Expert Credibility and Sentiment in Infodemiology of Hydroxychloroquine's Efficacy on Cable News Programs: Empirical Analysis

Infodemic exacerbates public health concerns by disseminating unreliable and false scientific facts to a population. During the COVID-19 pandemic, the efficacy of hydroxychloroquine as a therapeutic solution emerged as a challenge to public health communication. Internet and social media spread info...

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Veröffentlicht in:JMIR infodemiology 2023-06, Vol.3, p.e45392-e45392
Hauptverfasser: Yim, Dobin, Khuntia, Jiban, King, Elliot, Treskon, Matthew, Galiatsatos, Panagis
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Sprache:eng
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Zusammenfassung:Infodemic exacerbates public health concerns by disseminating unreliable and false scientific facts to a population. During the COVID-19 pandemic, the efficacy of hydroxychloroquine as a therapeutic solution emerged as a challenge to public health communication. Internet and social media spread information about hydroxychloroquine, whereas cable television was a vital source. To exemplify, experts discussed in cable television broadcasts about hydroxychloroquine for treating COVID-19. However, how the experts' comments influenced airtime allocation on cable television to help in public health communication, either during COVID-10 or at other times, is not understood. This study aimed to examine how 3 factors, that is, the credibility of experts as doctors (DOCTOREXPERT), the credibility of government representatives (GOVTEXPERT), and the sentiments (SENTIMENT) expressed in discussions and comments, influence the allocation of airtime (AIRTIME) in cable television broadcasts. SENTIMENT pertains to the information credibility conveyed through the tone and language of experts' comments during cable television broadcasts, in contrast to the individual credibility of the doctor or government representatives because of the degree or affiliations. We collected transcriptions of relevant hydroxychloroquine-related broadcasts on cable television between March 2020 and October 2020. We coded the experts as DOCTOREXPERT or GOVTEXPERT using publicly available data. To determine the sentiments expressed in the broadcasts, we used a machine learning algorithm to code them as POSITIVE, NEGATIVE, NEUTRAL, or MIXED sentiments. The analysis revealed a counterintuitive association between the expertise of doctors (DOCTOREXPERT) and the allocation of airtime, with doctor experts receiving less airtime (P
ISSN:2564-1891
2564-1891
DOI:10.2196/45392