Advertisement

The Study of Predicting Social Topic Trends

  • Sung-Shun WengEmail author
  • Huai-Wen Hsu
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 41)

Abstract

The rapid growth of the social media leads people participate in the popular topics that have been discussed in our daily lives by the social networks. Large amounts of word-of-mouth and news event have flood the social media. Recognizing the trends of the main topics that people care about from the huge and various social messages, grasping the business opportunities and adopting appropriate strategies have become an important lesson for business, governmental and non-governmental organizations. Previous research on social topic detection has focused on sentiment analysis for content. This study integrates the hidden markov model and latent dirichlet allocation topic model to forecast trends of the social topics based on time series data of user reviews. Experimental results on real dataset showed that the approach proposed by this study are able to recognize the latent social topics, keywords and forecast the trends of social topics effectively on the social media.

Keywords

Social media Topic detection Time series Trend prediction 

References

  1. 1.
    Chen, Y., Amiri, H., Li, Z., Chua, T.-S.: Emerging topic detection for organizations from microblogs. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43–52. ACM, New York (2013)Google Scholar
  2. 2.
    Ritter, A., Etzioni, O., Clark, S.: Open domain event extraction from Twitter. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1104–1112. ACM, New York (2012)Google Scholar
  3. 3.
    Kaplan, A.M., Haenlein, M.: Users of the world, unite! the challenges and opportunities of Social Media. Bus. Horiz. 53(1), 59–68 (2010)CrossRefGoogle Scholar
  4. 4.
    Turban, E., King, D.R., Lang, J.: Introduction to Electronic Commerce. Prentice Hall, Upper Saddle River (2009)Google Scholar
  5. 5.
    Kotler, P., Keller, K.L.: Marketing Management. Pearson Prentice Hall, Upper Saddle River (2009)Google Scholar
  6. 6.
    Smith, N., Wollan, R., Zhou, C.: The Social Media Management Handbook: Everything You Need to Know to Get Social Media Working in Your Business. Wiley, Hoboken (2011)Google Scholar
  7. 7.
    Hemann, C., Burbary, K.: Digital Marketing Analytics: Making Sense of Consumer Data in a Digital World. Que, Indianapolis (2013)Google Scholar
  8. 8.
    Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer, Berlin (2011)CrossRefGoogle Scholar
  9. 9.
    Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in Twitter: the million follower fallacy. In: ICWSM, vol. 10, no. 10-17, p. 30 (2010)Google Scholar
  10. 10.
    Li, H., Mukherjee, A., Liu, B., Kornfield, R., Emery, S.: Detecting campaign promoters on Twitter using markov random fields. In: 2014 IEEE International Conference on Data Mining, pp. 290–299 (2014)Google Scholar
  11. 11.
    Singh, V.K., Piryani, R., Uddin, A., Waila, P.: Sentiment analysis of movie reviews and blog posts. In: 2013 IEEE 3rd International Advance Computing Conference (IACC), pp. 893–898 (2013)Google Scholar
  12. 12.
    Chen, C.C., Chen, Y.-T., Sun, Y., Chen, M.C.: Life cycle modeling of news events using aging theory. In: Lavrač, N., Gamberger, D., Blockeel, H., Todorovski, L. (eds.) Machine Learning: ECML 2003, pp. 47–59. Springer, Berlin Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Sayyadi, H., Hurst, M., Maykov, A.: Event detection and tracking in social streams. In: ICWSM, May 2009Google Scholar
  14. 14.
    Cataldi, M., Di Caro, L., Schifanella, C.: Emerging topic detection on Twitter based on temporal and social terms evaluation. In: Proceedings of the Tenth International Workshop on Multimedia Data Mining, pp. 4:1–4:10. ACM, New York (2010)Google Scholar
  15. 15.
    You, L., Du, Y., Ge, J., Huang, X., Wu, L.: BBS based hot topic retrieval using back-propagation neural network. In: Su, K.-Y., Tsujii, J., Lee, J.-H., Kwong, O.Y. (eds.) Natural Language Processing – IJCNLP 2004, pp. 139–148. Springer, Berlin Heidelberg (2004)Google Scholar
  16. 16.
    Xie, J., Liu, G., Ning, W.: A topic detection method for Chinese microblog. In: 2012 International Symposium on Information Science and Engineering (ISISE), pp. 100–103 (2012)Google Scholar
  17. 17.
    Becker, H., Naaman, M., Gravano, L.: Learning similarity metrics for event identification in social media. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 291–300. ACM, New York (2010)Google Scholar
  18. 18.
    Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)CrossRefGoogle Scholar
  19. 19.
    Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. 42(1–2), 177–196 (2001)CrossRefGoogle Scholar
  20. 20.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  21. 21.
    Wang, X., McCallum, A.: Topics over Time: a non-Markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 424–433. ACM, New York (2006)Google Scholar
  22. 22.
    Wang, Y., Agichtein, E., Benzi, M.: TM-LDA: efficient online modeling of latent topic transitions in social media. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 123–131. ACM, New York (2012)Google Scholar
  23. 23.
    Szabo, G., Huberman, B.A.: Predicting the popularity of online content. Commun. ACM 53(8), 80–88 (2010)CrossRefGoogle Scholar
  24. 24.
    Achrekar, H., Gandhe, A., Lazarus, R., Yu, S.-H., Liu, B.: Predicting flu trends using twitter data. In: 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 702–707 (2011)Google Scholar
  25. 25.
    Kim, S.D., Kim, S.H., Cho, H.G.: Predicting the virtual temperature of web-blog articles as a measurement tool for online popularity. In: 2011 IEEE 11th International Conference on Computer and Information Technology (CIT), pp. 449–454 (2011)Google Scholar
  26. 26.
    Ritterman, J., Osborne, M., Klein, E.: Using prediction markets and Twitter to predict a swine flu pandemic. In: 1st International Workshop on Mining Social Media, vol. 9, pp. 9–17, November 2009Google Scholar
  27. 27.
    Bandari, R., Asur, S., Huberman, B.A.: The pulse of news in social media: forecasting popularity (2012)Google Scholar
  28. 28.
    Zaman, T., Fox, E.B., Bradlow, E.T.: A Bayesian approach for predicting the popularity of tweets. Ann. Appl. Stat. 8(3), 1583–1611 (2014)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Figueiredo, F.: On the prediction of popularity of trends and hits for user generated videos. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 741–746. ACM New York (2013)Google Scholar
  30. 30.
    Fernandes, K., Vinagre, P., Cortez, P.: A proactive intelligent decision support system for predicting the popularity of online news. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds.) Progress in Artificial Intelligence, pp. 535–546. Springer, Berlin (2015)CrossRefGoogle Scholar
  31. 31.
    Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  32. 32.
    Lewis, C.D.: Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting. Butterworth-Heinemann, Oxford (1982)Google Scholar
  33. 33.
    Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Nat. Acad. Sci. 101(Suppl. 1), 5228–5235 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Taipei University of TechnologyTaipeiTaiwan

Personalised recommendations