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.


text categorization marketing social media prediction media type 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Blei, D., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. J. Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  2. 2.
    Chung, C.K., Pennebaker, J.W.: The Psychological Function of Function Words. In: Fiedler, K. (ed.) Social Communication: Frontiers of Social Psychology, pp. 343–359 (2007)Google Scholar
  3. 3.
    Domingos, P., Pazzani, M.: On the Optimality of the Simple Bayesian Classifier Under Zero-one Loss. Machine Learning 29, 103–130 (1997)CrossRefzbMATHGoogle Scholar
  4. 4.
    Forman, G.: An Extensive Empirical Study of Feature Selection Metrics for Text Categorization. Journal of Machine Learning Research 3, 1289–1305 (2003)zbMATHGoogle Scholar
  5. 5.
    Hong, L., Davison, B.D.: Empirical Study of Topic Modeling in Twitter. In: Proceedings of the First Workshop on Social Media Analytics, Washington D.C, July 25 (2010)Google Scholar
  6. 6.
    Joachims, T.: Text categorization with Support Vector Machines: Learning with Many Relevant Features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  7. 7.
    Killian, K.S.: Top 400 Restaurant Chains. Restaurant & Institutions, 28–72 (2009)Google Scholar
  8. 8.
    Mangold, W.G., Faulds, D.J.: Social Media: The New Hybrid Element of the Promotion Mix. Business Horizons 52(4), 357–365 (2009)CrossRefGoogle Scholar
  9. 9.
    Szabo, G., Huberman, B.A.: Predicting the Popularity of Online Content. Communications of the ACM 53(8), 80–88 (2010)CrossRefGoogle Scholar
  10. 10.
    Xiang, Z., Gretzel, U.: Role of Social Media in Online Travel Information Search. Tourism Management 31(2), 179–188 (2010)CrossRefGoogle Scholar
  11. 11.
    Young, L.: Brave New World. Foodservice and Hospitality 42(11), 24–28 (2010)Google Scholar
  12. 12.
    Yu, B.: An evaluation of text classification methods for literary study. Journal of Literary and Linguistic Computing 23(3), 327–343 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bei Yu
    • 1
  • Miao Chen
    • 1
  • Linchi Kwok
    • 2
  1. 1.School of Information StudiesSyracuse UniversityUSA
  2. 2.College of Human EcologySyracuse UniversityUSA

Personalised recommendations