Quantifying herding effects in crowd wisdom

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

Volumn , Issue , 2014, Pages 1087-1096

  Wang, Ting ^a   Wang, Dashun ^a   Wang, Fei ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

crowd wisdom; herding effect; social influence

Indexed keywords

EID: 84907032776     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2623330.2623720     Document Type: Conference Paper

References (27)

1. N. A. Christakis and J. H. Fowler. The spread of obesity in a large social network over 32 years. New England journal of medicine, 357(4):370-379, 2007.
2. N. A. Christakis and J. H. Fowler. The collective dynamics of smoking in a large social network. New England journal of medicine, 358(21):2249-2258, 2008.
3. P. Cui, S. Jin, L. Yu, F. Wang, W. Zhu, and S. Yang. Cascading outbreak prediction in networks: A data-driven approach. In KDD, 2013.
4. A. Das, S. Gollapudi, R. Panigrahy, and M. Salek. Debiasing social wisdom. In KDD, 2013.
5. A. P. Dawid and A. M. Skene. Maximum likelihood estimation of observer error-rates using the em algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1):20-28, 1979.
6. J. Eisenstein, A. Ahmed, and E. P. Xing. Sparse additive generative models of text. In ICML, 2011.
7. J. H. Fowler, N. A. Christakis, Steptoe, and D. Roux. Dynamic spread of happiness in a large social network: longitudinal analysis of the framingham heart study social network. BMJ: British medical journal, pages 23-27, 2009.
8. F. Galton. Vox populi. Nature, 75(7):450, 1907.
9. G. Ganu, N. Elhadad, and A. Marian. Beyond the stars: Improving rating predictions using review text content. In WebDB, 2009.
10. M. Hu and B. Liu. Mining and summarizing customer reviews. In KDD, 2004.
11. S. Judd, M. Kearns, and Y. Vorobeychik. Behavioral dynamics and influence in networked coloring and consensus. Proceedings of the National Academy of Sciences, 107(34):14978-14982, 2010.
12. B. Krishnapuram, L. Carin, M. A. T. Figueiredo, and A. J. Hartemink. Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE Trans. Pattern Anal. Mach. Intell., 27(6):957-968, June 2005.
13. D. Liben-Nowell and J. Kleinberg. Tracing information flow on a global scale using internet chain-letter data. Proceedings of the National Academy of Sciences, 105(12):4633-4638, 2008.
14. J. Lorenz, H. Rauhut, F. Schweitzer, and D. Helbing. How social influence can undermine the wisdom of crowd effect. Proceedings of the National Academy of Sciences, 108(22):9020-9025, 2011.
15. J. McAuley and J. Leskovec. Hidden factors and hidden topics: Understanding rating dimensions with review text. In RecSys, 2013.
16. L. Muchnik, S. Aral, and S. J. Taylor. Social influence bias: A randomized experiment. Science, 341(6146):647-651, 2013.
17. J. W. Pillow, J. Shlens, L. Paninski, A. Sher, A. M. Litke, E. J. Chichilnisky, and E. P. Simoncelli. Spatio-temporal correlations and visual signaling in a complete neuronal population. Nature, 454(7206):995-999, 2008.
18. C. P. Robert and G. Casella. Monte Carlo Statistical Methods (Springer Texts in Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2005.
19. M. J. Salganik, P. S. Dodds, and D. J. Watts. Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762):854-856, 2006.
20. V. S. Sheng, F. Provost, and P. G. Ipeirotis. Get another label? improving data quality and data mining using multiple, noisy labelers. In KDD, 2008.
21. H. Sun, A. Morales, and X. Yan. Synthetic review spamming and defense. In KDD, 2013.
22. J. Surowiecki. The Wisdom of Crowds. Anchor, 2005.
23. D. Wang, C. Song, and A.-L. Barabási. Quantifying long-term scientific impact. Science, 342(6154):127-132, 2013.
24. D. Wang, Z. Wen, H. Tong, C.-Y. Lin, C. Song, and A.-L. Barabási. Information spreading in context. In WWW, 2011.
25. T. Wang, M. Srivatsa, D. Agrawal, and L. Liu. Microscopic social influence. In SDM, 2012.
26. D. Zhou, J. C. Platt, S. Basu, and Y. Mao. Learning from the wisdom of crowds by minimax entropy. In NIPS, 2012.
27. K. Zhou, H. Zha, and L. Song. Learning triggering kernels for multi-dimensional hawkes processes. In ICML, 2013.