The case for tracking misinformation the way we track disease

Bibliographic Data

ID5260724
AuthorsErika Bonnevie (0000-0001-8068-5123, The Public Good Projects, Alexandria, VA, USA, corresponding author), Jennifer Sittig (The Public Good Projects, Alexandria, VA, USA), Joe Smyser (The Public Good Projects, Alexandria, VA, USA)
Year2021
Volume8
Issue1
Publication date2021-01-01
Open AccessYes
TypeARTICLE
VenueBig Data & Society (JOURNAL)
Journal identifiersISSN: 2053-9517 • E-ISSN: 2053-9517
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/20539517211013867
OpenAlexW3157924305
LanguageEN
Citations received4
References cited18

While public health organizations can detect disease spread, few can monitor and respond to real-time misinformation. Misinformation risks the public's health, the credibility of institutions, and the safety of experts and front-line workers. Big Data, and specifically publicly available media data, can play a significant role in understanding and responding to misinformation. The Public Good Projects uses supervised machine learning to aggregate and code millions of conversations relating to vaccines and the COVID-19 pandemic broadly, in real-time. Public health researchers supervise this process daily, and provide insights to practitioners across a range of disciplines. Through this work, we have gleaned three lessons to address misinformation. (1) Sources of vaccine misinformation are known; there is a need to operationalize learnings and engage the pro-vaccination majority in debunking vaccine-related misinformation. (2) Existing systems can identify and track threats against health experts and institutions, which have been subject to unprecedented harassment. This supports their safety and helps prevent the further erosion of trust in public institutions. (3) Responses to misinformation should draw from cross-sector crisis management best practices and address coordination gaps. Real-time monitoring and addressing misinformation should be a core function of public health, and public health should be a core use case for data scientists developing monitoring tools. The tools to accomplish these tasks are available; it remains up to us to prioritize them

Business · Computer security · Credibility · Internet privacy · Misinformation · Operationalization · Political science · Public health · Public relations · Computer Science · Data-Driven Disease Surveillance · Medicine · Misinformation and Its Impacts · Vaccine Coverage and Hesitancy

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