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Selective tweeting of COVID-19 articles: Does title or abstract positivity influence dissemination?
Nicholas Fabiano; Zachary Hallgrimson; Stanley Wong; Jean-Paul Salameh; Sakib Kazi; Rudy R Unni; Lee Treanor; Robert Frank; Ross Prager; Matthew DF McInnes.
Affiliation
  • Nicholas Fabiano; Department of Radiology, Faculty of Medicine, University of Ottawa
  • Zachary Hallgrimson; Department of Radiology, Faculty of Medicine, University of Ottawa
  • Stanley Wong; Department of Radiology, Faculty of Medicine, University of Ottawa
  • Jean-Paul Salameh; Clinical Epidemiology Program, Ottawa Hospital Research Institute
  • Sakib Kazi; Department of Radiology, Faculty of Medicine, University of Ottawa
  • Rudy R Unni; Department of Medicine, Faculty of Medicine, University of Ottawa
  • Lee Treanor; Department of Radiology, Faculty of Medicine, University of Ottawa
  • Robert Frank; Department of Radiology, Faculty of Medicine, University of Ottawa
  • Ross Prager; Department of Medicine, Faculty of Medicine, University of Ottawa
  • Matthew DF McInnes; University of Ottawa Department of Radiology. Clinical Epidemiology Program, Ottawa Hospital Research Institute.
Preprint in English | medRxiv | ID: ppmedrxiv-21259354
ABSTRACT
BackgroundPrevious research has shown that articles may be cited more frequently on the basis of title or abstract positivity. Whether a similar selective sharing practice exists on Twitter is not well understood. The objective of this study was to assess if COVID-19 articles with positive titles or abstracts were tweeted more frequently than those with non-positive titles or abstracts. MethodsCOVID-19 related articles published between January 1st and April 14th, 2020 were extracted from the LitCovid database and all articles were screened for eligibility. Titles and abstracts were classified using a list of positive and negative words from a previous study. A negative binomial regression analysis controlling for confounding variables (2018 impact factor, open access status, continent of the corresponding author, and topic) was performed to obtain regression coefficients, with the p values obtained by likelihood ratio testing. ResultsA total of 3752 COVID-19 articles were included. Of the included studies, 44 titles and 112 abstracts were positive; 1 title and 7 abstracts were negative; and 3707 titles and 627 abstracts were neutral. Articles with positive titles had a lower tweet rate relative to articles with non-positive titles, with a regression coefficient of -1.10 (P < .001), while the positivity of the abstract did not impact tweet rate (P = .2218). ConclusionCOVID-19 articles with non-positive titles are preferentially tweeted, while abstract positivity does not influence tweet rate.
License
cc_by_nc_nd
Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2021 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2021 Document type: Preprint
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