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TitlePredicting the Influence of Users’ Posted Information for eWOM Advertising in Social Networks
Creator唐揆
Chen, Y. L.;Tang, Kwei;Wu, C. C.;Jheng, J. Y.
Contributor企管系
Key WordsSocial network; Electronic word-of-mouth (eWOM); Influence; Data mining; Sentiment analysis
Date2014
Date Issued25-Aug-2016 14:14:07 (UTC+8)
SummaryMany 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.
RelationElectronic Commerce Research and Applications, 13(6), 431-439
Typearticle
DOI http://dx.doi.org/10.1016/j.elerap.2014.10.001
dc.contributor 企管系
dc.creator (作者) 唐揆zh_TW
dc.creator (作者) Chen, Y. L.;Tang, Kwei;Wu, C. C.;Jheng, J. Y.
dc.date (日期) 2014
dc.date.accessioned 25-Aug-2016 14:14:07 (UTC+8)-
dc.date.available 25-Aug-2016 14:14:07 (UTC+8)-
dc.date.issued (上傳時間) 25-Aug-2016 14:14:07 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/100742-
dc.description.abstract (摘要) 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.
dc.format.extent 707347 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Electronic Commerce Research and Applications, 13(6), 431-439
dc.subject (關鍵詞) Social network; Electronic word-of-mouth (eWOM); Influence; Data mining; Sentiment analysis
dc.title (題名) Predicting the Influence of Users’ Posted Information for eWOM Advertising in Social Networks
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1016/j.elerap.2014.10.001
dc.doi.uri (DOI) http://dx.doi.org/10.1016/j.elerap.2014.10.001