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

doi:10.1109/MIS.2010.27

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 journal › Article › peer-review

35 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 language	English (US)
Article number	5432262
Pages (from-to)	75-78
Number of pages	4
Journal	IEEE Intelligent Systems
Volume	25
Issue number	1
DOIs	https://doi.org/10.1109/MIS.2010.27
State	Published - 2010

ASJC Scopus subject areas

Computer Networks and Communications
Artificial Intelligence

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10.1109/MIS.2010.27

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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.",

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AU - Lusch, Robert F.

AU - Chen, Hsinchun

AU - Zimbra, David

AU - Zeng, Shuo

PY - 2010

Y1 - 2010

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

Abstract

ASJC Scopus subject areas

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