highly interactive Twitter communities that share common interests, based on the frequency and patterns of direct tweeting among users, rather than the topological information implicit in follower/following links. From a topological aspect, we show that our method detects communities that are more cohesive and connected within different interest groups. We also show that the detected communities interact actively about the specific interests, based on the high frequency of #hash tags and @mentions related to this interest. In addition, we study the trends in their tweeting patterns such as how they follow and unfollow other users.