A bayesian approach for predicting the popularity of tweets

Annals of Applied Statistics

Volumn 8, Issue 3, 2014, Pages 1583-1611

  Zaman, Tauhid ^a   Fox, Emily B ^b   Bradlow, Eric T ^c  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Bayesian inference; Forecasting; Social networks; Time series; Twitter

Indexed keywords

EID: 84908265634     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/14-AOAS741     Document Type: Article

References (24)

1. AGARWAL, D., CHEN, B. and ELANGO, P. (2009). Spatial-temporal models for estimating clickthrough rates. Unpublished manuscript.
2. BAKSHY, E., HOFMAN, J. M., MASON, W. A. and WATTS, D. J. (2010). Everyone’s an influencer: Quantifying influence on Twitter. In Proc. WSDM. ACM, New York.
3. BANDARI, R., ASUR, S. and HUBERMAN, B. A. (2012). The pulse of news in social media: Forecasting popularity. In AAAI Conference on Weblogs and Social Media. AAAI, Dublin, Ireland.
4. BROWN, L., GANS, N., MANDELBAUM, A., SAKOV, A., SHEN, H., ZELTYN, S. and ZHAO, L. (2005). Statistical analysis of a telephone call center: A queueing-science perspective. J. Amer. Statist. Assoc. 100 36-50. MR2166068
5. CHA, M., HADDADI, H., BENEVENUTO, F. and GUMMADI, K. P. (2010). Measuring user influence in Twitter: The million follower fallacy. In Proc. AAAI Conf. on Weblogs and Social Media. AAAI, Washington, DC.
6. GELMAN, A. and HILL, H. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge Univ. Press, Cambridge.
7. GELMAN, A. and RUBIN, D. B. (1992). Inference from iterative simulation using multiple sequences. Statist. Sci. 7 457-472.
8. GOEL, S., WATTS, D. J. and GOLDSTEIN, D. G. (2012). The structure of online diffusion networks. In Proc. EC. ACM, New York.
9. HONG, L., DAN, O. and DAVISON, B. D. (2011). Predicting popular messages in Twitter. In Proceedings of the 20th International Conference Companion on World Wide Web 57-58. ACM, New York.
10. KWAK, H., LEE, C., PARK, H. and MOON, S. (2010). What is Twitter, a social network or a news media? In Proc. WWW. ACM, New York.
11. NAVEED, N., GOTTRON, T., KUNEGIS, J. and ALHADI, A. C. (2011). Bad news travels fast: A content-based analysis of interestingness on Twitter. In ACM Web Science. ACM, New York.
12. PETROVIC, S., OSBORNE, M. and LAVRENKO, V. (2011). RT to win! Prediction message popularity in Twitter. In AAAI Conference on Weblogs and Social Media. AAAI, Barcelona. Spain.
13. SPIEGELHALTER, D. J., BEST, N. G., CARLIN, B. P. and VAN DER LINDE, A. (2002). Bayesian measures of model complexity and fit. J. R. Stat. Soc. Ser. B Stat. Methodol. 64 583-639. MR1979380
14. STOUFFER, D. B., MALMGREN, R. D. and AMARAL, L. A. N. (2006). Log-normal statistics in e-mail communication patterns. Available at ArXiv:physics/0605027.
15. SUH, B., HONG, L., PIROLLI, P. and CHI, E. H. (2010). Want to be rewteeted? Large scale analysis on factors impacting retweet in Twitter network. In IEEE International Conference on Social Computing 177-184. IEEE, Minneapolis, MN.
16. SZABO, G. and HUBERMAN, B. A. (2010). Predicting the popularity of online content. Commun. ACM 8 80-88.
17. TWITTER (2012). Using the Twitter search API. Available at https://dev.twitter.com/docs/using-search.
18. ULRICH, R. and MILLER, J. (1993). Information processing models generating lognormally distributed reaction times. J. Math. Psych. 37 513-525.
19. US SECURITIES and EXCHANGE COMMISSION (2013). Twitter, Inc. Form S-1. Available at http://www.sec.gov/Archives/edgar/data/1418091/000119312513424260/d564001ds1a.htm.
20. VAN BREUKELEN, G. J. P. (1995). Theoretical note: Parallel information processing models compatible with lognormally distributed response times. J. Math. Psych. 39 396-399.
21. VU, D. Q., ASUNCION, A. U., HUNTER, D. R. and SMYTH, P. (2011). Dynamic egocentric models for citation networks. In International Conference on Machine Learning. ACM, New York.
22. ZAMAN, T., FOX, E. B. and BRADLOW, E. T. (2014). Supplement to “A Bayesian approach for predicting the popularity of tweetss.” DOI:10.1214/14-AOAS741SUPP.
23. ZAMAN, T., HERBRICH, R., GAEL, J. V. and STERN, D. (2010). Predicting information spreading in Twitter. In Proc. Workshop on Computational Social Science and the Wisdom of Crowds, NIPS. NIPS, Vancouver, Canada.
24. ZHOU, Z., BANDARI, R., KONG, J., QIAN, H. and ROYCHOWDHURY, V. (2010). Information resonance on Twitter: Watching Iran. In ACM Workshop on Social Media Analytics 123-131. ACM, New York.