Peering into the Internet Abyss: Using Big Data Audience Analysis to Understand Online Comments

John R. Gallagher ; Yinyin Chen University of Illinois Urbana-Champaign ; Kyle Wagner University of Minnesota System ; Xuan Wang University of Illinois Urbana-Champaign ; Jingyi Zeng University of Illinois Urbana-Champaign ; Alyssa Lingyi Kong Ernst & Young (Israel)

Abstract

This article offers a methodology for conducting large-scale audience analysis called “big data audience analysis” (BDAA). BDAA uses distant reading and thin description to examine a large corpus of text data from online audiences. In this article, that corpus is approximately 450,000 online reader comments. We analyze this corpus through sentiment analysis, statistical analysis, and geolocation to identify trends and patterns in large datasets. BDAA can better prepare TPC researchers for large-scale audience studies.

Journal
Technical Communication Quarterly
Published
2020-04-02
DOI
10.1080/10572252.2019.1634766
Open Access
Closed

Citation Context

Cited by in this index (11)

  1. Technical Communication Quarterly
  2. Computers and Composition
  3. Rhetoric Society Quarterly
  4. Technical Communication Quarterly
  5. Computers and Composition
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  1. Technical Communication Quarterly
  2. Technical Communication Quarterly
  3. Technical Communication Quarterly
  4. Journal of Business and Technical Communication
  5. Computers and Composition
  6. Rhetoric & Public Affairs

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  1. Technical Communication Quarterly
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  10. Journal of Technical Writing and Communication
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  12. Technical Communication Quarterly
  13. Journal of Business and Technical Communication
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