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Community detection in online social networks is typically based on theanalysis of the explicit connections between users, such as "friends" onFacebook and "followers" on Twitter. But online users often have hundreds oreven thousands of such connections, and many of these connections do notcorrespond to real friendships or more generally to accounts that usersinteract with. We claim that community detection in online social networksshould be question-oriented and rely on additional information beyond thesimple structure of the network. The concept of 'community' is very general,and different questions such as "whom do we interact with?" and "with whom dowe share similar interests?" can lead to the discovery of different socialgroups. In this paper we focus on three types of communities beyond structuralcommunities: activity-based, topic-based, and interaction-based. We analyze aTwitter dataset using three different weightings of the structural networkmeant to highlight these three community types, and then infer the communitiesassociated with these weightings. We show that the communities obtained in thethree weighted cases are highly different from each other, and from thecommunities obtained by considering only the unweighted structural network. Ourresults confirm that asking a precise question is an unavoidable first step incommunity detection in online social networks, and that different questions canlead to different insights about the network under study.

Followers Are Not Enough: A Question-Oriented Approach to Community Detection in Online Social Networks