Abstract
Health experts and government authorities' actions to combat the coronavirus outbreak are strongly compromised by the misinformation infodemic that evolved in parallel to the COVID-19 pandemic. When people get misled by unscientific and unsubstantiated claims regarding the origin or cures for COVID-19, public health response efforts get undermined and people might be less likely to comply with official guidance and thus spread the virus or even harm themselves. To prevent this from happening, a first step is to reveal the prevalence of misinformation ideas in the public. In this study, we use search log analysis to investigate the extent and characteristics of misinformation seeking behaviour in the US using the Bing Search Data-set for Coronavirus Intent. We train a machine learning model to distinguish between regular and misinformation queries and find that only around 1\% of queries are related to misinformation myths or conspiracy theories. The query term qanon - connecting the conspiracy theory to many different origin myths of COVID-19 - is the most frequent and steadily increasing misinformation-related query in the data-set.
Originalsprog | Engelsk |
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Titel | Proceedings of iConference 2021 |
Publikationsdato | 2021 |
ISBN (Trykt) | 978-3-030-71305-8 |
Status | Udgivet - 2021 |
Begivenhed | iConference 2021: Diversity, Divergence, Dialogue - Online, Beijing, Kina Varighed: 28 mar. 2021 → 31 mar. 2021 Konferencens nummer: 16 https://ischools.org/iConference-2021-Summary |
Konference
Konference | iConference 2021 |
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Nummer | 16 |
Lokation | Online |
Land/Område | Kina |
By | Beijing |
Periode | 28/03/2021 → 31/03/2021 |
Internetadresse |
Navn | Lecture Notes in Computer Science |
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Vol/bind | 14645 |
ISSN | 0302-9743 |