Group Types in Social Media

Part of the Human–Computer Interaction Series book series (HCIS)


Dynamics of social systems are the result of the complex superposition of interactions taking place at different scales, ranging from the pairwise communications between individuals to the macroscopic evolutionary patterns of the full interaction graph. Social communities, namely groups of people originated by any spontaneous aggregation process, constitute the mid-ground between such two extremes. Groups are important constituents of social environments as they form the basis for people’s participation and engagement beyond their minute dyadic interactions. Communities in online social media have been studied widely in their static and evolutionary aspects, but only recently some attention has been devoted to the exploration of their nature. Besides the characterization of online communities along their spatio-temporal and activity features, the recent advancements in the emerging field of computational sociology have provided a new lens to study social aggregations along their social and topical dimensions. Using the online photo sharing community Flickr as a main running example, we survey some techniques that have been used to get a multi-faceted description of group types and we show that different types of groups impact on orthogonal interaction processes on the social graph, such as the diffusion of information along social ties. Our overview supports the intuition that a more nuanced description of groups could not only improve the understanding of the activity of the user base but can also foster a better interpretation of other phenomena occurring on social graphs.


Group Type Community Detection Algorithm Online Group Topical Group Common Identity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Przemyslaw Grabowicz, David Martin-Borregón, Rossano Schifanella, Bogdan State, Alejandro Jaimes, and Ricardo Baeza-Yates for the research work they have conducted jointly with the author and that is summarized in this chapter. This work is supported by the SocialSensor FP7 project, partially funded by the EC under contract number 287975.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Yahoo LabsLondonUK

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