Using explicit linguistic expressions of preference in social media to predict voting behavior

Shawn O'Banion, Lawrence A Birnbaum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Probably the major approach to making predictions or recommendations about user behavior is by pairing unambiguous indicators of preference with attributes of the users who have given those indications. However, since the necessary preference data is often difficult to obtain, it is considered very valuable and often held closely by vendors and advertisers. On the other hand, while Twitter and other social media platforms provide a wealth of data about users by way of what they say or tweet, who or what they like or follow, etc., little work has been done to combine these data with indicators of preference for purposes of prediction or recommendation. In this paper we present a novel approach to mining preference data from natural language expressions in social media, which are then extrapolated to other individuals whose preferences are not known through predictive modeling. As an application for this approach, we describe the implementation of Tweetcast Your Vote, a publicly accessible system that predicted the voting decisions of Twitter users in the 2012 U.S. presidential election.

Original languageEnglish (US)
Title of host publicationProceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
PublisherAssociation for Computing Machinery
Pages207-214
Number of pages8
ISBN (Print)9781450322409
DOIs
StatePublished - Jan 1 2013
Event2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 - Niagara Falls, ON, Canada
Duration: Aug 25 2013Aug 28 2013

Other

Other2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
CountryCanada
CityNiagara Falls, ON
Period8/25/138/28/13

Fingerprint

Linguistics
Data mining

Keywords

  • Opinion identification and extraction
  • Personalization
  • Prediction
  • Recommendation systems
  • Social media analysis
  • Text mining

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

Cite this

O'Banion, S., & Birnbaum, L. A. (2013). Using explicit linguistic expressions of preference in social media to predict voting behavior. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 (pp. 207-214). Association for Computing Machinery. https://doi.org/10.1145/2492517.2492538
O'Banion, Shawn ; Birnbaum, Lawrence A. / Using explicit linguistic expressions of preference in social media to predict voting behavior. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013. Association for Computing Machinery, 2013. pp. 207-214
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O'Banion, S & Birnbaum, LA 2013, Using explicit linguistic expressions of preference in social media to predict voting behavior. in Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013. Association for Computing Machinery, pp. 207-214, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, Niagara Falls, ON, Canada, 8/25/13. https://doi.org/10.1145/2492517.2492538

Using explicit linguistic expressions of preference in social media to predict voting behavior. / O'Banion, Shawn; Birnbaum, Lawrence A.

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013. Association for Computing Machinery, 2013. p. 207-214.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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O'Banion S, Birnbaum LA. Using explicit linguistic expressions of preference in social media to predict voting behavior. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013. Association for Computing Machinery. 2013. p. 207-214 https://doi.org/10.1145/2492517.2492538