- Anatoliy Gruzd, Ted Rogers School of Information Technology Management, Ryerson University, Toronto, Canada
- Manlio De Domenico, Center for Information Technology of Fondazione Bruno Kessler, Italy
- Pier Luigi Sacco, Department of Humanities, IULM University, Milan, Italy; metaLAB (at) Harvard, USA
- Sylvie Briand, World Health Organization, Switzerland
As manufacturing and distribution of the vaccines are ramping up, false and misleading information about vaccines efficacy, safety and side-effects have also increased on social media. This is reflected in the increasing number of vaccine-related claims being debunked by international fact-checking organizations. But, false and misleading COVID-19 claims, as tracked by the COVID19Misinfo portal from Ryerson University Social Media Lab, are not limited to vaccine-related content. In fact, since the onset of the COVID-19 pandemic in early 2020, social media has been a key vector in the spread of various types of misinformation about the virus including how it is transmitted and how to treat it. The prevalence of COVID-19 related misinformation on social media contributes to the phenomenon called “infodemic,” when people are exposed to large quantities of both accurate and misleading information related to a health topic. An infodemic makes it challenging for people to know what or whom to trust, especially when faced with conflicting claims or information.
To address the challenges of detecting and combating the spread of COVID-19 misinformation on social media and to contribute to the rapidly growing area of infodemiology, we are pleased to present the special theme on “Studying the COVID-19 Infodemic at Scale”. This special theme in Big Data & Society provides a space for original research articles and commentaries at the intersection of infodemiology, Big Data, and COVID-related dis/misinformation studies that explore questions such as: What are key terminologies and different computational approaches currently used to study and combat the spread of misinformation on social media? How can we use social media data to estimate the effects of the infodemic on individuals and society in general? And more specifically, how can we assess and mitigate the infodemic risks and consequences using Big Data?
The special theme issue builds on a successful series of public events and consultations organized by the World Health Organization (WHO) Information Network for Epidemics (EPI-WIN) Infodemic Management team in 2020. We are also building on the Big Data & Society symposium called “Viral Data” edited by Leszczynski and Zook (2020) which examined Big Data practices and specifically the notion of data virality as related to the pandemic at the midpoint of 2020.
All together the special theme features the following six research articles and four commentaries by 57 authors from 23 institutions in six countries:
- Studying the COVID-19 infodemic at scale – Anatoliy Gruzd, Manlio De Domenico, Pier Luigi Sacco, and Sylvie Briand
- Countering misinformation: A multidisciplinary approach – Kacper T Gradoń, Janusz A. Hołyst, Wesley R Moy, Julian Sienkiewicz and Krzysztof Suchecki
- The COVID-19 Infodemic: Twitter versus Facebook – Kai-Cheng Yang, Francesco Pierri, Pik-Mai Hui, David Axelrod, Christopher Torres-Lugo, John Bryden and Filippo Menczer
- Identifying how COVID-19 related misinformation reacts to the announcement of the UK national lockdown: An interrupted time-series study – Mark Green, Elena Musi, Francisco Rowe, Darren Charles, Frances Darlington Pollock, Chris Kypridemos, Andrew Morse, Patricia Rossini, John Tulloch, Andrew Davies, Emily Dearden, Henrdramoorthy Maheswaran, Alex Singleton, Roberto Vivancos and Sally Sheard
- Identifying and characterizing scientific authority-related misinformation discourse about hydroxychloroquine on twitter using unsupervised machine learning – Michael Robert Haupt, Jiawei Li and Tim K Mackey
- Toxicity and verbal aggression on social media: Polarized discourse on wearing face masks during the COVID-19 pandemic – Paola Pascual-Ferrá, Neil Alperstein, Daniel J Barnett, and Rajiv N Rimal
- Towards psychological herd immunity: Cross-cultural evidence for two prebunking interventions against COVID-19 misinformation – Melisa Basol, Jon Roozenbeek, Manon Berriche, Fatih Uenal, William P. McClanahan and Sander van der Linden
- Knowing when to act: A call for an open misinformation library to guide actionable surveillance – Adam G Dunn, Maryke Steffens, Amalie Dyda and Kenneth D Mandl
- Knowledge co-creation in participatory policy and practice: Building community through data-driven direct democracy – Myron A Godinho, Ann Borda, Timothy Kariotis, Andreea Molnar, Patty Kostkova and Siaw-Teng Liaw
- Communicating public health during COVID-19, implications for vaccine rollout – Peter S Bloomfield, Josefine Magnusson, Maeve Walsh, and Annemarie Naylor
- The case for tracking misinformation the way we track disease – Erika Bonnevie, Jennifer Sittig and Joe Smyser