RSC: Mining and modeling temporal activity in social media

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

Volumn 2015-August, Issue , 2015, Pages 269-278

  Costa, Alceu Ferraz ^a   Yamaguchi, Yuto ^b   Traina, Agma Juci Machado ^a   Traina, Caetano ^a   Faloutsos, Christos ^c  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

^b UNIVERSITY OF TSUKUBA   (Japan)

^c Carnegie Mellon University ^*  (United States)

Author keywords

Generative model; Social media; Time series; User behavior

Indexed keywords

BEHAVIORAL RESEARCH; DATA MINING; TIME SERIES;

BIMODAL DISTRIBUTION; GENERATIVE MODEL; HUMAN COMMUNICATIONS; INTER-ARRIVAL TIME; POSITIVE CORRELATIONS; SOCIAL MEDIA; SOCIAL MEDIA SERVICES; USER BEHAVIORS;

SOCIAL NETWORKING (ONLINE);

EID: 84954155259     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2783258.2783294     Document Type: Conference Paper

References (28)

1. C. C. Aggarwal and P. S. Yu. Outlier detection for high dimensional data. SIGMOD, pages 37-46, 2001.
2. R. Baeza-Yates and B. Ribeiro-Neto. Modern information retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 2nd edition, 1999.
3. A. L. Barabasi. The origin of bursts and heavy tails in human dynamics. Nature, 435(7039):207-211, 2005.
4. V. Chandola, A. Banerjee, and V. Kumar. Anomaly detection: a survey. ACM Computing Surveys, 43(3):1-72, 2009.
5. J. Cho and H. Garcia-Molina. Estimating frequency of change. ACM Transactions on Internet Technology, 3:256-290, 2003.
6. Z. Chu, S. Gianvecchio, and H. Wang. Who is Tweeting on Twitter: Human, Bot, or Cyborg? In ACSAC, pages 21-30, 2010.
7. C. Drummond and R. C. Holte. Cost curves: An improved method for visualizing classifier performance. Machine Learning, 65:95-130, 2006.
8. J.-P. Eckmann, E. Moses, and D. Sergi. Entropy of dialogues creates coherent structures in e-mail traffic. PNAS, 101(7):14333-14337, 2004.
9. S. Günnemann, N. Günnemann, and C. Faloutsos. Detecting Anomalies in Dynamic Rating Data: a Robust Probabilistic Model for Rating Evolution. In KDD, pages 841-850, 2014.
10. C. A. Hidalgo R. Conditions for the emergence of scaling in the inter-event time of uncorrelated and seasonal systems. Physica A, 369(2):877-883, Sept. 2006.
11. P. G. Hoel, S. C. Port, and C. J. Stone. Introduction to Stochastic Processes. Waveland Pr. Inc., 1986.
12. A. Ihler, J. Hutchins, and P. Smyth. Adaptive event detection with time-varying poisson processes. In KDD, pages 207-216, 2006.
13. D. Juan, L. Li, H. Peng, D. Marculescu, and C. Faloutsos. Beyond Poisson: Modeling Inter-Arrival Time of Requests in a Datacenter. In PAKDD, Volume 1, pages 198-209, 2014.
14. M. Karsai, K. Kaski, A. Barabási, and J. Kertész. Universal features of correlated bursty behaviour. Scientific Reports, 2:1-7, 2012.
15. J. Kleinberg. Bursty and Hierarchical Structure in Streams. In KDD, pages 373-397, 2003.
16. N. C. Krishnan and D. J. Cook. Activity recognition on streaming sensor data. Pervasive and Mobile Computing, 10:138-154, 2014.
17. T. Lappas, M. R. Vieira, D. Gunopulos, and V. J. Tsotras. On the spatiotemporal burstiness of terms. In VLDB, pages 836-847, 2012.
18. R. D. Malmgren, J. M. Hofman, L. A. N. Amaral, and D. J. Watts. Characterizing Individual Communication Patterns. In KDD, pages 607-616, 2009.
19. R. D. Malmgren, D. B. Stouffer, A. S. L. O. Campanharo, and L. A. N. Amaral. On universality in human correspondence activity. Science, 325:1696-1700, 2009.
20. R. D. Malmgren, D. B. Stouffer, A. E. Motter, and L. A. N. Amaral. A Poissonian explanation for heavy tails in e-mail communication. PNAS, 105(47):18153-18158, 2008.
21. J. G. Oliveira and A. Barabási. Human dynamics: Darwin and Einstein correspondence patterns. Nature, 437:1251, 2005.
22. R. Ottoni, D. L. Casas, J. P. Pesce, W. Meira Jr., C. Wilson, A. Mislove, and V. Almeida. Of Pins and Tweets: Investigating How Users Behave Across Image-and Text-Based Social Networks, 2014.
23. K. C. Sia, J. Cho, and H. K. Cho. Efficient monitoring algorithm for fast news alerts. TKDE, 19(7):950-961, 2007.
24. M. Tsytsarau, T. Palpanas, and M. Castellanos. Dynamics of News Events and Social Media Reaction. In KDD, pages 901-910, 2014.
25. P. O. S. Vaz de Melo, C. Faloutsos, R. Assunção, R. Alves, and A. A. F. Loureiro. Universal and Distinct Properties of Communication Dynamics: How to Generate Realistic Inter-event Times. CoRR, abs/1403.4, 2014.
26. P. O. S. Vaz de Melo, C. Faloutsos, R. Assunção, and A. A. F. Loureiro. The Self-Feeding Process: A Unifying Model for Communication Dynamics in the Web. In WWW, pages 1319-1330, 2013.
27. Y. Wu, C. Zhou, J. Xiao, J. Kurths, and H. J. Schellnhuber. Evidence for a bimodal distribution in human communication. PNAS, 107:18803-18808, 2010.
28. C. M. Zhang and V. Paxson. Detecting and Analyzing Automated Activity on Twitter. Lecture Notes in Computer Science, 6579:102-111, 2011.