Discovering social structures of local influence by using tweetStimuli |
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| Authors: | Arturo Tejeda-Gómez Miquel Sànchez-Marrè Josep M Pujol |
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| Affiliation: | 1. Knowledge Engineering and Machine Learning Group, Universitat Politecnica de Catalunya-Barcelona Tech, Jordi Girona 1-3, 08034 Barcelona, Spainjatejeda@lsi.upc.edu;3. Knowledge Engineering and Machine Learning Group, Universitat Politecnica de Catalunya-Barcelona Tech, Jordi Girona 1-3, 08034 Barcelona, Spain;4. 3scale Networks S.L. Nàpols 187, 8th Floor, 08013 Barcelona, Spain |
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| Abstract: | Information diffusion in large-scale networks has been studied to identify the users influence. The influence has been targeted as a key feature either to reach large populations or influencing public opinion. Through the use of micro-blogs, such as Twitter, global influencers have been identified and ranked based on message propagation (retweets). In this paper, a new application is presented, which allows to find first and classify then the local influence on Twitter: who have influenced you and who have been influenced by you. Until now, social structures of tweets’ original authors that have been either retweeted or marked as favourites are unobservable. Throughout this application, these structures can be discovered and they reveal the existence of communities formed by users of similar profile (that are connected among them) interrelated with other similar profile users’ communities. |
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| Keywords: | influence social networks clustering social structures |
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