The article explores the concept of infodemics during the COVID-19 pandemic, focusing on the propagation of false or inaccurate information proliferating worldwide throughout the SARS-CoV-2 health crisis. We provide an overview of disinformation, misinformation and malinformation and discuss the notion of “fake news”, and highlight the threats these phenomena bear for health policies and national and international security. We discuss the mis-/disinformation as a significant challenge to the public health, intelligence, and policymaking communities and highlight the necessity to design measures (...) enabling the prevention, interdiction, and mitigation of such threats. We then present an overview of selected opportunities for applying technology to study and combat disinformation, outlining several approaches currently being used to understand, describe, and model the phenomena of misinformation and disinformation. We focus specifically on complex networks, machine learning, data- and text-mining methods in misinformation detection, sentiment analysis, and agent-based models of misinformation spreading and the detection of misinformation sources in the network. We conclude with the set of recommendations supporting the World Health Organization’s initiative on infodemiology. We support the implementation of integrated preventive procedures and internationalization of infodemic management. We also endorse the application of the cross-disciplinary methodology of Crime Science discipline, supplemented by Big Data analysis and related information technologies to prevent, disrupt, and detect mis- and disinformation efficiently. (shrink)

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. Sources of vaccine misinformation are known; there is a need to operationalize learnings and engage the pro-vaccination majority in debunking vaccine-related misinformation. 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. 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. (shrink)

COVID-19 is unique in that it is the first global pandemic occurring amidst a crowded information environment that has facilitated the proliferation of misinformation on social media. Dangerous misleading narratives have the potential to disrupt ‘official’ information sharing at major government announcements. Using an interrupted time-series design, we test the impact of the announcement of the first UK lockdown on short-term trends of misinformation on Twitter. We utilise a novel dataset of all COVID-19-related social media posts on Twitter from the (...) UK 48 hours before and 48 hours after the announcement. We find that while the number of tweets increased immediately post announcement, there was no evidence of an increase in misinformation-related tweets. We found an increase in COVID-19-related bot activity post-announcement. Topic modelling of misinformation tweets revealed four distinct clusters: ‘government and policy’, ‘symptoms’, ‘pushing back against misinformation’ and ‘cures and treatments’. (shrink)

Medical and public health professionals recommend wearing face masks to combat the spread of the coronavirus disease of 2019. While the majority of people in the United States support wearing face masks as an effective tool to combat COVID-19, a smaller percentage declared the recommendation by public health agencies as a government imposition and an infringement on personal liberty. Social media play a significant role in amplifying public health issues, whereby a minority against the imposition can speak loudly, perhaps using (...) tactics of verbal aggression taking the form of toxic language. We investigated the role that toxicity plays in the online discourse around wearing face masks. Overall, we found tweets including anti-mask hashtags were significantly more likely to use toxic language, while tweets with pro-mask hashtags were somewhat less toxic with the exception of #WearADamnMask. We conclude that the tensions between these two positions raise doubt and uncertainty around the issue, which make it difficult for health communicators to break through the clutter in order to combat the infodemic. Public health agencies and other governmental institutions should monitor toxicity trends on social media in order to better ascertain prevailing sentiment toward their recommendations and then apply these data-driven insights to refine and adapt their risk communication messaging toward mask wearing, vaccine uptake, and other interventions. (shrink)

A large body of information and opinion related to COVID-19 is being shared via social media platforms. Recent reports have raised concerns about the reliability and verifiability of said information being disseminated and the way systems, processes and design of the platforms facilitates such spread. This, alongside other areas of concern, has resulted in several social media platforms taking steps towards tackling the spread of mis- and dis-information. Here we discuss approaches to online public health messaging from a range of (...) sources during COVID-19, with a focus on official and non-official sources in the United Kingdom. We highlight issues for ongoing public health decisions, and the potential impact for the future course of the pandemic. (shrink)

The design and reporting of data-driven studies seeking to measure misinformation are patchy and inconsistent, and these studies rarely measure associations with, or effects on, behaviour. The consequence is that data-driven misinformation studies are not yet useful as an empirical basis for guiding when to act on emerging misinformation threats, or for deciding when it is more appropriate to do nothing to avoid inadvertently amplifying misinformation. In a narrative review focused on examples of health-related misinformation, we take a critical perspective (...) of data-driven misinformation studies. To address this problem, we propose a curated and open library of misinformation examples and describe its structure and how it might be used to support actionable surveillance. We draw on experiences with other curated repositories to speculate on the likely challenges related to achieving critical mass and maintaining data consistency. We conclude that an open library of misinformation could help improve the consistency of data-driven misinformation study design and reporting, as well as provide an empirical basis from which to make decisions about how to act on new and emerging misinformation threats. (shrink)

In late March of 2020, a new hashtag, #FilmYourHospital, made its first appearance on social media. The hashtag encouraged people to visit local hospitals to take pictures and videos of empty hospitals to help “prove” that the COVID-19 pandemic is an elaborate hoax. Using techniques from Social Network Analysis, this case study examines how this conspiracy theory propagated on Twitter and whether the hashtag virality was aided by the use of automation or coordination among Twitter users. We found that while (...) much of the content came from users with limited reach, the oxygen that fueled this conspiracy in its early days came from a handful of prominent conservative politicians and far right political activists on Twitter. These power users used this hashtag to build awareness about the campaign and to encourage their followers to break quarantine and film what is happening at their local hospitals. After the initial boost by a few prominent accounts, the campaign was mostly sustained by pro-Trump accounts, followed by a secondary wave of propagation outside the U.S. The rise of the #FilmYourHospital conspiracy from a single tweet demonstrates the ongoing challenge of addressing false, viral information during the COVID-19 pandemic. While the spread of misinformation can be potentially mitigated by fact-checking and directing people to credible sources of information from public health agencies, false and misleading claims that are driven by politics and supported by strong convictions and not science are much harder to root out. (shrink)

This study investigates the types of misinformation spread on Twitter that evokes scientific authority or evidence when making false claims about the antimalarial drug hydroxychloroquine as a treatment for COVID-19. Specifically, we examined tweets generated after former U.S. President Donald Trump retweeted misinformation about the drug using an unsupervised machine learning approach called the biterm topic model that is used to cluster tweets into misinformation topics based on textual similarity. The top 10 tweets from each topic cluster were content coded (...) for three types of misinformation categories related to scientific authority: medical endorsements of hydroxychloroquine, scientific information used to support hydroxychloroquine’s use, and a comparison group that included scientific evidence opposing hydroxychloroquine’s use. Results show a much higher volume of tweets featuring medical endorsements and use of supportive scientific information compared to accurate and updated scientific evidence, that misinformation-related tweets propagated for a longer time frame, and the majority of hydroxychloroquine Twitter discourse expressed positive views about the drug. Metadata from Twitter accounts found that prominent users within misinformation discourse were more likely to have media or political affiliation and explicitly expressed support for President Trump. Conversely, prominent accounts within the scientific opposition discourse primarily consisted of medical doctors or scientists but had far less influence in the Twitter discourse. Implications of these findings and connections to related social media research are discussed, as well as cognitive mechanisms for understanding susceptibility to misinformation and strategies to combat misinformation spread via online platforms. (shrink)

Engaging citizens with digital technology to co-create data, information and knowledge has widely become an important strategy for informing the policy response to COVID-19 and the ‘infodemic’ of misinformation in cyberspace. This move towards digital citizen participation aligns well with the United Nations’ agenda to encourage the use of digital tools to enable data-driven, direct democracy. From data capture to information generation, and knowledge co-creation, every stage of the data lifecycle bears important considerations to inform policy and practice. Drawing on (...) evidence of participatory policy and practice during COVID-19, we outline a framework for citizen ‘e-participation’ in knowledge co-creation across every stage of the policy cycle. We explore how coupling the generation of information with that of social capital can provide opportunities to collectively build trust in institutions, accelerate recovery and facilitate the ‘e-society’. We outline the key aspects of realising this vision of data-driven direct democracy by discussing several examples. Sustaining participatory knowledge co-creation beyond COVID-19 requires that local organisations and institutions incorporate adaptive learning mechanisms into their operational and governance structures, their integrated service models, as well as employing emerging social innovations. (shrink)