Do cascades recur?

25th International World Wide Web Conference, WWW 2016

Volumn , Issue , 2016, Pages 671-681

  Cheng, Justin ^a   Adamic, Lada A ^b   Kleinberg, Jon ^c   Leskovec, Jure ^a  

^a STANFORD UNIVERSITY   (United States)

^b FACEBOOK   (United States)

^c Department of Obstetrics Gynecology   (United States)

Author keywords

Cascade prediction; Content recurrence; Information diffusion; Memes; Virality.

Indexed keywords

WORLD WIDE WEB;

COMPLEX PICTURES; CONTENT RECURRENCE; INFORMATION DIFFUSION; INFORMATION SHARING; LARGE-SCALE ANALYSIS; MEMES; PRIMARY MECHANISM; VIRALITY;

SOCIAL NETWORKING (ONLINE);

EID: 84988665001     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2872427.2882993     Document Type: Conference Paper

References (49)

1. L. A. Adamic, T. M. Lento, E. Adar, and P. C. Ng. Information evolution in social networks. WSDM, 2016.
2. M. Ahmed, S. Spagna, F. Huici, and S. Niccolini. A peek into the future: Predicting the evolution of popularity in user generated content. WSDM, 2013.
3. S. Altizer, A. Dobson, P. Hosseini, P. Hudson, M. Pascual, and P. Rohani. Seasonality and the dynamics of infectious diseases. Ecol. Lett., 2006.
4. S. Asur, B. Huberman, et al. Predicting the future with social media. WI-IAT, 2010.
5. S. Asur, B. Huberman, G. Szabo, and C. Wang. Trends in social media: Persistence and decay. ICWSM, 2011.
6. N. T. Bailey et al. The mathematical theory of infectious diseases and its applications. 1975.
7. E. Bakshy, I. Rosenn, C. Marlow, and L. A. Adamic. The role of social networks in information diffusion. WWW, 2012.
8. A.-L. Barabasi. The origin of bursts and heavy tails in human dynamics. Nature, 2005.
9. C. Bauckhage, F. Hadiji, and K. Kersting. How viral are viral videos? ICWSM, 2015.
10. C. Bauckhage, K. Kersting, and F. Hadiji. Mathematical models of fads explain the temporal dynamics of internet memes. ICWSM, 2013.
11. Y. Borghol, S. Ardon, N. Carlsson, D. Eager, and A. Mahanti. The untold story of the clones: content-Agnostic factors that impact Youtube video popularity. KDD, 2012.
12. Y. Borghol, S. Mitra, S. Ardon, N. Carlsson, D. Eager, and A. Mahanti. Characterizing and modelling popularity of user-generated videos. Perform. Eval., 2011.
13. M. Cha, F. Benevenuto, Y.-Y. Ahn, and K. P. Gummadi. Delayed information cascades in Flickr: Measurement, analysis, and modeling. Computer Networks, 2012.
14. J. Cheng, L. A. Adamic, P. A. Dow, J. M. Kleinberg, and J. Leskovec. Can cascades be predicted? WWW, 2014.
15. H. Choi and H. Varian. Predicting the present with Google Trends. Econ. Rec., 2012.
16. M. Coscia. Average is boring: How similarity kills a memes success. Sci. Rep., 2014.
17. R. Crane and D. Sornette. Robust dynamic classes revealed by measuring the response function of a social system. PNAS, 2008.
18. M. Fiedler. Algebraic connectivity of graphs. Czech. Math. J., 1973.
19. A. Friggeri, L. A. Adamic, D. Eckles, and J. Cheng. Rumor cascades. ICWSM, 2014.
20. M. Girvan, D. S. Callaway, M. E. Newman, and S. H. Strogatz. Simple model of epidemics with pathogen mutation. Phys. Rev. E, 2002.
21. Y. Gong and S. Lazebnik. Iterative quantization: A procrustean approach to learning binary codes. CVPR, 2011.
22. N. Grinberg, M. Naaman, B. Shaw, and G. Lotan. Extracting diurnal patterns of real world activity from social media. ICWSM, 2013.
23. D. Gruhl, R. Guha, D. Liben-Nowell, and A. Tomkins. Information diffusion through blogspace. WWW, 2004.
24. A. Guille and H. Hacid. A predictive model for the temporal dynamics of information diffusion in online social networks. WWW Companion, 2012.
25. A. Johansen. A simple model of recurrent epidemics. J. Theor. Biol., 1996.
26. D. Kifer, S. Ben-David, and J. Gehrke. Detecting change in data streams. VLDB, 2004.
27. J. Kleinberg. Bursty and hierarchical structure in streams. Data Min. Knowl. Discov., 2003.
28. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. NIPS, 2012.
29. R. Kumar, J. Novak, P. Raghavan, and A. Tomkins. On the bursty evolution of blogspace. WWW, 2005.
30. M. Kuperman and G. Abramson. Small world effect in an epidemiological model. Phys. Rev. Lett., 2001.
31. S. Kwon, M. Cha, K. Jung, W. Chen, and Y. Wang. Prominent features of rumor propagation in online social media. ICDM, 2013.
32. H. Lakkaraju, J. J. McAuley, and J. Leskovec. Whats in a name? understanding the interplay between titles, content, and communities in social media. ICWSM, 2013.
33. J. Leskovec, L. Backstrom, and J. Kleinberg. Meme-Tracking and the dynamics of the news cycle. KDD, 2009.
34. J. Leskovec, M. McGlohon, C. Faloutsos, N. S. Glance, and M. Hurst. Patterns of cascading behavior in large blog graphs. SDM, 2007.
35. D. Liben-Nowell and J. Kleinberg. Tracing information flow on a global scale using internet chain-letter data. PNAS, 2008.
36. Y. Matsubara, Y. Sakurai, B. A. Prakash, L. Li, and C. Faloutsos. Rise and fall patterns of information diffusion: model and implications. KDD, 2012.
37. S. A. Myers and J. Leskovec. The bursty dynamics of the twitter information network. WWW, 2014.
38. S. A. Myers, C. Zhu, and J. Leskovec. Information diffusion and external influence in networks. KDD, 2012.
39. M. E. Newman. Spread of epidemic disease on networks. Phys. Rev. E, 2002.
40. L. F. Olsen, G. L. Truty, and W. M. Schaffer. Oscillations and chaos in epidemics: A nonlinear dynamic study of six childhood diseases in Copenhagen, Denmark. Theor. Popul. Biol., 1988.
41. G. Palshikar et al. Simple algorithms for peak detection in time-series. ICADABAI, 2009.
42. P. R. Rosenbaum and D. B. Rubin. The central role of the propensity score in observational studies for causal effects. Biometrika, 1983.
43. B. H. Spitzberg. Toward a model of meme diffusion (M3D). Communication Theory, 2014.
44. G. Stoddard. Popularity dynamics and intrinsic quality in reddit and hacker news. ICWSM, 2015.
45. J. Verdasca, M. T. Da Gama, A. Nunes, N. Bernardino, J. Pacheco, and M. Gomes. Recurrent epidemics in small world networks. J. Theor. Biol., 2005.
46. L. Weng, A. Flammini, A. Vespignani, and F. Menczer. Competition among memes in a world with limited attention. Sci. Rep., 2012.
47. J. Yang and S. Counts. Predicting the speed, scale, and range of information diffusion in twitter. ICWSM, 2010.
48. J. Yang and J. Leskovec. Modeling information diffusion in implicit networks. ICDM, 2010.
49. J. Yang and J. Leskovec. Patterns of temporal variation in online media. WSDM, 2011.