On predictability of rare events leveraging social media: A machine learning perspective

COSN 2015 - Proceedings of the 2015 ACM Conference on Online Social Networks

Volumn , Issue , 2015, Pages 3-13

  Le, Lei ^a   Ferrara, Emilio ^a   Flammini, Alessandro ^a  

^a INDIANA UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; FORECASTING; LEARNING SYSTEMS; MEDIA STREAMING; SPORTS;

ALTERNATIVE INFORMATION; EXTERNAL SOURCES; LEARNING FRAMEWORKS; PREDICTION PERFORMANCE; PREDICTIVE POWER; SENTIMENT ANALYSIS; SOCIAL MEDIA; WISDOM OF THE CROWDS;

SOCIAL NETWORKING (ONLINE);

EID: 84964059125     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2817946.2817949     Document Type: Conference Paper

References (32)

1. S. Asur and B. A. Huberman. Predicting the future with social media. In Web Intelligence and Intelligent Agent Technology (WI-IAT), volume 1, pages 492-499. IEEE, 2010.
2. A. Bakliwal, P. Arora, S. Madhappan, N. Kapre, M. Singh, and V. Varma. Mining sentiments from tweets. Proceedings of the WASSA, 2012.
3. A. Bermingham and A. F. Smeaton. On using Twitter to monitor political sentiment and predict election results. Sentiment Analysis where AI meets Psychology (SAAIP), page 2, 2011.
4. J. Bollen, H. Mao, and X. Zeng. Twitter mood predicts the stock market. Journal of Computational Science, 2(1):1-8, 2011.
5. A. Choudhary, W. Hendrix, K. Lee, D. Palsetia, and W.-K. Liao. Social media evolution of the egyptian revolution. Communications of the ACM, 55(5):74-80, 2012.
6. F. Ciulla, D. Mocanu, A. Baronchelli, B. Gonçalves, N. Perra, and A. Vespignani. Beating the news using social media: the case study of american idol. EPJ Data Science, 1(1):1-11, 2012.
7. M. D. Conover, C. Davis, E. Ferrara, K. McKelvey, F. Menczer, and A. Flammini. The geospatial characteristics of a social movement communication network. PloS one, 8(3):e55957, 2013.
8. M. D. Conover, E. Ferrara, F. Menczer, and A. Flammini. The digital evolution of occupy wall street. PloS one, 8(5):e64679, 2013.
9. J. DiGrazia, K. McKelvey, J. Bollen, and F. Rojas. More tweets, more votes: Social media as a quantitative indicator of political behavior. PloS one, 8(11):e79449, 2013.
10. E. Ferrara, O. Varol, F. Menczer, and A. Flammini. Traveling trends: social butteries or frequent iers? In Proceedings of the first ACM conference on Online social networks, pages 213-222. ACM, 2013.
11. D. Gayo-Avello. No, you cannot predict elections with Twitter. Internet Computing, 16(6):91-94, 2012.
12. D. Gayo-Avello, P. T. Metaxas, and E. Mustafaraj. Limits of electoral predictions using Twitter. In ICWSM, 2011.
13. A. Go, R. Bhayani, and L. Huang. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, pages 1-12, 2009.
14. J. W. Kim, D. Kim, B. Keegan, J. H. Kim, S. Kim, and A. Oh. Social media dynamics of global co-presence during the 2014 FIFA World Cup. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems. ACM, 2015.
15. D. Lazer, R. Kennedy, G. King, and A. Vespignani. The parable of Google u: Traps in big data analysis. Science, 343(6176):1203-1205, 2014.
16. W. A. Mason, F. R. Conrey, and E. R. Smith. Situating social inuence processes: Dynamic, multidirectional ows of inuence within social networks. Personality and social psychology review, 11(3):279-300, 2007.
17. M. Mathioudakis and N. Koudas. Twittermonitor: trend detection over the Twitter stream. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pages 1155-1158. ACM, 2010.
18. T. Nasukawa and J. Yi. Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the 2nd international conference on Knowledge capture, pages 70-77. ACM, 2003.
19. A. Oghina, M. Breuss, M. Tsagkias, and M. De Rijke. Predicting imdb movie ratings using social media. In Advances in information retrieval, pages 503-507. 2012.
20. B. Pang and L. Lee. Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2(1-2):1-135, 2008.
21. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830, 2011.
22. H. Saif, M. Fernandez, Y. He, and H. Alani. Evaluation datasets for Twitter sentiment analysis. In Proceedings, 1st Workshop on Emotion and Sentiment in Social and Expressive Media (ESSEM), 2013.
23. H. Saif, Y. He, and H. Alani. Semantic smoothing for Twitter sentiment analysis. 2011.
24. H. Saif, Y. He, and H. Alani. Alleviating data sparsity for twitter sentiment analysis. Making Sense of Microposts (# MSM2012), 2012.
25. H. Schoen, D. Gayo-Avello, P. T. Metaxas, E. Mustafaraj, M. Strohmaier, and P. Gloor. The power of prediction with social media. Internet Research, 23(5):528-543, 2013.
26. G. Shteynberg and E. P. Apfelbaum. The power of shared experience: Simultaneous observation with similar others facilitates social learning. Social Psychological and Personality Science, 4(6):738-744, 2013.
27. G. Shteynberg, J. B. Hirsh, E. P. Apfelbaum, J. T. Larsen, A. D. Galinsky, and N. J. Roese. Feeling more together: Group attention intensifies emotion. Emotion, 14(6):1102, 2014.
28. M. Speriosu, N. Sudan, S. Upadhyay, and J. Baldridge. Twitter polarity classification with label propagation over lexical links and the follower graph. In Proceedings of the First workshop on Unsupervised Learning in NLP, pages 53-63. ACL, 2011.
29. M. Thelwall, K. Buckley, and G. Paltoglou. Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62(2):406-418, 2011.
30. A. Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe. Predicting elections with Twitter: What 140 characters reveal about political sentiment. ICWSM, 10:178-185, 2010.
31. O. Varol, E. Ferrara, C. L. Ogan, F. Menczer, and A. Flammini. Evolution of online user behavior during a social upheaval. In Proceedings of the 2014 ACM conference on Web science, pages 81-90. ACM, 2014.
32. X. Zhang, H. Fuehres, and P. A. Gloor. Predicting stock market indicators through Twitter "I hope it is not as bad as I fear". Procedia-Social and Behavioral Sciences, 26:55-62, 2011.