Studying the COVID-19 infodemic at scale

This special theme issue of Big Data & Society presents leading-edge, interdisciplinary research that focuses on examining how health-related (mis-)information is circulating on social media. In particular, we are focusing on how computational and Big Data approaches can help to provide a better understanding of the ongoing COVID-19 infodemic (overexposure to both accurate and misleading information on a health topic) and to develop effective strategies to combat it.

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[2]  D. Barnett,et al.  Toxicity and verbal aggression on social media: Polarized discourse on wearing face masks during the COVID-19 pandemic , 2021, Big Data Soc..

[3]  J. Hołyst,et al.  Countering misinformation: A multidisciplinary approach , 2021, Big Data Soc..

[4]  Patrícia G. C. Rossini,et al.  Identifying how COVID-19-related misinformation reacts to the announcement of the UK national lockdown: An interrupted time-series study , 2021 .

[5]  T. Mackey,et al.  Identifying and characterizing scientific authority-related misinformation discourse about hydroxychloroquine on twitter using unsupervised machine learning , 2021, Big Data Soc..

[6]  Erika Bonnevie,et al.  The case for tracking misinformation the way we track disease , 2021, Big Data Soc..

[7]  S. Linden,et al.  Towards psychological herd immunity: Cross-cultural evidence for two prebunking interventions against COVID-19 misinformation , 2021, Big Data Soc..

[8]  Myron Anthony Godinho,et al.  Knowledge co-creation in participatory policy and practice: Building community through data-driven direct democracy , 2021, Big Data & Society.

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[10]  K. Mandl,et al.  Knowing when to act: A call for an open misinformation library to guide actionable surveillance , 2021, Big Data Soc..

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[12]  Filippo Menczer,et al.  The COVID-19 Infodemic: Twitter versus Facebook , 2020, Big Data Soc..

[13]  Marcos G. Quiles,et al.  Fake news agenda in the era of COVID-19: Identifying trends through fact-checking content , 2020, Online Soc. Networks Media.

[14]  M. Buchanan.  Managing the infodemic , 2020, Nature Physics.

[15]  Nicholas Dias,et al.  Researching Fact Checking: Present Limitations and Future Opportunities , 2020 .

[16]  Philip Mai,et al.  Going viral: How a single tweet spawned a COVID-19 conspiracy theory on Twitter , 2020, Big Data Soc..

[17]  Matthew Zook,et al.  Viral Data , 2020 .

[18]  M. De Domenico,et al.  Bots are less central than verified accounts during contentious political events , 2020, Proceedings of the National Academy of Sciences.

[19]  G. Eysenbach.  How to Fight an Infodemic: The Four Pillars of Infodemic Management , 2020, Journal of medical Internet research.

[20]  M. De Domenico,et al.  Framework for Managing the COVID-19 Infodemic: Methods and Results of an Online, Crowdsourced WHO Technical Consultation , 2020, Journal of medical Internet research.

[21]  Melodie Yun-Ju Song,et al.  Examining algorithmic biases in YouTube's recommendations of vaccine videos , 2020, Int. J. Medical Informatics.

[22]  A. Gruzd,et al.  Inoculating Against an Infodemic: A Canada-Wide Covid-19 News, Social Media, and Misinformation Survey , 2020 .

[23]  M. De Domenico,et al.  Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics , 2020, Nature Human Behaviour.

[24]  Emilio Ferrara,et al.  Bots increase exposure to negative and inflammatory content in online social systems , 2018, Proceedings of the National Academy of Sciences.

[25]  Manlio De Domenico,et al.  Influence of augmented humans in online interactions during voting events , 2018, PloS one.

[26]  Emilio Ferrara,et al.  Social Bots Distort the 2016 US Presidential Election Online Discussion , 2016, First Monday.