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Differences in sensationalism in international news media reporting of COVID-19: An exploratory analysis using the
Joanna Przepiorkowski1, Tenzin Norzin1, Abdelhamid Zaghlool1
1Global Public Health Intelligence Network, Public Health Agency of Canada, OttawaON.
Background:
The Global Public Health Intelligence Network (GPHIN) is an event-based surveillance platform that collects thousands of pieces of open-source information, including international news media, across multiple languages on a daily basis. Analysts have observed that news media reporting in some languages tended to use more sensational wording to describe major health events. There has been minimal research exploring potential differences in sensationalism in international news media reporting to confirm these observations.
Objective:
This exploratory study assessed the differences in the level of sensationalism in early international news media reporting of COVID-19 through a mixed-methods analysis.
Methods:
Relevant news media articles received in GPHIN seven days following the Public Health Emergency of International Concern declaration of COVID-19 by the World Health Organization were extracted for screening and analysis. An adapted tool was used to measure the sensationalism of pandemic-related health news. Deductive thematic analysis was conducted to examine themes of sensationalism. Differences in prevalence of sensationalism in news media reporting by language and country/territory of publication were assessed. Sentiment analysis assessed the sentiment and emotional tone of the news media articles.
Results:
Of 951 news articles that met the eligibility criteria, 155 contained sensationalism. There were significant differences between languages (French, Russian and Spanish) and various domains of sensationalism. This study also found a more negative emotional tone in news media articles with sensationalism.
Conclusion:
This exploratory study showed that language has the potential to impact the perception of health events using more sensationalized language.
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