User-generated content on social media: Predicting market success with online word-of-mouth

Yong Liu, Yubo Chen, Robert F. Lusch, Hsinchun Chen, David Zimbra, Shuo Zeng

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

Online social media, a user-generated content or online word of mouth (WOM), which allows consumers to share their product opinions and experience and has the potential to influence product sales and firm strategy, is studied in context of the Hollywood movie industry. An online WOM information was collected from the message board of Yahoo Movies for a total of 257 movies released from 2005 to 2006. SentiWordNet and OpinionFinder, two lexical packages of computational linguistics, were used to construct the sentiment measures for the WOM data. Results show that WOM communication starts early in the preproduction period, becomes highly active before movie release, and diminishes as the movie is shown for more weeks in theaters. A movie that receives more active WOM communication tends to receive higher evaluations from movie critics, suggesting the number of messages could work as a signal for product quality.

Original languageEnglish (US)
Article number5432262
Pages (from-to)75-78
Number of pages4
JournalIEEE Intelligent Systems
Volume25
Issue number1
DOIs
StatePublished - Jun 3 2010

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence

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