Modeling Behavioral Aspects of Social Media Discourse for Moral Classification

Kristen Johnson, Dan Goldwasser


Abstract
Political discourse on social media microblogs, specifically Twitter, has become an undeniable part of mainstream U.S. politics. Given the length constraint of tweets, politicians must carefully word their statements to ensure their message is understood by their intended audience. This constraint often eliminates the context of the tweet, making automatic analysis of social media political discourse a difficult task. To overcome this challenge, we propose simultaneous modeling of high-level abstractions of political language, such as political slogans and framing strategies, with abstractions of how politicians behave on Twitter. These behavioral abstractions can be further leveraged as forms of supervision in order to increase prediction accuracy, while reducing the burden of annotation. In this work, we use Probabilistic Soft Logic (PSL) to build relational models to capture the similarities in language and behavior that obfuscate political messages on Twitter. When combined, these descriptors reveal the moral foundations underlying the discourse of U.S. politicians online, across differing governing administrations, showing how party talking points remain cohesive or change over time.
Anthology ID:
W19-2112
Volume:
Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venues:
NAACL | NLP+CSS | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
100–109
Language:
URL:
https://www.aclweb.org/anthology/W19-2112
DOI:
10.18653/v1/W19-2112
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-2112.pdf