TY - GEN
T1 - Incorporating the Measurement of Moral Foundations Theory into Analyzing Stances on Controversial Topics
AU - Rezapour, Rezvaneh
AU - Dinh, Ly
AU - Diesner, Jana
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/8/30
Y1 - 2021/8/30
N2 - This paper investigates the correlation between moral foundations and the expression of opinions in the form of stance on different issues of public interest. This work is based on the assumption that the formation of values (personal and societal) and language are interrelated, and that we can observe differences in points of view in user-generated text data. We leverage the Moral Foundations Theory to expand the scope of stance analysis by examining the narratives in favor or against several topics. Applying an expanded version of the Moral Foundations Dictionary to a benchmark dataset for stance analysis, we capture and analyze the relationships between moral values and polarized online discussions. Using this enhanced methodology, we find that each social issue has different "moral and lexical profiles."While some social issues project more authority related words (Donald Trump), others consists of words related to care and purity (abortion and feminism). Our correlation analysis of stance and morality revealed notable associations between stances on social issues and various types of morality, such as care, fairness, and loyalty, hence demonstrating that there are certain morality types that are more attributed to stance classification than others. Overall, our analysis highlights the usefulness of considering morality when studying stance. The differences observed in various viewpoints and stances highlights linguistic variation in discourse, which may assist in analyzing cultural values and biases in society.
AB - This paper investigates the correlation between moral foundations and the expression of opinions in the form of stance on different issues of public interest. This work is based on the assumption that the formation of values (personal and societal) and language are interrelated, and that we can observe differences in points of view in user-generated text data. We leverage the Moral Foundations Theory to expand the scope of stance analysis by examining the narratives in favor or against several topics. Applying an expanded version of the Moral Foundations Dictionary to a benchmark dataset for stance analysis, we capture and analyze the relationships between moral values and polarized online discussions. Using this enhanced methodology, we find that each social issue has different "moral and lexical profiles."While some social issues project more authority related words (Donald Trump), others consists of words related to care and purity (abortion and feminism). Our correlation analysis of stance and morality revealed notable associations between stances on social issues and various types of morality, such as care, fairness, and loyalty, hence demonstrating that there are certain morality types that are more attributed to stance classification than others. Overall, our analysis highlights the usefulness of considering morality when studying stance. The differences observed in various viewpoints and stances highlights linguistic variation in discourse, which may assist in analyzing cultural values and biases in society.
KW - controversial topics
KW - moral foundations theory
KW - social media
KW - stance analysis
KW - text analysis
UR - http://www.scopus.com/inward/record.url?scp=85114807568&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85114807568&partnerID=8YFLogxK
U2 - 10.1145/3465336.3475112
DO - 10.1145/3465336.3475112
M3 - Conference contribution
AN - SCOPUS:85114807568
T3 - HT 2021 - Proceedings of the 32nd ACM Conference on Hypertext and Social Media
SP - 177
EP - 188
BT - HT 2021 - Proceedings of the 32nd ACM Conference on Hypertext and Social Media
PB - Association for Computing Machinery
T2 - 32nd ACM Conference on Hypertext and Social Media, HT 2021
Y2 - 30 August 2021 through 2 September 2021
ER -