Patterns of temporal variation in online media

Proceedings of the 4th ACM International Conference on Web Search and Data Mining, WSDM 2011

Volumn , Issue , 2011, Pages 177-186

  Yang, Jaewon ^a   Leskovec, Jure ^a  

^a STANFORD UNIVERSITY   (United States)

Author keywords

Social media; Time series clustering

Indexed keywords

ADAPTIVE WAVELETS; CLUSTER CENTROIDS; DIFFERENT TIME SCALE; HUMAN ATTENTION; INCREMENTAL APPROACH; K-MEANS CLUSTERING ALGORITHM; LARGE DATASETS; MASSIVE DATA SETS; NEWS MEDIA; ON-LINE CONTENTS; ONLINE MEDIA; REAL TIME; SIMILARITY MEASURE; SIMILARITY METRICS; SOCIAL MEDIA; TEMPORAL DYNAMICS; TEMPORAL PATTERN; TEMPORAL SHAPE; TEMPORAL VARIATION; TIME SERIES CLUSTERING; USER-GENERATED CONTENT;

CLUSTER ANALYSIS; DATA MINING; INFORMATION RETRIEVAL; SOCIAL NETWORKING (ONLINE); TIME SERIES; WORLD WIDE WEB;

CLUSTERING ALGORITHMS;

EID: 79952376390     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1935826.1935863     Document Type: Conference Paper

References (35)

1. Extended version of the paper. Patterns of temporal variation in online media. Technical Report, Stanford Infolab, 2010.
2. E. Adar, D. Weld, B. Bershad, and S. Gribble Why We Search: Visualizing and Predicting User Behavior. In WWW '07, 2007.
3. E. Adar, L. Zhang, L. A. Adamic, and R. M. Lukose. Implicit structure and the dynamics of blogspace. In Workshop on the Weblogging Ecosystem, 2004.
4. C. Aperjis, B. A. Huberman, and F. Wu. Harvesting collective intelligence: Temporal behavior in yahoo answers. ArXiv e-prints, Jan 2010.
5. L. Backstrom, J. Kleinberg, and R. Kumar. Optimizing web traffic via the media scheduling problem. In KDD '09, 2009.
6. A.-L. Barabási. The origin of bursts and heavy tails in human dynamics. Nature, 435: 207, 2005.
7. F. K.-P. Chan, A. W. chee Fu, and C. Yu. Haar wavelets for efficient similarity search of time-series: With and without time warping. IEEE TKDE, 15(3):686-705, 2003.
8. S. Chien and N. Immorlica. Semantic Similarity between Search Engine Queries Using Temporal Correlation. In WWW '05, 2005.
9. K. K. W. Chu and M. H. Wong. Fast time-series searching with scaling and shifting. In PODS '99, 237-248, 1999.
10. R. Crane and D. Sornette. Robust dynamic classes revealed by measuring the response function of a social system. PNAS, 105(41): 15649-15653, October 2008.
11. H. Ding, G. Trajcevski, P. Scheuermann, X. Wang, and E. Keogh. Querying and mining of time series data: experimental comparison of representations and distance measures. VLDB., 1(2): 1542-1552, 2008.
12. G. H. Golub and C. F. Van Loan. Matrix computations (3rd ed.). Johns Hopkins University Press, 1996.
13. D. Gruhl, R. Guha, R. Kumar, J. Novak, and A. Tomkins. The predictive power of online chatter. In KDD '05, 2005.
14. D. Gruhl, D. Liben-Nowell, R. V. Guha, and A. Tomkins. Information diffusion through blogspace. In WWW, 2004.
15. A. Java, X. Song, T. Finin, and B. Tseng. Why we twitter: understanding microblogging usage and communities. In WebKDD workshop, pages 56-65. 2007.
16. E. Katz and P. Lazarsfeld. Personal influence: The part played by people in the flow of mass communications. Free Press, 1955.
17. L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis (Wiley Series in Probability and Statistics). Wiley-Interscience, March 2005.
18. E. Keogh and C. Ratanamahatana. Exact indexing of dynamic time warping. Knowledge and Information Systems, 7(3): 358-386, 2005. (Pubitemid 40347209)
19. A. Krause, J. Leskovec, and C. Guestrin. Data association for topic intensity tracking. In ICML '06, 2006.
20. M. Kumar, N. R. Patel, and J. Woo. Clustering seasonality patterns in the presence of errors. In KDD '02, 2002.
21. R. Kumar, J. Novak, P. Raghavan, and A. Tomkins. On the bursty evolution of blogspace. In WWW '02, 2003.
22. J. Leskovec, L. Backstrom, and J. Kleinberg. Meme-tracking and the dynamics of the news cycle. In KDD '09, 2009.
23. J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. Glance. Cost-effective outbreak detection in networks. In KDD '07, 2007.
24. J. Leskovec, M. McGlohon, C. Faloutsos, N. Glance, and M. Hurst. Cascading behavior in large blog graphs. In SDM '07, 2007.
25. J. Lin, E. Keogh, S. Lonardi, and B. Chiu. A symbolic representation of time series, with implications for streaming algorithms. In SIGMOD '03, 2003.
26. R. D. Malmgren, D. B. Stouffer, A. E. Motter, and L. A. A. N. Amaral. A poissonian explanation for heavy tails in e-mail communication. PNAS, 105(47):18153-18158, 2008.
27. Q. Mei, C. Liu, H. Su, and C. Zhai. A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In WWW '06, 2006.
28. J. L. Michail, J. Lin, M. Vlachos, E. Keogh, and D. Gunopulos. Iterative incremental clustering of time series. In EDBT, 2004.
29. G. Salton and M. J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, 1986.
30. G. Szabo and B. A. Huberman. Predicting the popularity of online content. ArXiv e-prints, Nov 2008.
31. X. Wang, C. Zhai, X. Hu, and R. Sproat. Mining correlated bursty topic patterns from coordinated text streams. In KDD '07, page 793, 2007.
32. T. Warren Liao. Clustering of time series data - a survey. Pattern Recognition, 38(11):1857-1874, 2005.
33. D. J. Watts and P. S. Dodds. Influentials, networks, and public opinion formation. Journal of Consumer Research, 34(4): 441-458, December 2007.
34. F. Wu and B. A. Huberman. Novelty and collective attention. PNAS, 104(45):17599-17601, 2007.
35. S. Yardi, S. A. Golder, and M. J. Brzozowski. Blogging at work and the corporate attention economy. In CHI '09, 2009.