Peering into the Internet Abyss: Using Big Data Audience Analysis to Understand Online Comments. Issue 2 (2nd April 2020)

• Record Type: Journal Article
• Authors: Gallagher, John R.; Chen, Yinyin; Wagner, Kyle; Wang, Xuan; Zeng, Jingyi; Kong, Alyssa Lingyi
• Is Part Of: Technical communication quarterly. Volume 29:Issue 2(2020)
• Page Start: 155
• Page End: 173
• Publication Date: 2020-04-02
• Subjects: Digital technologies -- research methods -- usability studies -- experimental research -- visual rhetoric/visualization techniques; Technical writing -- Periodicals; Technical writing -- Study and teaching -- Periodicals; 808.0666
• DOI: 10.1080/10572252.2019.1634766
• Languages: English
• ISSNs: 1057-2252

Abstract

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.