@inproceedings{johnson-goldwasser-2019-modeling,
title = "Modeling Behavioral Aspects of Social Media Discourse for Moral Classification",
author = "Johnson, Kristen and
Goldwasser, Dan",
editor = "Volkova, Svitlana and
Jurgens, David and
Hovy, Dirk and
Bamman, David and
Tsur, Oren",
booktitle = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2112",
doi = "10.18653/v1/W19-2112",
pages = "100--109",
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, \textit{across} differing governing administrations, showing how party talking points remain cohesive or change over time.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Modeling Behavioral Aspects of Social Media Discourse for Moral Classification
%A Johnson, Kristen
%A Goldwasser, Dan
%Y Volkova, Svitlana
%Y Jurgens, David
%Y Hovy, Dirk
%Y Bamman, David
%Y Tsur, Oren
%S Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F johnson-goldwasser-2019-modeling
%X 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.
%R 10.18653/v1/W19-2112
%U https://aclanthology.org/W19-2112
%U https://doi.org/10.18653/v1/W19-2112
%P 100-109
Markdown (Informal)
[Modeling Behavioral Aspects of Social Media Discourse for Moral Classification](https://aclanthology.org/W19-2112) (Johnson & Goldwasser, NLP+CSS 2019)
ACL