Research question stated in current paper concerns measuring significance of interest topic to a person on the base of digital footprints, observed in on-line social media. Interests are represented by on-line social groups in VK social network, which were marked by topics. Topic significance to a person is supposed to be related to the fraction of representative groups in user’s subscription list. We imply that for each topic, depending on its popularity, relation to geographical region, and social acceptability, there is a value of group size which is significant. In addition, we suppose, that professional clusters of groups demonstrate relatively higher inner density and unify common groups. Therefore, following groups from more specific clusters indicate higher personal involvement to a topic – in this way, representative topical groups are marked. We build social group similarity graph, which is based on the number of common followers, extract subgraphs related to a single topic, and analyse bins of groups, build with increase of group sizes. Results show topics of general interests have higher density at larger groups in contrast to specific interests, which is in correspondence with initial hypothesis.
CITATION STYLE
Vaganov, D., Bardina, M., & Guleva, V. (2020). From generality to specificity: On matter of scale in social media topic communities. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12140 LNCS, pp. 305–318). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-50423-6_23
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