Abstract
Identifying Relevant Sets, i.e., variable subsets that exhibit a coordinated behavior, in complex systems is a very relevant research topic. Systems that exhibit complex dynamics are, for example, social networks, which are characterized by complex and dynamic relationships among users. A challenging topic within this context regards the identification of communities or subsets of users, both within the whole network and within specific groups. We applied the Relevance Index method, which has been shown to be effective in many situations, to the study of communities of users in the Facebook group of the Italian association of patients affected by Hidradenitis Suppurativa. Since the need for computing the Relevance Index for each possible variable subset of users makes the exhaustive computation unfeasible, we resorted to the help of an efficient niching evolutionary metaheuristic, hybridized with local searches. The communities detected through the aforementioned method have been studied to search similarities in terms of number of posts, sentiments, number of contacts, roles, behaviors, etc. The results demonstrate that it is possible to detect such subsets of users in the particular Facebook group we analyzed.
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Sani, L., Lombardo, G., Pecori, R., Fornacciari, P., Mordonini, M., Cagnoni, S. (2018). Social Relevance Index for Studying Communities in a Facebook Group of Patients. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_10
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