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    Why are medical research articles tweeted? The news value perspective

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    Htoo_Why_are_medical_2022.pdf
    Embargo:
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    Authors
    Htoo, Tint Hla Hla
    Na, Jin-Cheon
    Thelwall, Mike cc
    Issue Date
    2022-11-14
    
    Metadata
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    Abstract
    Counts of tweets mentioning research articles are potentially useful as social impact altmetric indicators, especially for health-related topics. One way to help understand what tweet counts indicate is to find factors that associate with the number of tweets received by articles. Using news value theory, this study examined six characteristics of research papers that may cause some articles to be more tweeted than others. For this, we manually coded 300 medical journal articles about COVID-19. A statistical analysis showed that all six factors that make articles more newsworthy according to news value theory (importance, controversy, elite nations, elite persons, scale, news prominence) associated with higher tweet counts. Since these factors are hypothesised to be general human news selection criteria, the results give new evidence that tweet counts may be indicators of general interest to members of society rather than measures of societal impact. This study also provides a new understanding of the strong positive relationship between news mentions and tweet counts for articles. Instead of news coverage attracting tweets or the other way round (journalists noticing highly tweeted articles and writing about them), the results are consistent with newsworthy characteristics of articles attracting both tweets and news mentions.
    Citation
    Htoo, T.H.H., Na, J-C. and Thelwall, M. (2022) Why are medical research articles tweeted? The news value perspective. Scientometrics.
    Publisher
    Springer
    Journal
    Scientometrics
    URI
    http://hdl.handle.net/2436/624962
    DOI
    10.1007/s11192-022-04578-1
    Additional Links
    https://link.springer.com/article/10.1007/s11192-022-04578-1
    Type
    Journal article
    Language
    en
    Description
    This is an accepted manuscript of an article published by Springer in Scientometrics on 14/11/2022. Available online : https://doi.org/10.1007/s11192-022-04578-1 The accepted version of the publication may differ from the final published version.
    ISSN
    0138-9130
    ae974a485f413a2113503eed53cd6c53
    10.1007/s11192-022-04578-1
    Scopus Count
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    Research Institute in Information and Language Processing

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