The power of prediction with social media

Internet Research

Volumn 23, Issue 5, 2013, Pages 528-543

  Schoen, Harald ^a   Gayo Avello, Daniel ^b   Takis Metaxas, Panagiotis ^c   Mustafaraj, Eni ^d   Strohmaier, Markus ^e   Gloor, Peter ^f  

^a UNIVERSITY OF BAMBERG   (Germany)

^b UNIVERSITY OF OVIEDO   (Spain)

^c HARVARD UNIVERSITY   (United States)

^d WELLESLEY COLLEGE   (United States)

^e GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

^f MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Computational social science; Forecasting; Predicting; Social media

Indexed keywords

EID: 84885455972     PISSN: 10662243     EISSN: None     Source Type: Journal    
DOI: 10.1108/IntR-06-2013-0115     Document Type: Article

References (77)

1. Antweiler, W. and Frank, M.Z. (2004), “Is all that talk just noise? The information content of internet stock message boards”, The Journal of Finance, Vol. LIX No. 3, pp. 1259-1294.
2. Arrow, K.J., Forsythe, R., Gorham, M., Hahn, R., Hanson, R., Ledyard, J.O., Levmore, S., Litan, R., Milgrom, P., Nelson, F.D., Neumann, G.R., Ottaviani, M., Schelling, T.C., Shiller, R.J., Smith, V.L., Snowberg, E., Sunstein, C.R., Tetlock, P.C., Tetlock, P.E., Varian, H.R., Wolfers, J. and Zitzewitz, E. (2008), “The promise of prediction markets”, Science, Vol. 320 No. 5878, pp. 877-878.
3. Asur, S. and Huberman, B. (2010), “Predicting the future with social media”, Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Toronto, August 31-September 3.
4. Berlemann, M. and Schmidt, C. (2001), “Predictive accuracy of political stock markets: empirical evidence from a European perspective”, Discussion Papers No. 57, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
5. Bishop, C.M. (2006), Pattern Recognition and Machine Learning, Springer, New York, NY.
6. Bollen, J., Mao, H. and Zeng, X.-J. (2011a), “Twitter mood predicts the stock market”, Journal of Computational Science, Vol. 2 No. 1, pp. 1-8, available at: http://dx.doi.org/10.1016/j.jocs.2010.12.007.
7. Bollen, J., Pepe, A. and Mao, H. (2011b), “Modeling public mood and emotion: twitter sentiment and socio-economic phenomena”, Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, Barcelona, July 17-21.
8. Brandt, P.T., Freeman, J.R. and Schrodt, P.A. (2011), “Real time, time series forecasting of inter- and intra-state political conflict”, Conflict Management and Peace Science, Vol. 28 No. 1, pp. 58-80.
9. Campbell, J.E. (2008), “The trial-heat forecast of the 2008 presidential vote: performance and value considerations in an open-seat election”, PS: Political Science and Politics, Vol. 41 No. 4, pp. 697-701.
10. Campbell, J.E. and Garand, J. (Eds) (2000), Before the Vote: Forecasting American National Elections, Sage Publications Inc, Thousand Oaks, CA.
11. Castillo, C., Mendoza, M. and Poblete, B. (2013), “Predicting information credibility in time-sensitive social media”, Internet Research, Vol. 23 No. 5, pp. 560-588.
12. Choi, H. and Varian, H. (2009), “Predicting the present with google trends”, Google TR, available at: www.google.com/googleblogs/pdfs/google_predicting_the_present.pdf (accessed June 18, 2013).
13. Choudhury, M., Gamon, M., Counts, S. and Horvitz, E. (2013), “Predicting depression via social media”, Proceedings of the 7th International AAAI Conference on Weblogs and Social Media, Boston, MA, July 8-11.
14. Choudhury, M., Sundaram, H., John, A. and Seligmann, D. (2008), “Can blog communication dynamics be correlated with stock market activity?”, Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia, pp. 55-60.
15. Clements, M.P. and Hendry, D.F. (Eds) (2011), The Oxford Handbook of Economic Forecasting, Oxford University Press, Oxford.
16. Erikson, R.S. and Wlezien, C. (2008a), “Are political markets really superior to polls as election predictors?”, Public Opinion Quarterly, Vol. 72 No. 2, pp. 190-215.
17. Erikson, R.S. and Wlezien, C. (2008b), “Leading economic indicators, the polls, and the presidential vote”, PS: Political Science and Politics, Vol. 41 No. 4, pp. 703-707.
18. Eysenbach, G. (2006), “Infodemiology: tracking flu-related searches on the web for syndromic surveillance”, Proceedings of AMIA Symposium, pp. 244-248.
19. Forsythe, R., Nelson, F.D., Neumann, G.R. and Wright, J. (1992), “Anatomy of an experimental political stock market”, American Economic Review, Vol. 82 No. 5, pp. 1142-1163.
20. Franch, F. (2013), “(Wisdom of the crowds)2: 2010 UK election prediction with social media”, Journal of Information Technology and Politics, Vol. 10 No. 1, pp. 57-71.
21. Gauntlett, D. (2011), Making is Connecting: The Social Meaning of Creativity, from DIY and Knitting to YouTube and Web 2.0, Polity, Cambridge.
22. Gayo-Avello, D. (2013), “A meta-analysis of state-of-the-art electoral prediction from Twitter data”, Social Science Computer Review, published online before print August 23, doi:10.1177/0894439313493979.
23. Gilbert, E. and Karahalios, K. (2010), “Widespread worry and the stock market”, Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, Washington, DC, May 23-26.
24. Gil, R. and Levitt, S.D. (2007), “Testing the efficiency of markets in the 2002 World Cup”, The Journal of Prediction Markets, Vol. 1 No. 3, pp. 255-270.
25. Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S. and Brilliant, L. (2009), “Detecting influenza epidemics using search engine query data”, Nature, Vol. 457 No. 19, pp. 1012-1014.
26. Goel, S., Hofman, J., Lahaie, S., Pennock, D. and Watts, D. (2010), “Predicting consumer behavior with web search”, PNAS, Vol. 107 No. 41, pp. 17486-17490.
27. Golbeck, J., Robles, C., Edmonson, M. and Turner, K. (2011), “Predicting personality from Twitter”, Proceedings of PASSAT/SocialCom, pp. 149-156.
28. Granger, C.W.J., Elliott, G. and Timmermann, A. (Eds) (2006), Handbook of Economic Forecasting, North-Holland, Amsterdam.
29. Granka, L. (2013), “Using online search traffic to predict US presidential elections”, PS: Political Science and Politics, Vol. 46 No. 1, pp. 271-279.
30. Gruhl, D., Guha, R., Kumar, R., Novak, J. and Tomkins, A. (2005), “The predictive power of online chatter”, Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 78-77.
31. Hargittai, E. and Hinnant, A. (2008), “Digital inequality: differences in young adults' use of the internet”, Communication Research, Vol. 35 No. 5, pp. 602-621.
32. Hawkins, J. (2004), On Intelligence ISBN: 0805074562, Times books, New York, NY.
33. Hendry, D.F. and Ericsson, N.R. (Eds) (2001), Understanding Economic Forecasts, MIT Press, Cambridge, MA.
34. Hibbs, D.A. (2008), “Implications of the ‘bread and peace’ model for the 2008 US presidential election”, Public Choice, Vol. 137 Nos 1-2, pp. 1-10.
35. Holbrook, T.M. (2008), “Incumbency, national conditions, and the 2008 presidential election”, PS: Political Science and Politics, Vol. 41 No. 4, pp. 709-712.
36. Jacobson, B., Potters, J., Schram, A., van Winden, F. and Wit, J. (2000), “(In)accuracy of a European political stock market. The influence of common value structures”, European Economic Review, Vol. 44 No. 2, pp. 205-230.
37. Janetzko, D. (2008), “Nonreactive data collection on the internet”, Fielding, N.G., Raymond M.L. and Blank G. (Eds), The Sage Handbook of Online Research Methods, Sage, London, pp. 161-174.
38. Jungherr, A. and Jürgens, P. (2013), “Forecasting the pulse: how deviations from regular patterns in online data can identify offline phenomena”, Internet Research, Vol. 23 No. 5, pp. 589-607.
39. Jungherr, A., Jürgens, P. and Schoen, H. (2012), “Why the pirate party won the German Election of 2009 or the trouble with predictions: a response to Tumasjan, A., Sprenger, T. O., Sander, P.G., and Welpe, I.M. ‘Predicting Elections With Twitter: What 140 Characters Reveal About Political Sentiment'”, Social Science Computer Review, Vol. 30 No. 2, pp. 229-234.
40. Kalampokis, E., Tambouris, E. and Tarabanis, K. (2013), “Understanding the predictive power of social media”, Internet Research, Vol. 23 No. 5, pp. 544-559.
41. King, G. (1995), “Replication, replication”, PS: Political Science and Politics, Vol. 28 No. 3, pp. 444-452.
42. Lampos, V. and Cristianini, N. (2010), “Tracking the flu pandemic by monitoring the Social Web”, Proceedings of the 2nd IAPR Workshop on Cognitive Information Processing (CIP), IEEE Press, pp. 411-416.
43. Lampos, V. and Cristianini, N. (2012), “Nowcasting events from the social web with statistical learning”, ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 3 No. 4, Article no. 72.
44. Lampos, V., Preotiuc-Pietro, D. and Cohn, T. (2013), “A user-centric model of voting intention from social media”, Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, ACL.
45. Lewis-Beck, M.S. and Rice, T.W. (1992), Forecasting Elections, Congressional Quarterly Press, Washington, DC.
46. Liu, B. (2012), Sentiment Analysis and Opinion Mining, Morgan and Claypool Publishers, San Rafael, CA.
47. Lui, C., Metaxas, P. and Mustafaraj, E. (2011), “On the predictability of the US elections through search volume activity”, Proceedings of the e-Society Conference, Avila, March 10-13.
48. Metaxas, P. and Mustafaraj, E. (2010), “From obscurity to prominence in minutes: political speech and real-time search”, Proceedings of the WebScience10: Extending the Frontiers of Society On-Line, Raleigh, NC, April 26-27.
49. Metaxas, P. and Mustafaraj, E. (2012), “Social media and elections”, Science, Policy Forum, October 26.
50. Mishne, G. and Glance, N. (2006), “Predicting movie sales from blogger sentiment”, AAAI 2006 Spring Symposium on Computational Approaches to Analysing Weblogs, Palo Alto, CA, March 27-29.
51. Montgomery, J.M., Hollenbach, F.M. and Ward, M.D. (2012), “Improving predictions using ensemble Bayesian model averaging”, Political Analysis, Vol. 20 No. 3, pp. 271-291.
52. Mustafaraj, E., Finn, S., Whitlock, C. and Metaxas, P. (2011), “Vocal minority versus silent majority: discovering the opinions of the long tail”, Proceedings of IEEE PASSAT/SocialCom, pp. 103-110.
53. O'Connor, B., Balasubramanyan, R., Routledge, B.R. and Smith, N.A. (2010), “From tweets to polls: linking text sentiment to public opinion time series”, ICWSM, Vol. 11 pp. 122-129.
54. O'Connor, P. and Feng, Z. (2008), “The tradesports NFL prediction market: an analysis of market efficiency, transaction costs, and bettor preferences”, The Journal of Prediction Markets, Vol. 2 No. 1, pp. 45-71.
55. O'Reilly, T. (2005), “What is Web 2.0: design patterns and business models for the next generation of software”, O'Reilly Blog, September 30, available at: http://oreilly.com/web2/archive/what-is-web-20.html (accessed January 16, 2012).
56. Pang, B. and Lee, L. (2008), “Opinion mining and sentiment analysis”, Foundations and Trends in Information Retrieval, Vol. 2 Nos 1-2, pp. 1-135.
57. Pennacchiotti, M. and Popescu, A.M. (2011), “A machine learning approach to twitter user classification”, Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, July 17-21.
58. Pennock, D.M., Lawrence, S., Giles, C.L. and Nielsen, F.A. (2001), “The real power of artificial markets”, Science, Vol. 291 No. 5506, pp. 987-988.
59. Perry, P. (1979), “Certain problems in election survey methodology”, Public Opinion Quarterly, Vol. 43 No. 3, pp. 312-325.
60. Polgreen, P.M., Nelson, F.D. and Neumann, G.R. (2007), “Use of prediction markets to forecast infectious disease activity”, Healthcare Epidemiology, Vol. 47 No. 11, pp. 272-279.
61. Polgreen, P.M., Chen, Y., Pennock, D.M. and Nelson, F.D. (2008), “Using internet searches for influenza surveillance”, Clinical Infectious Diseases, Vol. 47 No. 11, pp. 1443-1448.
62. Qiu, L., Rui, H. and Whinston, A.B. (2011), “A Twitter-based prediction market: social network approach”, available at: http://ssrn.com/abstract=2047846, http://dx.doi.org/10.2139/ssrn.2047846 (accessed December 1, 2011).
63. Quercia, D., Kosinski, M., Stillwell, D. and Crowcroft, J. (2011), “Our twitter profiles, ourselves: predicting personality with twitter”, Proceedings of PASSAT/SocialCom, Boston, MA, October 9-11.
64. Ratkiewicz, J., Conover, M., Meiss, M., Gonçalves, B., Flammini, A. and Menczer, F. (2011), “Detecting and tracking political abuse in social media”, ICWSM.
65. Rattinger, H. and Ohr, D. (1989), “Wahlprognosen in einer Welt ohne Stichprobenfehler: Analytische Überlegungen und empirische Befunde”, in Falter, J.W., Rattinger, H., and Troitzsch, K.-G. (Eds), Wahlen und politische Einstellungen in der Bundesrepublik Deutschland: Neuere Entwicklungen der Forschung, Peter Lang, Frankfurt, pp. 282-331 (article title: “Predicting election outcomes in a world without sampling error. Analytical considerations and empirical evidence”; volume: “Elections and political attitudes in the Federal Republic of Germany: Recent developments in research”).
66. Rhode, P.W. and Strumpf, K.S. (2004), “Historical presidential betting markets”, Journal of Economic Perspectives, Vol. 18 No. 2, pp. 127-142.
67. Shi, L., Agarwal, N., Agrawal, A., Garg, R. and Spoelstra, J. (2012), “Predicting US primary elections with Twitter”, Proceedings of Social Network and Social Media Analysis: Methods, Models and Applications (NIPS Workshop), Lake Tahoe, NV, December 7.
68. Shirky, C. (2008), Here Comes Everybody: The Power of Organizing Without Organizations, The Penguin Press, New York, NY.
69. Signorini, A., Segre, A.M. and Polgreen, P.M. (2011), “The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic”, PLoS ONE, Vol. 6 No. 5, p. -.
70. Silver, N. (2012), The Signal and the Noise. The Art and Science of Prediction, Allen Lane, London.
71. Song, C., Qu, Z., Blumm, N. and Barabási, A.L. (2010), “Limits of predictability in human mobility”, Science, Vol. 327 No. 5968, pp. 1018-1021.
72. Surowiecki, J. (2004), The Wisdom of Crowds, Doubleday, New York, NY.
73. Tumarkin, R. and Whitelaw, R.F. (2001), “News or noise? Internet postings and stock prices”, Financial Analysts Journal, Vol. 57 No. 3, p. 41-51.
74. Wolfers, J. and Zitzewitz, E. (2004), “Prediction markets”, Journal of Economic Perspectives, Vol. 18 No. 2, pp. 107-126.
75. Wong, F.M.F., Sen, S. and Chiang, M. (2012), “Why watching movie tweets won't tell the whole story?”, Proceedings of WOSN'12, Helsinki, August 17.
76. Wüthrich, B., Permunetilleke, D., Leung, S., Cho, V., Zhang, J. and Lam, W. (1998), “Daily prediction of major stock indices from textual WWW data”, Proceedings of KDD-98, New York, NY, August 27-31.
77. Zhang, X., Fuehres, H. and Gloor, P.A. (2011), “Predicting stock market indicators through twitter ‘I hope it is not as bad as I fear'”, Procedia – Social and Behavioral Sciences, Vol. 26, pp. 55-62, available at: www.sciencedirect.com/science/article/pii/S1877042811023895