Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Mining topic-level influence in heterogeneous networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Patterns of temporal variation in online media
Proceedings of the fourth ACM international conference on Web search and data mining
Robust sentiment detection on Twitter from biased and noisy data
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Enhanced sentiment learning using Twitter hashtags and smileys
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A social-aware multimedia system for interactive cultural and educational experiences
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
The perception of others: inferring reputation from social media in the enterprise
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
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Users of Social Media typically gather into communities on the basis of some common interest. Their interactions inside these on-line communities follow several, interesting patterns. For example, they differ in the level of influence they exert to the rest of the group: some community members are actively involved, affecting a large part of the community with their actions, while the majority comprises plain participants (e.g., information consumers). Identifying users of the former category lies on the focus of interest of many recent works, as they can be employed in a variety of applications, like targeted marketing. In this paper, we build on previous research that examined influencers in the context of a popular Social Media web site, namely Twitter. Unlike existing works that consider its user base as a whole, we focus on communities that are created on-the-fly by people that post messages about a particular topic (i.e., topic communities). We examine a large and representative sample of real-world communities and evaluate to which extent their influential users determine the aggregate behavior of the entire community. To this end, we consider a practical use case: we check whether the community's overall sentiment stems from the aggregate sentiment of this core group. We also examine the dynamics of groups of influencers by assessing the strength of the ties between them. In addition, we identify patterns in the content produced by influencers and the relation between influencers of different communities. Our experiments lead to interesting conclusions that highlight many aspects of influencers' activity inside topic communities; thus, they form the basis for intelligent, data mining techniques that can automatically discover influencers in the context of a community.