Selective tweeting of COVID-19 articles: Does title or abstract positivity influence dissemination?

  1. Nicholas Fabiano
  2. Zachary Hallgrimson
  3. Stanley Wong
  4. Jean-Paul Salameh
  5. Sakib Kazi
  6. Rudy R Unni
  7. Lee Treanor
  8. Robert Frank
  9. Ross Prager
  10. Matthew DF McInnes

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Abstract

Background

Previous 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.

Methods

COVID-19 related articles published between January 1 st and April 14 th , 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.

Results

A 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).

Conclusion

COVID-19 articles with non-positive titles are preferentially tweeted, while abstract positivity does not influence tweet rate.

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  1. ScreenIT

    SciScore for 10.1101/2021.06.22.21259354: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Python v3.7.4 was used to search included article titles or abstracts for words in sentiment criteria.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: This study is subject to limitations. Firstly, the list of positive and negative words is finite, and thus may not have quantified all sentiment present in the titles and abstracts. A prespecified list of words was employed in order to exclude the subjective bias in classifying the sentiment of words as positive, negative, or neutral. Further, the location or context of the word in the title or abstract was not studied, which could hold significant value. Moreover, self-promotion (e.g. authors tweeting their own article) could influence the number of tweets an article receives, however, the extent of this is difficult to quantify. Also, this study did not determine the sentiment of the tweets, which could provide insightful information. Despite this, it has been demonstrated that 94.8% of tweets linking to scientific papers were neutral in sentiment (35), suggesting that a similar trend was present in this cohort. There was also a loss of precision due to the use of the categorical variable continent instead of the country of the corresponding author’s institution, which was necessary in order to get an appropriate fit of the model. Similarly, although imputation was performed to address the relatively high proportion of missing values, this could have impacted the robustness of the model fit to the data.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.06.22.21259354v1 on medRxiv