Characterization of online groups along space, time, and social dimensions

Martin, David; Aiello, Luca Maria; Grabowicz, Przemyslaw; Jaimes Larrarte, Alejandro; Baeza Yates, Ricardo

doi:http://dx.doi.org/10.1140/epjds/s13688-014-0008-y

Characterization of online groups along space, time, and social dimensions

Llicència

Citació

Martin-Borregon D, Aiello LM, Grabowicz P, Jaimes A, Baeza-Yates R. Characterization of online groups along space, time, and social dimensions. EPJ Data Science. 2014; 3: 8. DOI 10.1140/epjds/s13688-014-0008-y

Resum

Social groups play a crucial role in online social media because they form the basis for user participation and engagement. Although widely studied in their static and evolutionary aspects, no much attention has been devoted to the exploration of the nature of groups. In fact, groups can originate from different aggregation processes that may be determined by several orthogonal factors. A key question in this scenario is whether it is possible to identify the different types of groups that emerge spontaneously in online social media and how they differ. We propose a general framework for the characterization of groups along the geographical, temporal, and socio-topical dimensions and we apply it on a very large dataset from Flickr. In particular, we define a new metric to account for geographic dispersion, we use a clustering approach on activity traces to extract classes of different temporal footprints, and we transpose the “common identity and common bond” theory into metrics to identify the skew of a group towards sociality or topicality. We directly validate the predictions of the sociological theory showing that the metrics are able to forecast with high accuracy the group type when compared to a human-generated ground truth. Last, we frame our contribution into a wider context by putting in relation different types of groups with communities detected algorithmically on the social graph and by showing the effect that the group type might have on processes of information diffusion. Results support the intuition that a more nuanced description of groups could improve not only the understanding of the activity of the user base but also the interpretation of other phenomena occurring on social graphs.