1,559
Views
7
CrossRef citations to date
0
Altmetric
Applications and Case Studies

A Hierarchical Model of Nonhomogeneous Poisson Processes for Twitter Retweets

ORCID Icon &
Pages 1-15 | Received 06 Mar 2018, Accepted 16 Feb 2019, Published online: 30 Apr 2019

References

  • Antoniades, D., and Dovrolis, C. (2015), “Co-evolutionary Dynamics in Social Networks: A Case Study of Twitter,” Computational Social Networks, 2, 14. DOI: 10.1186/s40649-015-0023-6.
  • Baddeley, A., Rubak, E., and Turner, R. (2015), Spatial Point Patterns: Methodology and Applications With R, Interdisciplinary Statistics Series, Boca Raton, FL: Chapman & Hall/CRC.
  • Bao, P., Shen, H.-W., Jin, X., and Cheng, X.-Q. (2015), “Modeling and Predicting Popularity Dynamics of Microblogs Using Self-Excited Hawkes Processes,” in Proceedings of the 24th International Converence on World Wide Web, WWW ’15 Companion, ACM, New York, NY, USA, pp. 9–10. DOI: 10.1145/2740908.2742744.
  • Bar-Lev, S. K., Lavit, I., and Reiser, B. (1992), “Bayesian Inference for the Power Law Process,” Annals of the Institute of Statistical Mathematics, 44, 623–639. DOI: 10.1007/BF00053394.
  • Barabási, A.-L., and Albert, R. (1999), “Emergence of Scaling in Random Networks,” Science, 286, 509–512. doi: 10.1126/science.286.5439.509
  • Bhamidi, S., Steele, J. M., and Zaman, T. (2015), “Twitter Event Networks and the Superstar Model,” The Annals of Applied Probability, 25, 2462–2502. DOI: 10.1214/14-AAP1053.
  • Carlin, B. P., and Chib, S. (1995), “Bayesian Model Choice via Markov Chain Monte Carlo Methods,” Journal of Royal Statistical Society, Series B, 157, 473–484. DOI: 10.1111/j.2517-6161.1995.tb02042.x.
  • Chiodi, M., and Adelfio, G. (2017), “Mixed Non-parametric and Parametric Estimation Techniques in R Package etasFLP for Earthquakes’ Description,” Journal of Statistical Software, 76, 1–29. DOI: 10.18637/jss.v076.i03.
  • Cressie, N. A. C. (1993), Statistics for Spatial Data, Wiley Series in Probability and Statistics, New York: Wiley.
  • Cressie, N., and Wikle, C. K. (2011), Statistics for Spatio-Temporal Data, Wiley Series in Probability and Statistics, New York: Wiley.
  • Daley, D. J., and Vere-Jones, D. (2003), An Introduction to the Theory of Point Processes: Volume I: Elementary Theory and Methods, Probability and Its Applications, New York: Springer.
  • Daley, D. J., and Vere-Jones, D. (2008), An Introduction to the Theory of Point Processes: Volume II: General Theory and Structure, Probability and Its Applications, New York: Springer.
  • Dellaportas, P., Forster, J. J., and Ntzoufras, I. (2002), “On Bayesian Model and Variable Selection Using MCMC,” Statistics and Computing, 12, 27–36. DOI: 10.1023/A:1013164120801.
  • Diggle, P. J. (2013), Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Monographs on Statistics and Applied Probability, Boca Raton, FL: Chapman & Hall.
  • Duane, J. T. (1964), “Learning Curve Approach to Reliability Monitoring,” IEEE Transactions on Aerospace, 2, 563–566. DOI: 10.1109/TA.1964.4319640.
  • Farajtabar, M., Wang, Y., Rodriguez, M. G., Li, S., Zha, H., and Song, L. (2015), “Coevolve: A Joint Point Process Model for Information Diffusion and Network Co-evolution,” in Advances in Neural Information Processing Systems 28, NIPS 2015.
  • Gelfand, A. E., Diggle, P. J., Fuentes, M., and Guttorp, P., eds (2010), Handbook of Spatial Statistics, Handbooks of Modern Statistical Methods, Boca Raton, FL: Chapman & Hall/CRC.
  • Gelman, A., Meng, X., and Stern, H. (1996), “Posterior Predictive Assessment of Model Fitness via Realized Discrepancies,” Statistica Sinica, 6, 733–807.
  • Götz, M., Leskovec, J., McGlohon, M., and Faloutsos, C. (2009), “Modeling Blog Dynamics,” in ICWSM.
  • Green, P. J. (1995), “Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination,” Biometrika, 82, 711–732. DOI: 10.1093/biomet/82.4.
  • Hong, L., Doumith, A. S., and Davison, B. D. (2013), “Co-factorization Machines: Modeling User Interests and Predicting Individual Decisions in Twitter,” in Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, WSDM ’13, ACM, New York, NY, USA, pp. 557–566. DOI: 10.1145/2433396.2433467.
  • Illian, J., Penttinen, A., Stoyan, H., and Stoyan, D. (2008), Statistical Analysis and Modelling of Spatial Point Patterns, Chichester: Wiley.
  • Kumar, S., Liu, H., Mehta, S., and Subramaniam, L. V. (2014), “From Tweets to Events: Exploring a Scalable Solution for Twitter Streams,” arXiv no. 1405.1392. DOI: 10.1145/2808797.2809389.
  • Kumar, S., Liu, H., Mehta, S., and Subramaniam, L. V. (2015), “Exploring a Scalable Solution to Identifying Events in Noisy Twitter Streams,” in Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, ASONAM ’15, ACM, New York, NY, USA, pp. 496–499.
  • Li, H., Liu, J., Xu, K., and Wen, S. (2012), “Understanding Video Propagation in Online Social Networks,” in Proceedings of the 2012 IEEE 20th International Workshop on Quality of Service.
  • Li, P., Li, W., Wang, H., and Zhang, X. (2014), “Modeling of Information Diffusion in Twitter-Like Social Networks Under Information Overload,” The Scientific World Journal, 2014, 914907. DOI: 10.1155/2014/914907.
  • Lim, K. W., Chen, C., and Buntine, W. (2016), “Twitter-Network Topic Model: A Full Bayesian Treatment for Social Network and Text Modeling,” arXiv no. 1609.06791.
  • Mahmud, J., Chen, J., and Nichols, J. (2013), “When Will You Answer This? Estimating Response Time in Twitter,” in Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, Association for the Advancement of Artificial Intelligence, pp. 697–700.
  • Masuda, N., and Rocha, L. E. C. (2018), “A Gillespie Algorithm for Non-Markovian Stochastic Processes,” SIAM Review, 60, 95–115. DOI: 10.1137/16M1055876.
  • Mathews, P., Mitchell, L., Nguyen, G., and Bean, N. (2017), “The Nature and Origin of Heavy Tails in Retweet Activity,” in Proceedings of the 26th International Conference on World Wide Web Companion, WWW ’17 Companion, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, pp. 1493–1498. DOI: 10.1145/3041021.3053903.
  • Mathiesen, J., Angheluta, L., Ahlgren, P. T. H., and Jensen, M. H. (2013), “Excitable Human Dynamics Driven by Extrinsic Events in Massive Communities,” Proceedings of the National Academy of Sciences, 110, 17259–17262. DOI: 10.1073/pnas.1304179110.
  • Miotto, J. M., Kantz, H., and Altmann, E. G. (2017), “Stochastic Dynamics and the Predictability of Big Hits in Online Videos,” Physical Review E, 95, 032311.DOI: 10.1103/PhysRevE.95.032311.
  • Møller, J., Syversveen, A. R., and Waagepetersen, R. P. (1998), “Log Gaussian Cox Processes,” Scandinavian Journal of Statistics, 25, 451–482. DOI: 10.1111/1467-9469.00115.
  • Mollgaard, A., and Mathiesen, J. (2015), “Emergent User Behavior on Twitter Modelled by a Stochastic Differential Equation,” PLoS One, 10, 1–12. DOI: 10.1371/journal.pone.0123876.
  • Nishi, R., Takaguchi, T., Oka, K., Maehara, T., Toyoda, M., Kawarabayashi, K., and Masuda, N. (2016), “Reply Trees in Twitter: Data Analysis and Branching Process Models,” Social Network Analysis and Mining, 6, 1–13. DOI: 10.1007/s13278-016-0334-0.
  • Ogata, Y. (1988), “Statistical Models for Earthquake Occurrences and Residual Analysis for Point Processes,” Journal of the American Statistical Association, 83, 9–27. DOI: 10.1080/01621459.1988.10478560.
  • Perera, R. D. W., Anand, S., Subbalakshmi, K. P., and Chandramouli, R. (2010), “Twitter Analytics: Architecture, Tools and Analysis,” in 2010—MILCOM 2010 Military Communications Conference, pp. 2186–2191. DOI: 10.1109/MILCOM.2010.5680493.
  • Pozdnoukhov, A., and Kaiser, C. (2011), “Space-Time Dynamics of Topics in Streaming Text,” in Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN ’11, ACM, New York, NY, USA, pp. 1–8. DOI: 10.1145/2063212.2063223.
  • Reinhart, A. (2018), “A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications,” Statistical Science, 33, 299–318. DOI: 10.1214/17-STS629.
  • Sakaki, T., Okazaki, M., and Matsuo, Y. (2010), “Earthquake Shakes Twitter Users: Real-Time Event Detection by Social Sensors,” in Proceedings of the 19th International Conference on World Wide Web, WWW ’10, ACM, New York, NY, USA, pp. 851–860. DOI: 10.1145/1772690.1772777.
  • Sanli, C., and Lambiotte, R. (2015), “Local Variation of Collective Attention in Hashtag Spike Trains,” in Modeling and Mining Temporal Interactions: Papers From the 2015 ICWSM Workshop, AAAI Press, Palo Alto, California.
  • Shen, H., Wang, D., Song, C., and Barabási, A.-L. (2014), “Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes,” in Proceedings of the 28th AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence, pp. 291– 297.
  • Smid, H., Mast, P., Tromp, M., Winterboer, A., and Evers, V. (2011), “Canary in a Coal Mine: Monitoring Air Quality and Detecting Environmental Incidents by Harvesting Twitter,” in CHI ’11 Extended Abstracts on Human Factors in Computing Systems, CHI EA ’11, ACM, New York, NY, USA, pp. 1855–1860. DOI: 11245/1.356070.
  • Srijith, P. K., Lukasik, M., Bontcheva, K., and Cohn, T. (2017), “Longitudinal Modeling of Social Media With Hawkes Process Based on Users and Networks,” in Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ACM. DOI: 10.1145/3110025.3110107.
  • Sutton, J., Spiro, E. S., Johnson, B., Fitzhugh, S., Gibson, B., and Butts, C. T. (2014), “Warning Tweets: Serial Transmission of Messages During the Warning Phase of a Disaster Event,” Information, Communication & Society, 17, 765–787. DOI: 10.1080/1369118X.2013.862561.
  • Wu, M., Guo, J., Zhang, C., and Xie, J. (2011), “Social Media Communication Model Research Bases on Sina-Weibo,” in Knowledge Engineering and Management: Proceedings of the Sixth International Converence on Intelligent Systems and Knowledge Enginnering, Shanghai, China, Dec 2011 (ISKE2011), Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 445–454. DOI: 10.1007/978-3-642-25661-5_57.
  • Xie, J., Zhang, C., and Wu, M. (2011), “Modeling Microblogging Communication Based on Human Dynamics,” in 2011 Eighth International Converence on Fuzzy Systems and Knowledge Discovery (FSKD) (Vol. 4), pp. 2290–2294. DOI: 10.1109/FSKD.2011.6020045.
  • Yakovlev, G., Rundle, J. B., Shcherbakov, R., and Turcotte, D. L. (2005), “Inter-arrival Time Distribution for the Non-homogeneous Poisson Process,” arXiv no. 0507657.
  • Zhu, J., Xiong, F., Piao, D., Liu, Y., and Zhang, Y. (2011), “Statistically Modelling the Effectiveness of Disaster Information in Social Media,” in 2011 IEEE Global Humanitarian Technology Conference, pp. 431–436. DOI: 10.1109/GHTC.2011.48.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.