TY - JOUR
T1 - Peering into the Internet Abyss
T2 - Using Big Data Audience Analysis to Understand Online Comments
AU - Gallagher, John R.
AU - Chen, Yinyin
AU - Wagner, Kyle
AU - Wang, Xuan
AU - Zeng, Jingyi
AU - Kong, Alyssa Lingyi
N1 - Publisher Copyright:
© 2019, © 2019 Association of Teachers of Technical Writing.
PY - 2020/4/2
Y1 - 2020/4/2
N2 - 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.
AB - 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.
KW - Digital technologies
KW - experimental research
KW - research methods
KW - usability studies
KW - visual rhetoric/visualization techniques
UR - http://www.scopus.com/inward/record.url?scp=85068106902&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068106902&partnerID=8YFLogxK
U2 - 10.1080/10572252.2019.1634766
DO - 10.1080/10572252.2019.1634766
M3 - Article
AN - SCOPUS:85068106902
SN - 1057-2252
VL - 29
SP - 155
EP - 173
JO - Technical Communication Quarterly
JF - Technical Communication Quarterly
IS - 2
ER -