Detecting sociostructural beliefs about group status differences in online discussions

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Authors
  1. Upal, A.
  2. Riordan, B.
  3. Wade, H.
Corporate Authors
Defence Research and Development Canada, Toronto Research Centre , Toronto ON (CAN)
Abstract
Detection of fine-grained opinions and beliefs holds promise for improved social media analysis for social science research, business intelligence, and government decision-makers. While commercial applications focus on mapping landscapes of opinions towards brands and products, our goal is to map “sociostructural” landscapes of perceptions of social groups. In this work, we focus on the detection of views of social group status differences. We report an analysis of methods for detecting views of the legitimacy of income inequality in the U.S. from online discussions, and demonstrate detection rates competitive with results from similar tasks such as debate stance classification.
Keywords
Information Extraction;Social Media;Machine Learning
Report Number
DRDC-RDDC-2018-N009 — External Literature
Date of publication
01 Feb 2018
Number of Pages
10
Reprinted from
Association for Computational Linguistics
DSTKIM No
CA045812
CANDIS No
806266
Format(s):
Electronic Document(PDF)

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