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STH-Bass: A Spatial-Temporal Heterogeneous Bass Model to Predict Single-Tweet Popularity

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Database Systems for Advanced Applications (DASFAA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9643))

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Abstract

Prediction in social networks attracts more and more attentions since social networks have become an important part of people’s lives. Although a few topic or event prediction models have been proposed in the past few years, researches that focus on the single tweet prediction just emerge recently. In this paper, we propose STH-Bass, a Spatial and Temporal Heterogeneous Bass model derived from economic field, to predict the popularity of a single tweet. Leveraging only the first day’s information after a tweet is posted, STH-Bass can not only predict the trend of a tweet with favorite count and retweet count, but also classify whether the tweet will be popular in the future. We perform extensive experiments to evaluate the efficiency and accuracy of STH-Bass based on real-world Twitter data. The evaluation results show that STH-Bass obtains much less APE than the baselines when predicting the trend of a single tweet, and an average of 24 % higher precision when classifying the tweets popularity.

This work has been supported in part by the China 973 project (2012CB316200), National Natural Science Foundation of China (Grant number 61202024, 61472252, 61133006, 61272443, 61473109), the Opening Project of Key Lab of Information Network Security of Ministry of Public Security (The Third Research Institute of Ministry of Public Security) Grant number C15602, and the Opening Project of Baidu (Grant number 181515P005267).

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Notes

  1. 1.

    https://dev.twitter.com/.

References

  1. Golbeck, J., Robles, C., Turner, K.: Predicting personality with social media. In: ACM CHI Conference on Human Computer Interaction, pp. 253–262 (2011)

    Google Scholar 

  2. Kong, L., Jiang, S., Yan, R., Xu, S., Zhang, Y.: Ranking news events by influence decay and information fusion for media and users. In: ACM International Conference on Information and Knowledge Management (CIKM), pp. 1849–1853 (2012)

    Google Scholar 

  3. Lerman, K., Hogg, T.: Using a model of social dynamics to predict popularity of news. In: International Conference on World Wide Web (WWW), pp. 621–630 (2010)

    Google Scholar 

  4. Popescu, A.-M., Pennacchiotti, M.: Detecting controversial events from twitter. In: ACM International Conference on Information and Knowledge Management (CIKM), pp. 1873–1876 (2010)

    Google Scholar 

  5. Asur, S., Huberman, B.: Predicting the future with social media. In: IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 492–499 (2010)

    Google Scholar 

  6. Zhang, Q., Gong, Y., Guo, Y., Huang, X.: Retweet behavior prediction using hierarchical dirichlet process. In: AAAI Conference on Artificial Intelligence (AAAI), pp. 403–409 (2015)

    Google Scholar 

  7. Zhao, Q., Erdogdu, M.A., He, H.Y., Rajaraman, A., Leskovec, J.: Seismic: a self-exciting point process model for predicting tweet popularity. In: ACM SIGKDD International Conference on Knowledge Discovery andData Mining (SIGKDD), pp. 1513–1522 (2015)

    Google Scholar 

  8. Bass, F.M.: A new product growth for model consumer durables. Manag. Sci. 15(5), 215–227 (1969)

    Article  MATH  Google Scholar 

  9. Bass, F.M.: Comments on “a new product growth for model consumer durables the Bass model”. Manag. Sci. 50(12), 1833–1840 (2004)

    Article  Google Scholar 

  10. Lerman, K., Ghosh, R.: Information contagion: an empirical study of the spread of news on digg and twitter social networks. In: The International AAAI Conference on Web and Social Media (ICWSM), vol. 10, pp. 90–97 (2010)

    Google Scholar 

  11. Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: ACM International Conference on World Wide Web (WWW), pp. 591–600 (2010)

    Google Scholar 

  12. Weng, J., Lim, E.-P., Jiang, J., He, Q.: Twitterrank: finding topicsensitive inuential twitterers. In: ACM International Conference on Web Searchand Data Mining (WSDM), pp. 261–270 (2010)

    Google Scholar 

  13. Yang, S.-H., Kolcz, A., Schlaikjer, A., Gupta, P.: Large-scale high-precision topic modeling on Twitter. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 1907–1916 (2014)

    Google Scholar 

  14. Zhou, D., Chen, L., He, Y.: An unsupervised framework of exploring events on Twitter: filtering, extraction and categorization. In: AAAI Conferenceon Artificial Intelligence (AAAI), pp. 2468–2474 (2015)

    Google Scholar 

  15. de Macedo, A.Q., Marinho, L.B.: Event recommendation in eventbasedsocial networks. In: Hypertext, Social Personalization Workshop, pp. 3130–3131 (2014)

    Google Scholar 

  16. Akram, H.A.A.L., Mahmood, A.: Predicting personality traits, gender and psychopath behavior of Twitter users. Int. J. Technol. Diffus. (IJTD) 5(2), 1–14 (2014)

    Article  Google Scholar 

  17. Zhang, X., Chen, X., Chen, Y., Wang, S., Li, Z., Xia, J.: Event detection and popularity prediction in microblogging. Neurocomputing 149, 1469–1480 (2015)

    Article  Google Scholar 

  18. Deng, Z.-H., Gong, X., Jiang, F., Tsang, I.W.: Effectively predicting whether and when a topic will become prevalent in a social network. In: AAAI Conference on Artificial Intelligence (AAAI), pp. 210–216 (2015)

    Google Scholar 

  19. Castillo, C., El-Haddad, M., Pfeffer, J., Stempeck, M.: Characterizing the life cycle of online news stories using social media reactions. In: ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW), pp. 211–223 (2014)

    Google Scholar 

  20. Bandari, R., Asur, S., Huberman, B.A.: The pulse of news insocial media: forecasting popularity. In: International AAAI Conference on Web and Social Media (ICWSM), pp. 26–33 (2012)

    Google Scholar 

  21. Huang, S., Chen, M., Luo, B., Lee, D.: Predicting aggregate social activities using continuous-time stochastic process. In: ACM international Conference on Information and Knowledge Management (CIKM), pp. 982–991 (2012)

    Google Scholar 

  22. McParlane, P.J., Moshfeghi, Y., Jose, J.M.: Nobody comes here anymore, it’s too crowded; predicting image popularity on flickr. In: ACM International Conference on Multimedia Retrieval (ICMR), pp. 385–391 (2014)

    Google Scholar 

  23. Kamath, K.Y., Caverlee, J.: Spatio-temporal meme prediction: learning what hashtags will be popular where. In: ACM International Conference on Information Knowledge Management (CIKM), pp. 1341–1350 (2013)

    Google Scholar 

  24. Zaman, T.R., Herbrich, R., Van Gael, J., Stern, D.: Predicting information spreading in Twitter. In: Workshop on Computational Social Science and the Wisdom of Crowds, Annual Conference on Neural Information Processing Systems (NIPS), pp. 599–601. Citeseer (2010)

    Google Scholar 

  25. Kong, S., Mei, Q., Feng, L., Ye, F., Zhao, Z.: Predicting bursts and popularity of hashtags in real-time. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 927–930 (2014)

    Google Scholar 

  26. Chang, H.-C.: A new perspective on Twitter hashtag use: diffusion of innovation theory. J. Am. Soc. Inf. Sci. Technol. 47(1), 1–4 (2010)

    Google Scholar 

  27. Cui, A., Zhang, M., Liu, Y., Ma, S., Zhang, K.: Discover breaking events with popular hashtags in Twitter. In: ACM International Conference on Information and Knowledge Management (CIKM), pp. 1794–1798 (2012)

    Google Scholar 

  28. Yang, L., Sun, T., Zhang, M., Mei, Q.: We know what@ you# tag: does the dual role affect hashtag adoption? In: ACM International Conference on World Wide Web (WWW), pp. 261–270 (2012)

    Google Scholar 

  29. Hong, L., Dan, O., Davison, B.D.: Predicting popular messages in Twitter. In: ACM International Conference Companion on World Wide Web (WWW), pp. 57–58 (2011)

    Google Scholar 

  30. Strang, D., Tuma, N.B.: Spatial and temporal heterogeneity in diffusion. Am. J. Soc. 99(3), 614–639 (1993)

    Article  Google Scholar 

  31. Szaba, G., Huberman, B.A.: Predicting the popularity of online content. Commun. ACM 53(8), 80–88 (2010)

    Article  Google Scholar 

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Authors and Affiliations

  1. Shanghai Key Laboratory of Data Science, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Yan Yan, Zhaowei Tan, Xiaofeng Gao & Guihai Chen

  2. Department of Information Systems, The University of Texas at Dallas, Richardson, USA

    Shaojie Tang

Authors
  1. Yan Yan
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  2. Zhaowei Tan
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  3. Xiaofeng Gao
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  4. Shaojie Tang
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  5. Guihai Chen
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Corresponding author

Correspondence to Xiaofeng Gao .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology , Atlanta, Georgia, USA

    Shamkant B. Navathe

  2. University of Texas at Dallas , Richardson, Texas, USA

    Weili Wu

  3. University of Minnesota , Minneapolis, Minnesota, USA

    Shashi Shekhar

  4. Renmin University , Beijing, China

    Xiaoyong Du

  5. Fudan University , Shanghai, China

    Sean X. Wang

  6. Rutgers, The State University of New Jer , New Brunswick, New Jersey, USA

    Hui Xiong

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© 2016 Springer International Publishing Switzerland

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Yan, Y., Tan, Z., Gao, X., Tang, S., Chen, G. (2016). STH-Bass: A Spatial-Temporal Heterogeneous Bass Model to Predict Single-Tweet Popularity. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, S., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9643. Springer, Cham. https://doi.org/10.1007/978-3-319-32049-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-32049-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32048-9

  • Online ISBN: 978-3-319-32049-6

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