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

John R. Gallagher, Yinyin Chen, Kyle Wagner, Xuan Wang, Jingyi Zeng, Alyssa Lingyi Kong

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)155-173
Number of pages19
JournalTechnical Communication Quarterly
Volume29
Issue number2
DOIs
StatePublished - Apr 2 2020

Keywords

  • Digital technologies
  • experimental research
  • research methods
  • usability studies
  • visual rhetoric/visualization techniques

ASJC Scopus subject areas

  • Education
  • Communication

Fingerprint Dive into the research topics of 'Peering into the Internet Abyss: Using Big Data Audience Analysis to Understand Online Comments'. Together they form a unique fingerprint.

  • Cite this