The problem of discovering information flow trends and influencers in social networks has become increasingly relevant both because of the increasing amount of content available from online networks in the form of social streams, and because of its relevance as a tool for content trends analysis. An important part of this analysis is to determine the key patterns of flow and corresponding influ-encers in the underlying network. Almost all the work on influence analysis has focused on fixed models of the network structure, and edge-based transmission between nodes. In this paper, we propose a fully content-centered model of flow analysis in social network streams, in which the analysis is based on actual content transmissions in the network, rather than a static model of transmission on the edges. First, we introduce the problem of information flow mining in social streams, and then propose a novel algorithm In-FlowMine to discover the information flow patterns in the network. We then leverage this approach to determine the key influencers in the network. Our approach is flexible, since it can also determine topic-specific influencers. We experimentally show the effectiveness and efficiency of our model.
|Original language||English (US)|
|Title of host publication||CIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management|
|Number of pages||6|
|State||Published - 2013|
|Event||22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States|
Duration: Oct 27 2013 → Nov 1 2013
|Name||International Conference on Information and Knowledge Management, Proceedings|
|Other||22nd ACM International Conference on Information and Knowledge Management, CIKM 2013|
|City||San Francisco, CA|
|Period||10/27/13 → 11/1/13|
Copyright 2013 Elsevier B.V., All rights reserved.
- Content analysis
- Flow mining
- Influence analysis