Predicting the influence of users' posted information for eWOM advertising in social networks

Yen Liang Chen, Kwei Tang, Chia Chi Wu, Ru Yun Jheng

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

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.

Original languageEnglish
Pages (from-to)431-439
Number of pages9
JournalElectronic Commerce Research and Applications
Volume13
Issue number6
DOIs
StatePublished - 1 Nov 2014

Keywords

  • Data mining
  • Electronic word-of-mouth (eWOM)
  • Influence
  • Sentiment analysis
  • Social network

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