dc.contributor.authorTan, Luke Kien-Weng
dc.contributor.authorNa, Jin-Cheon
dc.contributor.authorDing, Ying
dc.date.accessioned2016-05-04T02:03:21Z
dc.date.available2016-05-04T02:03:21Z
dc.date.issued2015
dc.identifier.citationTan, L. K.-W., Na, J.-C., & Ding, Y. (2015). Influence diffusion detection using the influence style (INFUSE) model. Journal of the Association for Information Science and Technology, 66(8), 1717-1733.en_US
dc.identifier.issn2330-1635en_US
dc.identifier.urihttp://hdl.handle.net/10220/40481
dc.description.abstractBlogs are readily available sources of opinions and sentiments that in turn could influence the opinions of the blog readers. Previous studies had attempted to infer influence from blog features, but they ignored the possible influence styles that describe the different ways or manner in which influence is exerted. In this paper, we propose a novel approach of analyzing bloggers’ influence styles, and using the influence styles as features to improve the performance of influence diffusion detection between linked bloggers. The proposed influence style (INFUSE) model describes bloggers’ influence through their engagement style, persuasion style, and persona. Methods used include similarity analysis to detect the creating-sharing aspect of engagement style, subjectivity analysis to measure persuasion style, and sentiment analysis to identify persona style. We further extend the INFUSE model to detect influence diffusion between linked bloggers based on the bloggers’ influence styles. The INFUSE model performed well with an average F1-score of 76% when compared to the in-degree and sentiment-value baseline approaches. While previous studies had focused on the existence of influence between linked bloggers in detecting influence diffusion, our INFUSE model is shown to provide a fine-grained description of the manner in which influence is diffused based on the bloggers’ influence styles.en_US
dc.format.extent18 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesJournal of the Association for Information Science and Technologyen_US
dc.rights© 2015 ASIS&T. This is the author created version of a work that has been peer reviewed and accepted for publication by Journal of the Association for Information Science and Technology, ASIS&T. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1002/asi.23287].en_US
dc.subjectNatural language processingen_US
dc.subjectText miningen_US
dc.subjectText processingen_US
dc.titleInfluence diffusion detection using the influence style (INFUSE) modelen_US
dc.typeJournal Article
dc.contributor.schoolWee Kim Wee School of Communication and Informationen_US
dc.identifier.doihttp://dx.doi.org/10.1002/asi.23287
dc.description.versionAccepted versionen_US


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