Twitter keyword volume, current spending, and weekday spending norms predict consumer spending

Proceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012

Volumn , Issue , 2012, Pages 747-753

  Stewart, Justin ^a,b,c   Strong, Homer ^b   Parker, Jeffrey ^a   Bedau, Mark A ^a  

^a REED COLLEGE   (United States)

^b Lucky Sort Inc   (United States)

^c UNIVERSITY OF PITTSBURGH   (United States)

Author keywords

Consumer spending; Forecast; Social media; Twitter

Indexed keywords

CHRISTMAS; CONSUMER SPENDINGS; ESTIMATE MODEL; FORECAST; FORECAST DEVIATIONS; HIGHLY-CORRELATED; MEASURE MODEL; N-GRAMS; ON CURRENTS; PREDICTION-BASED; PREDICTIVE ABILITIES; SOCIAL MEDIA; SUCCESS MEASURE; TEST MODELS; TWITTER;

AGGREGATES; DATA MINING; SOCIAL NETWORKING (ONLINE);

FORECASTING;

EID: 84873134234     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMW.2012.98     Document Type: Conference Paper

References (15)

1. J. Bollen, H. Mao, and S. Counts, "Computational economic and finance gauges: Polls, search, and twitter," Meeting of the National Bureau of Economic Research - Behavioral Finance Meeting, Stanford, CA, November 2011.
2. X. Zhang, H. Furhres, and P. Gloor, "Predicting the stock market through twitter 'i hope it is not as bad as i fear'," Collaborative Innovation Networks (COINs), Savannah, GA, 2010.
3. B. O'Connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith, "From tweets to polls: Linking text sentiment to public opinion time series," In Proceedings of the International AAAI, Conference on Weblogs and Social Media, 2010.
4. S. Asur and B. A. Huberman, "Predicting the future with social media," arXiv:1003.5699v1, March 2010.
5. J. Weng, E.-P. Lim, Q. He, and C. W.-K. Leung, "What do people want in microblogs? measuring interestingness of hashtags in twitter," in 2010 IEEE 10th International Conference on Data Mining, December 2010, pp. 1121 - 1126.
6. J. Bollen, H. Mao, and X. Zeng, "Twitter mood predicts the stock market," Journal of Computational Science, vol. 2, no. 1, pp. 1-8, March 2011.
7. E. Gilbert and K. Karahalios, "Widespread worry and the stock market," in Fourth International AAAI Conference on Weblogs and Social Media. Washington, DC: E. Gilbert and K. Karahalios, 2010, pp. 58-65.
8. D. Gruhl, R. Guha, R. Kumar, J. Novak, and A. Tomkins, "The predictive power of online chatter," in Proceeding of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. New York, NY: ACM Press, 2005, pp. 78-87.
9. J. Slacalek, "Forecasting consumption," 2004, working Paper, German Institute for Economic Research.
10. J. A. Wilcox, "Forecasting components of consumption with components of consumer sentiment," Business Economics, vol. 42, no. 4, pp. 36-46, 2007.
11. M. C. Lovell, "Data mining," The Review of Economics and Statistics, vol. 65, no. 1, pp. 1-12, 1983.
12. A. Pak and P. Paroubek., "Twitter as a corpus for sentiment analysis and opinion finding," in Proceedings of the Seventh conference on international Language Resources Association (ELRA), Valletta, Malta, May 2010, pp. 19-21.
13. D. Lazer, A. Pentland, L. Adamic, S. Aral, A.-L. Barabasi, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmann, T. Jebara, G. King, M. Macy, D. Roy, and M. V. Alstyne, "Computational social science," Science, vol. 323, no. 5915, pp. 721-723, 2009.
14. J. Giles, "Making the links," Nature, vol. 488, pp. 448-450, 2012.
15. D. J. Watts, "A twenty-first century science," Nature, vol. 445, p. 489, 2007.