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With over 16 million tweets per hour, 600 new blog posts per minute, and 400 million active users on Facebook, businesses have begun searching for ways to turn real-time consumer-based

With over 16 million tweets per hour, 600 new blog posts per minute, and 400 million active users on Facebook, businesses have begun searching for ways to turn real-time consumer-based posts into actionable intelligence. The goal is to extract information from this noisy, unstructured data and use it for trend analysis and prediction. Current practices support the idea that visual analytics (VA) can help enable the effective analysis of such data. However, empirical evidence demonstrating the effectiveness of a VA solution is still lacking.

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Date Created
  • 2014-09-01
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  • Text
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    Identifier
    • Digital object identifier: 10.1109/MCG.2014.61
    • Identifier Type
      International standard serial number
      Identifier Value
      0272-1716
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    • Copyright 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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    Lu, Yafeng, Wang, Feng, & Maciejewski, Ross (2014). Business Intelligence from Social Media A Study from the VAST Box Office Challenge. IEEE COMPUTER GRAPHICS AND APPLICATIONS, 34(5), 58-69. http://ieeexplore.ieee.org.ezproxy1.lib.asu.edu/xpl/abstractKeywords.js…

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