Abstract

A key analytical task across many domains is model building and exploration for predictive analysis. Data is collected, parsed and analyzed for relationships, and features are selected and mapped to estimate the response of a system under exploration. As social media data has grown more abundant, data can be captured that may potentially represent behavioral patterns in society. In turn, this unstructured social media data can be parsed and integrated as a key factor for predictive intelligence. In this paper, we present a framework for the development of predictive models utilizing social media data. We combine feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. In order to explore how predictions might be performed in such a framework, we present results from a user study focusing on social media data as a predictor for movie box-office success.

Original languageEnglish (US)
Title of host publication2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings
EditorsMin Chen, David Ebert, Chris North
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages193-202
Number of pages10
ISBN (Electronic)9781479962273
DOIs
StatePublished - Feb 13 2015
Event2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Paris, France
Duration: Oct 9 2014Oct 14 2014

Publication series

Name2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings

Other

Other2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014
CountryFrance
CityParis
Period10/9/1410/14/14

Keywords

  • Feature Selection
  • Predictive Analytics
  • Social Media

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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