TY - GEN
T1 - Learning to Adapt Domain Shifts of Moral Values via Instance Weighting
AU - Huang, Xiaolei
AU - Wormley, Alexandra
AU - Cohen, Adam
N1 - Funding Information:
The authors want to thank for reviewers' valuable comments.
Publisher Copyright:
© 2022 ACM.
PY - 2022/6/28
Y1 - 2022/6/28
N2 - Classifying moral values in user-generated text from social media is critical in understanding community cultures and interpreting user behaviors of social movements. Moral values and language usage can change across the social movements; however, text classifiers are usually trained in source domains of existing social movements and tested in target domains of new social issues without considering the variations. In this study, we examine domain shifts of moral values and language usage, quantify the effects of domain shifts on the morality classification task, and propose a neural adaptation framework via instance weighting to improve cross-domain classification tasks. The quantification analysis suggests a strong correlation between morality shifts, language usage, and classification performance. We evaluate the neural adaptation framework on a public Twitter data across 7 social movements and gain classification improvements up to 12.1%. Finally, we release a new data of the COVID-19 vaccine labeled with moral values and evaluate our approach on the new target domain. For the case study of the COVID-19 vaccine, our adaptation framework achieves up to 5.26% improvements over neural baselines. This is the first study to quantify impacts of moral shifts, propose adaptive framework to model the shifts, and conduct a case study to model COVID-19 vaccine-related behaviors from moral values.
AB - Classifying moral values in user-generated text from social media is critical in understanding community cultures and interpreting user behaviors of social movements. Moral values and language usage can change across the social movements; however, text classifiers are usually trained in source domains of existing social movements and tested in target domains of new social issues without considering the variations. In this study, we examine domain shifts of moral values and language usage, quantify the effects of domain shifts on the morality classification task, and propose a neural adaptation framework via instance weighting to improve cross-domain classification tasks. The quantification analysis suggests a strong correlation between morality shifts, language usage, and classification performance. We evaluate the neural adaptation framework on a public Twitter data across 7 social movements and gain classification improvements up to 12.1%. Finally, we release a new data of the COVID-19 vaccine labeled with moral values and evaluate our approach on the new target domain. For the case study of the COVID-19 vaccine, our adaptation framework achieves up to 5.26% improvements over neural baselines. This is the first study to quantify impacts of moral shifts, propose adaptive framework to model the shifts, and conduct a case study to model COVID-19 vaccine-related behaviors from moral values.
KW - adaptation
KW - classification
KW - domain variation
KW - instant weighting
KW - moral values
KW - morality
UR - http://www.scopus.com/inward/record.url?scp=85134258180&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85134258180&partnerID=8YFLogxK
U2 - 10.1145/3511095.3531269
DO - 10.1145/3511095.3531269
M3 - Conference contribution
AN - SCOPUS:85134258180
T3 - HT 2022: 33rd ACM Conference on Hypertext and Social Media - Co-located with ACM WebSci 2022 and ACM UMAP 2022
SP - 121
EP - 131
BT - HT 2022
PB - Association for Computing Machinery, Inc
T2 - 33rd ACM Conference on Hypertext and Social Media, HT 2022 - Co-located with ACM WebSci 2022 and ACM UMAP 2022
Y2 - 28 June 2022 through 1 July 2022
ER -