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    政大機構典藏 > 商學院 > 企業管理學系 > 期刊論文 >  Item 140.119/75525
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/75525


    Title: Predicting the influence of users' posted information for eWOM advertising in social networks
    Authors: Chen, Y.-L.;Tang, Kwei;Wu, C.-C.
    唐揆
    Contributors: 企管系
    Keywords: Social networking (online);Electronic wordof-mouth (eWOM);Influence;Sentiment analysis;Data mining
    Date: 2014-11
    Issue Date: 2015-06-02 17:10:41 (UTC+8)
    Abstract: Many social network websites have been aggressively exploring innovative electronic word-of-mouth (eWOM) advertising strategies using information shared by users, such as posts and product reviews. For example, Facebook offers a service allowing marketers to utilize users' posts to automatically generate advertisements. The effectiveness of this practice depends on the ability to accurately predict a post's influence on its readers. For an advertising strategy of this nature, the influence of a post is determined jointly by the features of the post, such as contents and time of creation, and the features of the author of the post. We propose two models for predicting the influence of a post using both sources of influence, post- and author-related features, as predictors. An empirical evaluation shows that the proposed predictive features improve prediction accuracy, and the models are effective in predicting the influence score.
    Relation: Electronic Commerce Research and Applications, 13(6), 431-439
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1016/j.elerap.2014.10.001
    DOI: 10.1016/j.elerap.2014.10.001
    Appears in Collections:[企業管理學系] 期刊論文

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