Learning to Adapt Domain Shifts of Moral Values via Instance Weighting
- URL: http://arxiv.org/abs/2204.07603v1
- Date: Fri, 15 Apr 2022 18:15:41 GMT
- Title: Learning to Adapt Domain Shifts of Moral Values via Instance Weighting
- Authors: Xiaolei Huang, Alexandra Wormley, Adam Cohen
- Abstract summary: Classifying moral values in user-generated text from social media is critical to understanding community cultures.
Moral values and language usage can change across the social movements.
We propose a neural adaptation framework via instance weighting to improve cross-domain classification tasks.
- Score: 74.94940334628632
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
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