Reducing Health Misinformation in Science: A Call to Arms

A Normative Framework for Assessing the Information Curation Algorithms of the Internet

It is critical to understand how algorithms structure the information people see and how those algorithms support or undermine society's core values. We offer a normative framework for the assessment of the information curation algorithms that determine much of what people see on the internet. The framework presents two levels of assessment: one for individual-level effects and another for systemic effects. With regard to individual-level effects we discuss whether (a) the information is aligned with the user's interests, (b) the information is accurate, and (c) the information is so appealing that it is difficult for a person's self-regulatory resources to ignore ("agency hacking"). At the systemic level we discuss whether (a) there are adverse civic-level effects on a system-level variable, such as political polarization; (b) there are negative distributional or discriminatory effects; and (c) there are anticompetitive effects, with the information providing an advantage to the platform. The objective of this framework is both to inform the direction of future scholarship as well as to offer tools for intervention for policymakers.

Beyond misinformation: developing a public health prevention framework for managing information ecosystems

The COVID-19 pandemic has highlighted how infodemics (defined as an overabundance of information, including misinformation and disinformation) pose a threat to public health and could hinder individuals from making informed health decisions. Although public health authorities and other stakeholders have implemented measures for managing infodemics, existing frameworks for infodemic management have been primarily focused on responding to acute health emergencies rather than integrated in routine service delivery. We review the evidence and propose a framework for infodemic management that encompasses upstream strategies and provides guidance on identifying different interventions, informed by the four levels of prevention in public health: primary, secondary, tertiary, and primordial prevention. On the basis of a narrative review of 54 documents (peer-reviewed and grey literature published from 1961 to 2023), we present examples of interventions that belong to each level of prevention. Adopting this framework requires proactive prevention and response through managing information ecosystems, beyond reacting to misinformation or disinformation.

Cancer: A model topic for misinformation researchers

Although cancer might seem like a niche subject, we argue that it is a model topic for misinformation researchers, and an ideal area of application given its importance for society. We first discuss the prevalence of cancer misinformation online and how it has the potential to cause harm. We next examine the financial incentives for those who profit from disinformation dissemination, how people with cancer are a uniquely vulnerable population, and why trust in science and medical professionals is particularly relevant to this topic. We finally discuss how belief in cancer misinformation has clear objective consequences and can be measured with treatment adherence and health outcomes such as mortality. In sum, cancer misinformation could assist the characterization of misinformation beliefs and be used to develop tools to combat misinformation in general.

Source-credibility information and social norms improve truth discernment and reduce engagement with misinformation online

Misinformation on social media is a pervasive challenge. In this study (N = 415) a social-media simulation was used to test two potential interventions for countering misinformation: a credibility badge and a social norm. The credibility badge was implemented by associating accounts, including participants', with a credibility score. Participants' credibility score was dynamically updated depending on their engagement with true and false posts. To implement the social-norm intervention, participants were provided with both a descriptive norm (i.e., most people do not share misinformation) and an injunctive norm (i.e., sharing misinformation is the wrong thing to do). Both interventions were effective. The social-norm intervention led to reduced belief in false claims and improved discrimination between true and false claims. It also had some positive impact on social-media engagement, although some effects were not robust to alternative analysis specifications. The presence of credibility badges led to greater belief in true claims, lower belief in false claims, and improved discrimination. The credibility-badge intervention also had robust positive impacts on social-media engagement, leading to increased flagging and decreased liking and sharing of false posts. Cumulatively, the results suggest that both interventions have potential to combat misinformation and improve the social-media information landscape.

The Online Misinformation Engagement Framework

Research on online misinformation has evolved rapidly, but organizing its results and identifying open research questions is difficult without a systematic approach. We present the Online Misinformation Engagement Framework, which classifies people's engagement with online misinformation into four stages: selecting information sources, choosing what information to consume or ignore, evaluating the accuracy of the information and/or the credibility of the source, and judging whether and how to react to the information (e.g., liking or sharing). We outline entry points for interventions at each stage and pinpoint the two early stages-source and information selection-as relatively neglected processes that should be addressed to further improve people's ability to contend with misinformation.

Effective correction of misinformation

This paper reviews correction effectiveness, highlighting which factors matter, which do not, and where further research is needed. To boost effectiveness, we recommend using detailed corrections and providing an alternative explanation wherever possible. We also recommend providing a reminder of the initial misinformation and repeating the correction. Presenting corrections pre-emptively (i.e., prebunking) or after misinformation exposure is unlikely to greatly impact correction effectiveness. There is also limited risk of repeating misinformation within a correction or that a correction will inadvertently spread misinformation to new audiences. Further research is needed into which correction formats are most effective, whether boosting correction memorability can enhance effectiveness, the effectiveness of discrediting a misinformation source, and whether distrusted correction sources can contribute to corrections backfiring.

Examining the replicability of backfire effects after standalone corrections

Corrections are a frequently used and effective tool for countering misinformation. However, concerns have been raised that corrections may introduce false claims to new audiences when the misinformation is novel. This is because boosting the familiarity of a claim can increase belief in that claim, and thus exposing new audiences to novel misinformation-even as part of a correction-may inadvertently increase misinformation belief. Such an outcome could be conceptualized as a familiarity backfire effect, whereby a familiarity boost increases false-claim endorsement above a control-condition or pre-correction baseline. Here, we examined whether standalone corrections-that is, corrections presented without initial misinformation exposure-can backfire and increase participants' reliance on the misinformation in their subsequent inferential reasoning, relative to a no-misinformation, no-correction control condition. Across three experiments (total N = 1156) we found that standalone corrections did not backfire immediately (Experiment 1) or after a one-week delay (Experiment 2). However, there was some mixed evidence suggesting corrections may backfire when there is skepticism regarding the correction (Experiment 3). Specifically, in Experiment 3, we found the standalone correction to backfire in open-ended responses, but only when there was skepticism towards the correction. However, this did not replicate with the rating scales measure. Future research should further examine whether skepticism towards the correction is the first replicable mechanism for backfire effects to occur.