How random are online social interactions?

Scientific Reports

Volumn 2, Issue , 2012, Pages

  Wang, Chunyan ^a   Huberman, Bernardo A ^b  

^a STANFORD UNIVERSITY   (United States)

^b HEWLETT PACKARD LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; HUMAN; INFORMATION SCIENCE; NONLINEAR SYSTEM; SOCIAL BEHAVIOR; SOCIAL MEDIA; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; DATA INTERPRETATION, STATISTICAL; HUMANS; INFORMATION THEORY; LIKELIHOOD FUNCTIONS; MODELS, BIOLOGICAL; NONLINEAR DYNAMICS; SOCIAL BEHAVIOR; SOCIAL MEDIA;

EID: 84866141924     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep00633     Document Type: Article

References (32)

1. Huberman, B.A. The Laws of the Web: Patterns in the Ecology of Information (The MIT Press, 2001).
2. Vázquez, A., Gama Oliveira, J., Dezsó, Z., Goh, K.-I., Kondor, I. & Barabási, A.-L. Modeling bursts and heavy tails in human dynamics. Physical Review E 73(3), 036127 (2006).
3. Golder, S. A.,Wilkinson, D. M. & Huberman, B. A. Rhythms of social interaction: messaging within a massive online network. In International Conference on Communities and Technologies, (2007).
4. Wu, F. & Huberman, B. A. Novelty and collective attention. Proc. Natl. Acad. Sci. 105 17599 (2007).
5. González, M. C., Hidalgo, C. A. & Barabási, A.-L. Understanding individual human mobility patterns. Nature (London) 453, 779 (2008).
6. Ratkiewicz, J., Fortunato, S., Flammini, A., Menczer, F. & Vespignani, A. Characterizing and modeling the dynamics of online popularity. Phys. Rev. Lett. 105(15), 158701 (2010).
7. Wu, Y., Zhou, C., Xiao, J., Kurths, J. & Schellnhuber, H. J. Evidence for a bimodal distribution in human communication. Proc. Natl. Acad. Sci. 107, 18803 (2010).
8. Golder, S. A. & Macy, M. W. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333 6051 (2011).
9. Huberman, B. A., Pirolli, P. L. T., Pitkow, J. E. & Lukose, R. M. Strong regularities in world wide web surfing. Science 280 95 (1998).
10. Tyler, J. R. & Tang, J. C. When can i expect an email response? a study of rhythms in email usage. In ECSCW, 239-258. Springer, (2003).
11. Gonalves, B., Perra, N. & Vespignani, A. Modeling users' activity on twitter networks: Validation of dunbar's number. PLoS ONE 6 8 (2011).
12. Scott, S. L. & Smyth, P. The markov modulated poisson process and markov poisson cascade with applications to web traffic data. Bayesian Statistics 7 (2003).
13. Eckmann, J., Moses, E. & Sergi, D. Entropy of dialogues creates coherent structures in e-mail traffic. Proc. Natl. Acad. Sci. 101 14333 (2004).
14. Barabasi, A. The origin of bursts and heavy tails in human dynamics. Nature 435 (2005).
15. Rybski, D., Buldyrev, S., Havlin, S., Liljeros, F. &Makse, H. Scaling laws of human interaction activity. Proc. Natl. Acad. Sci. 106 12640 (2009).
16. Crane, R. & Sornette, D. Robust dynamic classes revealed by measuring the response function of a social system. Proc. Natl. Acad. Sci. 105 15649 (2008).
17. Adar, E., Teevan, J. & Dumais, S. Resonance on the web: Web dynamics and revisitation patterns. In CHI, (2009).
18. Chmiel, A., Kowalska, K. & Holyst, J. A. Scaling of human behavior during portal browsing. Phys. Rev. E 80 066122 (2010).
19. Takaguchi, T., Nakamura, M., Sato, N., Yano, K. & Masuda, N. Predictability of conversation partners. Phys. Rev. X 1 011008 (2011).
20. Song, C., Qu, Z., Blumm, N. & Barabási, A.-L. Limits of predictability in human mobility. Science 327(5968), 1018-1021 (2010).
21. Page, L., Brin, S., Motwani, R. & Winograd, T. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford University, (1998).
22. Baluja, S., Seth, R., Sivakumar, D., Jing, Y., Yagnik, J., Kumar, S., Ravichandran, D. & Aly, M. Video suggestion and discovery for youtube: taking random walks through the view graph. In WWW, 895-904. ACM, (2008).
23. Archak, N., Mirrokni, V. S. & Muthukrishnan, S. Mining advertiser-specific user behavior using adfactors. In WWW, 31-40. ACM, (2010).
24. Lu, L. & Zhou, T. Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications 390(6), 1150-1170 (2011).
25. Chierichetti, F., Kumar, R., Raghavan, P. & Sarlós, T. Are web users really markovian? In WWW, 609-618. ACM, (2012).
26. Steuer, R. E., Kurths, J., Daub, C.O., Weise, J. & Selbig, J. The mutual information: Detecting and evaluating dependencies between variables. In ECCB, 231-240 (2002).
27. Wuchty, S., Jones, B. F. & Uzzi, B. The increasing dominance of teams in production of knowledge. Science 316(5827), 1036-1039 (2007). (Pubitemid 46804914)
28. Palla, G., Barabasi, A. & Vicsek, T. Quantifying social group evolution. Nature 446, 664-667 (2007). (Pubitemid 46606882)
29. Choudhury, M. D. Modeling and predicting group activity over time in on-line social media. In Hypertext 349-350 (2009).
30. Panzeri, S., Senatore, R., Montemurro, M. A. & Petersen, R. S. Correcting for the sampling bias problem in spike train information measures. J Neurophysiol 3(98), 1064-1072 (2007). (Pubitemid 47435863)
31. Panzeri, S.&Treves, A. Analytical estimates of limited sampling biases in different information measures. Network: Computation in Neural Systems 7, 87107 (1996).
32. Steeg, G. V. & Galstyan, A. Information transfer in social media. In WWW, 509-518. ACM, (2012).