Skip to main content
Contribution to Book
Toward predicting poularity of social marketing messages
Social Computing, Behavioral-Cultural Modeling and Prediction: Lecture Notes in Computer Science (2011)
  • Linchi Kwok, Syracuse University
  • Bei Yu, Syracuse University
  • Miam Chen, Syracuse University
Abstract

Computing, Behavioral-Cultural Modeling and Prediction: Lecture Notes in Computer Science (pp. 317-324). Heidelberg, Germany: Springer. Popularity of social marketing messages indicates the effectiveness of the corresponding marketing strategies. This research aims to discover the characteristics of social marketing messages that contribute to different level of popularity. Using messages posted by a sample of restaurants on Facebook as a case study, we measured the message popularity by the number of “likes” voted by fans, and examined the relationship between the message popularity and two properties of the messages: (1) content, and (2) media type. Combining a number of text mining and statistics methods, we have discovered some interesting patterns correlated to “more popular” and “less popular” social marketing messages. This work lays foundation for building computational models to predict the popularity of social marketing messages in the future.

Keywords
  • Social Media,
  • Facebook,
  • Content Analysis,
  • New Media,
  • Prediction,
  • Marketing,
  • Hospitality Management
Publication Date
Spring March, 2011
Editor
J. Salerno, S. J. Yang, D. Nau, & S. K. Chai
Publisher
Springer
ISBN
978-3-642-19655-3
Publisher Statement
Yu, B., Chen, M., & Kwok, L. (2011). Toward predicting popularity of social marketing messages. In J. Salerno, S.J. Yang, D. Nau, & S.K. Chai (Ed.), Social Computing, Behavioral-Cultural Modeling and Prediction: Lecture Notes in Computer Science (pp. 317-324). Heidelberg, Germany: Springer.
Citation Information
Linchi Kwok, Bei Yu and Miam Chen. "Toward predicting poularity of social marketing messages" Heidelberg, GermanySocial Computing, Behavioral-Cultural Modeling and Prediction: Lecture Notes in Computer Science (2011)
Available at: http://works.bepress.com/linchi_kwok/10/