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In response to the escalating threat of misinformation, social media platforms have introduced a wide range of interventions aimed at reducing the spread and influence of false information. However, there is a lack of a coherent macro-level perspective that explains how these interventions operate independently and collectively.

Misinformation research often draws optimistic conclusions, with fact-checking, for example, being established as an effective means of reducing false beliefs. However, it rarely considers the details of socioeconomic disparities that often shape who is most vulnerable to science misinformation. Historical and systemic inequalities have fostered mistrust in institutions, limiting access to credible information, for example, when Black patients distrust public health guidance due to past medical racism.

Experts consider misinformation a significant societal concern due to its associated problems like political polarization, erosion of trust, and public health challenges. However, these broad effects can occur independently of misinformation, illustrating a misalignment with the narrow focus of the prevailing misinformation concept.

We used a modified Delphi method to elicit and synthesize experts’ views on disinformation, misinformation, and malign influence (DMMI). In a three-part process, experts first independently generated a range of effective strategies for combatting DMMI, identified the most impactful barriers to combatting DMMI, and proposed areas for future research.

The field of misinformation is facing several challenges, from attacks on academic freedom to polarizing discourse about the nature and extent of the problem for elections and digital well-being. However, we see this as an inflection point and an opportunity to chart a more informed and contextual research practice.

Academic journals, archives, and repositories are seeing an increasing number of questionable research papers clearly produced using generative AI. They are often created with widely available, general-purpose AI applications, most likely ChatGPT, and mimic scientific writing. Google Scholar easily locates and lists these questionable papers alongside reputable, quality-controlled research.

We reviewed 555 papers published from 2016–2022 that presented misinformation to participants. We identified several trends in the literature—increasing frequency of misinformation studies over time, a wide variety of topics covered, and a significant focus on COVID-19 misinformation since 2020. We also identified several important shortcomings, including overrepresentation of samples from the United States and Europe and excessive emphasis on short-term consequences of brief, text-based misinformation.

The proliferation of misinformation has prompted significant research efforts, leading to the development of a wide range of interventions. There is, however, insufficient guidance on how to evaluate these interventions. Here, we argue that researchers should consider not just the interventions’ primary effectiveness but also ancillary outcomes and implementation challenges.

We surveyed 150 academic experts on misinformation and identified areas of expert consensus. Experts defined misinformation as false and misleading information, though views diverged on the importance of intentionality and what exactly constitutes misinformation. The most popular reason why people believe and share misinformation was partisanship, while lack of education was one of the least popular reasons.