Detecting Influential Users in Customer-Oriented Online Communities
Every year the activity of users in various social networks is increasing. Different business entities can analyze in more detail the behavior of the audience and adapt their products and services to its needs. Social network data allow not only to find the influential individuals according to their local topological properties, but also to investigate their preferences, and thus to personalize strategies of interaction with opinion leaders. However, information channels of organizations (e.g., community of a bank in a social network) include not only target audience but also employees and fake accounts. This lowers the applicability of network-based methods of identifying influential nodes. In this study, we propose an algorithm of discovering influential nodes which combines topological metrics with the individual characteristics of users’ profiles and measures of their activities. The algorithm is used along with preliminary clustering procedure, which is aimed at the identification of groups of users with different roles, and with the algorithm of profiling the interests of users according to their subscriptions. The applicability of approach is tested using the data from a community of large Russian bank in the vk.com social network. Our results show that: (i) it is important to consider user’s role in the leader detection algorithm, (ii) the roles of poorly described users may be effectively identified using roles of its neighbors, (iii) proposed approach allows for finding users with high values of actual informational influence and for distinguishing their key interests.
KeywordsSocial network analysis Opinion leaders Topic modeling Opinion mining
This research is financially supported by The Russian Science Foundation, Agreement No 17-71-30029 with co-financing of Bank Saint Petersburg.
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