Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource

  1. Konrad Krawczyk
  2. Tadeusz Chelkowski
  3. Daniel J Laydon
  4. Swapnil Mishra
  5. Denise Xifara
  6. Benjamin Gibert
  7. Seth Flaxman
  8. Thomas Mellan
  9. Veit Schwämmle
  10. Richard Röttger
  11. Johannes T Hadsund
  12. Samir Bhatt

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Abstract

Before the advent of an effective vaccine, nonpharmaceutical interventions, such as mask-wearing, social distancing, and lockdowns, have been the primary measures to combat the COVID-19 pandemic. Such measures are highly effective when there is high population-wide adherence, which requires information on current risks posed by the pandemic alongside a clear exposition of the rules and guidelines in place.

Objective

Here we analyzed online news media coverage of COVID-19. We quantified the total volume of COVID-19 articles, their sentiment polarization, and leading subtopics to act as a reference to inform future communication strategies.

Methods

We collected 26 million news articles from the front pages of 172 major online news sources in 11 countries (available online at SciRide). Using topic detection, we identified COVID-19–related content to quantify the proportion of total coverage the pandemic received in 2020. The sentiment analysis tool Vader was employed to stratify the emotional polarity of COVID-19 reporting. Further topic detection and sentiment analysis was performed on COVID-19 coverage to reveal the leading themes in pandemic reporting and their respective emotional polarizations.

Results

We found that COVID-19 coverage accounted for approximately 25.3% of all front-page online news articles between January and October 2020. Sentiment analysis of English-language sources revealed that overall COVID-19 coverage was not exclusively negatively polarized, suggesting wide heterogeneous reporting of the pandemic. Within this heterogenous coverage, 16% of COVID-19 news articles (or 4% of all English-language articles) can be classified as highly negatively polarized, citing issues such as death, fear, or crisis.

Conclusions

The goal of COVID-19 public health communication is to increase understanding of distancing rules and to maximize the impact of governmental policy. The extent to which the quantity and quality of information from different communication channels (eg, social media, government pages, and news) influence public understanding of public health measures remains to be established. Here we conclude that a quarter of all reporting in 2020 covered COVID-19, which is indicative of information overload. In this capacity, our data and analysis form a quantitative basis for informing health communication strategies along traditional news media channels to minimize the risks of COVID-19 while vaccination is rolled out.

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  1. Version published to 10.2196/28253
  2. ScreenIT

    SciScore for 10.1101/2020.12.24.20248813: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    For each ONS, we collected the archived front-page snapshots dating back to 2015 via a free service available through WebArchive (https://web.archive.org/), cutting off coverage in 2020 at 15th October.
    WebArchive
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    This avoided the caveat of tangential references to certain topics mentioned in the full article body or ambiguities that might arise by using more sophisticated topic modeling algorithms34. Unlike more complex topic modeling methods, our approach did not capture much more subtle references to these topics, underestimating the total coverage. Nonetheless, even using our very simple approach to topic modeling of COVID-19, we still identified a non-trivial amount of articles on the front pages of our ONSs referring to it. We estimate that a mean of 25% of our sample of front-page articles from 11 countries in 2020 mention COVID-19 in their titles and descriptions. Our method had reduced topic identification recall by not accounting for more subtle references to COVID-19 and the totality of the articles was certainly contaminated by retrieval of erroneous links that were not actual news articles. Therefore, the factual proportion of articles on news front pages referencing COVID might have been indeed higher. We envision that the amount of reporting on a topic of general interest like COVID-19 needs to be balanced. Too little information might leave the population under-informed and ill-equipped to respond appropriately. Too much coverage on the other hand runs the risk of obscuring information that is crucial for individuals to build an understanding of the pandemic and how to act in order to stay safe. Reporting on the pandemic cannot be perceived only for its informative func...

    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.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2020.12.24.20248813v1 on medRxiv