From user comments to on-line conversations

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2012, Pages 244-252

  Wang, Chunyan ^a   Ye, Mao ^b   Huberman, Bernardo A ^b  

^a STANFORD UNIVERSITY   (United States)

^b HEWLETT PACKARD LABORATORIES   (United States)

Author keywords

conversation dynamics; social networks

Indexed keywords

EMPIRICAL MEASUREMENT; GROWTH DYNAMICS; SOCIAL MEDIA; SOCIAL NETWORKS; USER BEHAVIORS;

BEHAVIORAL RESEARCH; DATA MINING; DYNAMICS;

SOCIAL NETWORKING (ONLINE);

EID: 84866043286     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2339530.2339573     Document Type: Conference Paper

References (36)

1. R. Kumar, M. Mahdian, and M. McGlohon. Dynamics of conversations. In KDD, 2010.
2. Guimer Roger, Uzz Brian, Spiro Jarrett, and Amaral Lus A. Nunes. Team assembly mechanisms determine collaboration network structure and team performance. Science, 308(5722), 2005.
3. Wuchty Stefan, Jones Benjamin, and Uzzi Brian. The increasing dominance of teams in production of knowledge. Science, 316(5827), 2007.
4. B. J. Jansen, M. Zhang, K. Sobel, and A. Chowdhury. Twitter power: Tweets as electronic word of mouth. J. Am. Soc. Inf. Sci., 60(11), 2009.
5. D. Gruhl, R.Guha, R. Kumar, J. Novak, and A. Tomkins. The predictive power of online chatter. In KDD, 2005.
6. M. Kearns, S. Suri, and N. Monfort. An experimental study of the coloring problem on human subject networks. Science, 313, 2006.
7. G. Mishne and N. Glance. Leave a reply: An analysis of weblog comments. In Third Annual Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics., 2006.
8. V. Gomez, H. Kappen, and A. Kaltenbrunner. Modeling the structure and evolution of discussion cascades. In HT, 2011.
9. V. Gomez, A. Kaltenbrunner, and V. Lopez. Statistical analysis of the social network and discussion threads in slashdot. In WWW, 2008.
10. P. Ogilvie. Modeling blog post comment counts. In http://livewebir.com/ blog/page/2/, 2008.
11. M. Tsagkias, W. Weerkamp, and M. de Rijke. Predicting the volume of comments on online news stories. In CIKM, 2009.
12. David Liben-Nowell and Jon Kleinberg. Tracing information flow on a global scale using internet chain-letter data. Proc. Natl. Acad. Sci., 105(12), 2008.
13. Benjamin Goluba and Matthew O. Jackson. Using selection bias to explain the observed structure of internet diffusions. Proc. Natl. Acad. Sci., 107(24), 2010.
14. J. Leskovec, L. Backstrom, and J. Kleinberg. Meme-tracking and the dynamics of the news cycle. In KDD, 2009.
15. F. Duarte, B. Mattos, A. Bestravras, V. Almedia, and J. Almedia. Traffic characteristics and communication patterns in blogosphere. In ICWSM, 2007.
16. M. De Choudhury, H. Sundaram, A. John, and D. Duncan Seligmann. What makes conversations interesting? themes, participants and consequences of conversations in online social media. In WWW, 2009.
17. B. A. Huberman. The laws of the web: Patterns in the ecology of information. The MIT Press, 2001.
18. Josef Falkinger. Limited attention as a scarce resource in information-rich economies. Economic Journal, 118(532), 2008.
19. Eugene Agichtein, Carlos Castillo, Debora Donato, Aristides Gionis, and Gilad Mishne. Finding high-quality content in social media. In WSDM, 2008.
20. Fang Wu and B. A. Huberman. Novelty and collective attention. Proc. Natl. Acad. Sci., 105(17599), 2007.
21. Kristina Lerman and Tad Hogg. Using a model of social dynamics to predict popularity of news. In WWW, 2010.
22. R. Crane and D. Sornette. Robust dynamic classes revealed by measuring the response function of a social system. Proc. Natl. Acad. Sci., 105(15649), 2008.
23. Jacob Ratkiewicz, Santo Fortunato, Alessandro Flammini, Filippo Menczer, and Alessandro Vespignani. Characterizing and modeling the dynamics of online popularity. Phys. Rev. Lett., 105(158701), 2010.
24. Sitaram Asur, B. A. Huberman, Gabor Szabo, and Chunyan Wang. Predicting the volume of comments on online news stories. In ICWSM, 2011.
25. A.L. Barabasi. The origin of bursts and heavy tails in human dynamics. Nature, 435, 2005.
26. J.P. Eckmann, E. Moses, and D. Sergi. Entropy of dialogues creates coherent structures in e-mail traffic. Proc. Natl. Acad. Sci., 101(14333), 2004.
27. D. Rybski, S. Buldyrev, S. Havlin, F. Liljeros, and H. Makse. Scaling laws of human interaction activity. Proc. Natl. Acad. Sci., 106(12640), 2009.
28. B. A. Huberman, P. L. T. Pirolli, J. E. Pitkow, and R. M. Lukose. Social attention in the age of the web. Science, 280(95), 1998.
29. Eytan Adar, Jaime Teevan, and Susan Dumais. Resonance on the web: Web dynamics and revisitation patterns. In CHI, 2009.
30. Anna Chmiel, Kamila Kowalska, and Janusz A. Holyst. Scaling of human behavior during portal browsing. Phys. Rev. E, 80(066122), 2010.
31. S. L. Scott and P. Smyth. The markov modulated poisson process and markov poisson cascade with applications to web traffic data. Bayesian Statistics, 7, 2003.
32. Juliette Stehle, Alain Barrat, and Ginestra Bianconi. Dynamical and bursty interactions in social networks. Phys. Rev. E, 81(035101), 2010.
33. D. Helbing and B. Tilch. A power law for the duration of high-flow states and its interpretation from a heterogeneous traffic flow perspective. The European Physical Journal B, 68(4), 2009.
34. A. Clauset, C.R. Shalizi, and M.E.J. Newman. Power-law distributions in empirical data. SIAM Review, 51(4), 2009.
35. Erhan Ćinlar. Introduction to stochastic processes. Prentice-Hall, Englewood Cliffs, NJ, 1975.
36. G. Yule. A mathematical theory of evolution based on the conclusions of dr. j.c. willis f.r.s. Philosophical Transactions of the Royal Society London, 213(21-87), 1924.