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        <title>Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives</title>
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        <namePart type="given">Elisa</namePart>
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    <name type="personal">
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            <namePart type="given">Tal</namePart>
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    <abstract>Crime reporting is a prevalent form of journalism with the power to shape public perceptions and social policies. How does the language of these reports act on readers? We seek to address this question with the SuspectGuilt Corpus of annotated crime stories from English-language newspapers in the U.S. For SuspectGuilt, annotators read short crime articles and provided text-level ratings concerning the guilt of the main suspect as well as span-level annotations indicating which parts of the story they felt most influenced their ratings. SuspectGuilt thus provides a rich picture of how linguistic choices affect subjective guilt judgments. We use SuspectGuilt to train and assess predictive models which validate the usefulness of the corpus, and show that these models benefit from genre pretraining and joint supervision from the text-level ratings and span-level annotations. Such models might be used as tools for understanding the societal effects of crime reporting.</abstract>
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    <identifier type="doi">10.18653/v1/2020.conll-1.5</identifier>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives
%A Kreiss, Elisa
%A Wang, Zijian
%A Potts, Christopher
%Y Fernández, Raquel
%Y Linzen, Tal
%S Proceedings of the 24th Conference on Computational Natural Language Learning
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F kreiss-etal-2020-modeling
%X Crime reporting is a prevalent form of journalism with the power to shape public perceptions and social policies. How does the language of these reports act on readers? We seek to address this question with the SuspectGuilt Corpus of annotated crime stories from English-language newspapers in the U.S. For SuspectGuilt, annotators read short crime articles and provided text-level ratings concerning the guilt of the main suspect as well as span-level annotations indicating which parts of the story they felt most influenced their ratings. SuspectGuilt thus provides a rich picture of how linguistic choices affect subjective guilt judgments. We use SuspectGuilt to train and assess predictive models which validate the usefulness of the corpus, and show that these models benefit from genre pretraining and joint supervision from the text-level ratings and span-level annotations. Such models might be used as tools for understanding the societal effects of crime reporting.
%R 10.18653/v1/2020.conll-1.5
%U https://aclanthology.org/2020.conll-1.5
%U https://doi.org/10.18653/v1/2020.conll-1.5
%P 56-68

Markdown (Informal)

[Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives](https://aclanthology.org/2020.conll-1.5) (Kreiss et al., CoNLL 2020)

• Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives (Kreiss et al., CoNLL 2020)

ACL

• Elisa Kreiss, Zijian Wang, and Christopher Potts. 2020. Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 56–68, Online. Association for Computational Linguistics.