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In the October 2024 flash floods in Spain, social media posts falsely claimed authorities were concealing the number of casualties. The narrative centered on a flooded parking structure in Valencia, where hundreds of bodies were falsely claimed to be trapped. This narrative gained traction even after videos showing the premises had been evacuated.

Not all misinformation consequences are equal. Faced by hundreds of thousands of false claims online and offline every day, fact checkers need a robust way to identify the important ones to check. This scalable model—used by fact checkers in trials in Europe, Africa, and the Middle East since 2024—helps forecast the potential imminent and cumulative harms of different false claims and is an early warning system for society that focuses efforts on factually false claims that cause real-world harms.

Fact-checking has become a key response to disinformation during crises and conflicts, but its role is increasingly contested due to concerns about its effectiveness and its co-optation by different political actors. In polarized, high-choice environments, fact-checking is often embedded within partisan and state-aligned infrastructures, shaping validation and rejection of knowledge claims.

When a bank run, a pandemic, or an election spirals out of control, the spark is often informational. In 2023, rumors online helped accelerate the collapse of Silicon Valley Bank. During COVID-19, false claims about vaccines fueled preventable harms by undermining public trust in health guidance, and election lies in the United States fed into the broader dynamics that culminated in the January 6 Capitol attack.

What if nearly everything we think we know about misinformation came from just a sliver of the world? When research leans heavily on online studies from a few wealthy nations, we risk drawing global conclusions from local noise. A WhatsApp group of fishermen, a displaced community in a refugee camp, or a bustling market in the Global South are not marginal examples of information environments; such contexts call for an evolution of how we study misinformation.

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.

In February 2025, Google’s AI Overview fooled itself and its users when it cited an April Fool’s satire about “microscopic bees powering computers” as factual in search results (Kidman, 2025). Google did not intend to mislead, yet the system produced a confident falsehood.

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.

The potential for harm entrenched in mis- and disinformation content, regardless of intentionality, opens space for a new analytical agenda to investigate the weaponization of identity-based features like gender, race, and ethnicity through the lens of violence. Therefore, we lay out the triangle of violence to support new studies aiming to investigate multimedia content, victims, and audiences of false claims.

The climate lockdown conspiracies claim that a clandestine group of elites are planning to use climate change as a justification to enact widespread lockdowns and curtail freedoms. This conspiracy draws on a wide range of unconnected real-world events and suggests that their possibility of happening again is all the proof required.