Targeting online communities to maximise information diffusion
Author(s) -
V́aclav Beĺak,
Samantha Lam,
Conor Hayes
Publication year - 2012
Publication title -
aran (university of galway research repository) (ollscoil na gaillimhe – university of galway)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1145/2187980.2188255
Subject(s) - order (exchange) , online community , computer science , set (abstract data type) , irish , world wide web , social network (sociolinguistics) , data science , internet privacy , knowledge management , business , social media , linguistics , philosophy , finance , programming language
In recent years, many companies have started to utilise online social communities as a means of communicating with and targeting their employees and customers. Such online communities include discussion fora which are driven by the conversational activity of users. For example, users may respond to certain ideas as a result of the influence of their neighbours in the underlying social network. We analyse such influence to target communities rather than individual actors because information is usually shared with the community and not just with individual users. In this paper, we study information diffusion across communities and argue that some communities are more suitable for maximising spread than others. In order to achieve this, we develop a set of novel measures for cross-community influence, and show that it outperforms other targeting strategies on 51 weeks of data of the largest Irish online discussion system, Boards.ie.
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