Abstract
Existing studies on predicting election results are under the assumption that all the users should be treated equally. However, recent work [14] shows that social media users from different groups (e.g., “silent majority” vs. “vocal minority”) have significant differences in the generated content and tweeting behavior. The effect of these differences on predicting election results has not been exploited yet. In this paper, we study the spectrum of Twitter users who participate in the on-line discussion of 2012 U.S. Republican Presidential Primaries, and examine the predictive power of different user groups (e.g., highly engaged users vs. lowly engaged users, right-leaning users vs. left-leaning users) against Super Tuesday primaries in 10 states. The insights gained in this study can shed light on improving the social media based prediction from the user sampling perspective and more.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Asur, S., Huberman, B.A.: Predicting the future with social media. Arxiv preprint arXiv:1003.5699 (2010), http://arxiv.org/abs/1003.5699
Bermingham, A., Smeaton, A.F.: On using Twitter to monitor political sentiment and predict election results. In: Proceedings of the Sentiment Analysis where AI meets Psychology Workshop at IJCNLP (2011)
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of Computational Science (2011)
Chen, L., Wang, W., Nagarajan, M., Wang, S., Sheth, A.P.: Extracting Diverse Sentiment Expressions with Target-dependent Polarity from Twitter. In: Proceedings of ICWSM (2012)
Conover, M.D., Gonçalves, B., Ratkiewicz, J., Flammini, A., Menczer, F.: Predicting the political alignment of twitter users. In: Proceedings of the IEEE 3rd International Confernece on Social Computing, pp. 192–199 (2011)
Conover, M.D., Ratkiewicz, J., Francisco, M., Goncalves, B., Flammini, A., Menczer, F.: Political polarization on twitter. In: Proceedings of ICWSM, pp. 89–96 (2011)
Gayo-Avello, D.: I Wanted to Predict Elections with Twitter and all I got was this Lousy Paper. Arxiv preprint arXiv:1204.6441 (2012), http://arxiv.org/abs/1204.6441
Gayo-Avello, D.: Don’t turn social media into another ’Literary Digest’ poll. Communications of the ACM 54(10), 121–128 (2011)
Gilbert, E., Karahalios, K.: Widespread worry and the stock market. In: Proceedings of ICWSM, pp. 229–247 (2010)
Golbeck, J., Hansen, D.: Computing political preference among twitter followers. In: Proceedings of the Annual Conference on Human Factors in Computing Systems, pp. 1105–1108 (2011)
Livne, A., Simmons, M.P., Adar, E., Adamic, L.A.: The party is over here: Structure and content in the 2010 election. In: Proceedings of ICWSM (2011)
Metaxas, P.T., Mustafaraj, E., Gayo-Avello, D.: How (Not) to predict elections. In: Proceedings of the IEEE 3rd International Confernece on Social Computing, pp. 165–171 (2011)
Mishne, G., Glance, N.: Predicting movie sales from blogger sentiment. In: AAAI Symposium on Computational Approaches to Analysing Weblogs (2006)
Mustafaraj, E., Finn, S., Whitlock, C., Metaxas, P.T.: Vocal minority versus silent majority: Discovering the opionions of the long tail. In: Proceedings of the IEEE 3rd International Confernece on Social Computing, pp. 103–110 (2011)
O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: Linking text sentiment to public opinion time series. In: Proceedings of ICWSM, pp. 122–129 (2011)
Sang, E.T.K., Bos, J.: Predicting the 2011 Dutch Senate Election Results with Twitter. In: Proceedings of SASN 2012, the EACL 2012 Workshop on Semantic Analysis in Social Networks, pp. 53–60 (2012)
Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: What 140 characters reveal about political sentiment. In: Proceedings of ICWSM, pp. 178–185 (2010)
Zhang, X., Fuehres, H., Gloor, P.A.: 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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, L., Wang, W., Sheth, A.P. (2012). Are Twitter Users Equal in Predicting Elections? A Study of User Groups in Predicting 2012 U.S. Republican Presidential Primaries. In: Aberer, K., Flache, A., Jager, W., Liu, L., Tang, J., Guéret, C. (eds) Social Informatics. SocInfo 2012. Lecture Notes in Computer Science, vol 7710. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35386-4_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-35386-4_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35385-7
Online ISBN: 978-3-642-35386-4
eBook Packages: Computer ScienceComputer Science (R0)