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        <title>An NLP Analysis of Exaggerated Claims in Science News</title>
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        <namePart type="given">Yingya</namePart>
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        <namePart type="given">Jieke</namePart>
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    <name type="personal">
        <namePart type="given">Bei</namePart>
        <namePart type="family">Yu</namePart>
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        <dateIssued>2017-09</dateIssued>
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            <title>Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism</title>
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            <namePart type="family">Popescu</namePart>
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        <name type="personal">
            <namePart type="given">Carlo</namePart>
            <namePart type="family">Strapparava</namePart>
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    <abstract>The discrepancy between science and media has been affecting the effectiveness of science communication. Original findings from science publications may be distorted with altered claim strength when reported to the public, causing misinformation spread. This study conducts an NLP analysis of exaggerated claims in science news, and then constructed prediction models for identifying claim strength levels in science reporting. The results demonstrate different writing styles journal articles and news/press releases use for reporting scientific findings. Preliminary prediction models reached promising result with room for further improvement.</abstract>
    <identifier type="citekey">li-etal-2017-nlp</identifier>
    <identifier type="doi">10.18653/v1/W17-4219</identifier>
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        <url>https://aclanthology.org/W17-4219</url>
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        <date>2017-09</date>
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            <start>106</start>
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%0 Conference Proceedings
%T An NLP Analysis of Exaggerated Claims in Science News
%A Li, Yingya
%A Zhang, Jieke
%A Yu, Bei
%Y Popescu, Octavian
%Y Strapparava, Carlo
%S Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F li-etal-2017-nlp
%X The discrepancy between science and media has been affecting the effectiveness of science communication. Original findings from science publications may be distorted with altered claim strength when reported to the public, causing misinformation spread. This study conducts an NLP analysis of exaggerated claims in science news, and then constructed prediction models for identifying claim strength levels in science reporting. The results demonstrate different writing styles journal articles and news/press releases use for reporting scientific findings. Preliminary prediction models reached promising result with room for further improvement.
%R 10.18653/v1/W17-4219
%U https://aclanthology.org/W17-4219
%U https://doi.org/10.18653/v1/W17-4219
%P 106-111

Markdown (Informal)

[An NLP Analysis of Exaggerated Claims in Science News](https://aclanthology.org/W17-4219) (Li et al., 2017)

• An NLP Analysis of Exaggerated Claims in Science News (Li et al., 2017)

ACL

• Yingya Li, Jieke Zhang, and Bei Yu. 2017. An NLP Analysis of Exaggerated Claims in Science News. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, pages 106–111, Copenhagen, Denmark. Association for Computational Linguistics.