Meta’s 2025 decision to replace professional fact-checkers in the U.S. with a crowdsourced system similar to X’s Community Notes marks a defining moment in the evolution of efforts to combat disinformation. More than a simple policy adjustment, it reflects a broader shift among major platforms away from institutional partnerships with independent fact-checking organizations and towards models that increasingly rely on user participation and algorithmic mediation—what we prefer to call “crowdsourced validation.” This is the topic that Lucas Graves, Raquel Recuero and I have recently addressed in an Editorial for the Special Issue “From Fact-Checking to Community Notes,” published in Communication & Society.

At first glance, this shift by platforms might appear pragmatic. The scale of online content is overwhelming, and professional fact-checkers cannot feasibly keep pace with the volume, speed, and complexity of misleading information circulating on social media. From this perspective, involving users directly in content moderation seems both efficient and democratic. However, this framing risks obscuring a fundamental issue: what platforms are introducing is not simply a new form of fact-checking, but a qualitatively different approach to assessing information.

The distinction between professional fact-checking and systems like Community Notes hinges on a conceptual difference between verification and validation. While these terms are often used interchangeably in everyday language, they refer to distinct processes with different implications for how factual information is established and maintained in digital environments.

Professional fact-checking is grounded in verification. It involves a systematic process in which trained journalists evaluate claims using documented evidence, expert sources, and transparent methodologies. The aim is to determine whether a statement is true, false, or misleading, and to make that assessment reproducible and open to scrutiny. This commitment to evidence and transparency is what gives fact-checking its authority and credibility.

By contrast, crowdsourced systems do not aim to verify information based on documented evidence and transparent methodologies. Instead, they facilitate the addition of contextual information by users, which is then collectively evaluated through voting or rating mechanisms. The outcome is not a verified conclusion but a form of social agreement about what contextualisation is helpful or relevant. As such, these systems are better understood as tools of validation rather than verification.

A transformation in authority and practice

This shift from verification to validation reflects a deeper transformation in the epistemological foundations of the digital information ecosystem. At its core lies a question of authority: who gets to decide what counts as reliable information?

For over a decade, professional fact-checking has emerged as a key institutional response to the spread of online disinformation. Fact-checking organisations around the world have built reputations as trusted intermediaries, relying on clearly defined methodologies and editorial independence. Their work is supported by international networks like the International Fact-Checking Network (IFCN) and the European Fact-Checking Standards Network (EFCSN), which promote shared ethical and professional standards.

These organisations operate according to a logic similar to other forms of knowledge production, such as science or investigative journalism. Their conclusions are expected to be based on verifiable evidence, and their methods are designed to allow others to replicate or challenge their findings. In this sense, professional fact-checking aspires not only to correct misinformation but also to reinforce broader norms of accountability and transparency in public discourse.

Crowdsourced validation systems, in contrast, rely on different principles. Their theoretical foundation lies in the idea of the “wisdom of crowds”: the notion that large groups of individuals, each contributing small pieces of information, can collectively arrive at accurate fact verification. In systems like Community Notes, this idea is operationalised through algorithms that identify contributions deemed useful across users with different perspectives.

This model has clear advantages for the platforms. It allows them to scale moderation efforts in ways that would be impossible for any professional organisation. It also encourages user participation, potentially fostering a sense of collective responsibility for the quality of online information. A recent study even suggested that exposure to community-generated notes can reduce the spread of misleading content and increase user trust compared to simple warning labels.

Yet these gains do not benefit every actor in the information ecosystem and should be interpreted with caution. Unlike professional fact-checking, crowdsourced validation does not rely on standardised methods or established hierarchies of evidence. What counts as “useful” or “credible” context is determined through user ratings and algorithmic thresholds, rather than through systematic verification processes. As a result, outcomes may vary depending on who participates, how content is framed, and how algorithms aggregate user input.

This introduces important uncertainties, as consensus does not necessarily equate to accuracy, particularly in highly polarised environments where shared beliefs may diverge sharply across groups. Moreover, participation in these systems is uneven: some users contribute more than others, and certain perspectives may be overrepresented. These dynamics can shape what appears as collectively validated knowledge, potentially reinforcing existing biases rather than correcting them.

Another key difference lies in how transparency operates. Professional fact-checking emphasises transparency in methods and evidence. Fact-checks typically include detailed explanations of how conclusions were reached, allowing readers to evaluate the reasoning and sources involved. Crowdsourced systems offer a different kind of transparency: they may publish datasets or describe the general functioning of their algorithms, but they do not necessarily provide clear explanations for why particular notes are shown or suppressed in specific cases.

This distinction matters because transparency is closely linked to accountability. When users cannot easily understand why certain interpretations are prioritised, it becomes more difficult to assess the reliability of the system or to challenge its outcomes.

Importantly, the apparent decentralisation of authority in crowdsourced systems does not mean that platforms relinquish control. On the contrary, platform companies remain central actors, designing the rules and algorithms that structure participation, visibility, and evaluation. In this sense, the shift toward community-based moderation can be understood as a redistribution of responsibilities rather than a genuine transfer of power.

By adopting validation systems, platforms may reduce their reliance on external fact-checking organisations while still maintaining control over the information environment. They set the parameters within which users interact, determine which contributions gain prominence, and ultimately shape how information circulates. As a result, platform governance remains a critical dimension of the fight against disinformation, even in ostensibly decentralised models.

Complementarity, not replacement

Despite the narrative of replacement, the relationship between professional fact-checking and crowdsourced validation is more accurately described as complementary. In practice, the two models are often interconnected.

Research shows that contributors to systems like Community Notes frequently rely on information produced by fact-checking organisations, citing their work as evidence to support contextual explanations. This indicates that professional verification continues to play an essential role, even within participatory frameworks.

Rather than eliminating the need for expert verification, crowdsourced systems appear to extend its reach, acting as a bridge between professional knowledge and broader public engagement. However, this hybridisation also complicates the information ecosystem. It becomes harder to distinguish between content that has been rigorously verified and content that has simply been collectively endorsed.

For policymakers and practitioners, this distinction has significant implications. Regulatory frameworks designed to support fact-checking may not be directly applicable to crowdsourced systems, which operate according to different logics and standards. Similarly, media literacy initiatives must equip citizens with the skills to understand not only how to identify misinformation, but also how to interpret different forms of correction and contextualisation.

The sustainability of fact-checking itself is another pressing concern. As platforms reduce financial and institutional support, many organisations face increasing economic precarity. Yet their work remains foundational, not only as a source of verified information but also as a benchmark against which other forms of moderation are measured.

Looking ahead

The transition from professional fact-checking to crowdsourced validation marks a significant turning point in the governance of digital information. It reflects both the opportunities and the challenges of addressing disinformation at scale in a rapidly evolving media environment.

However, treating these systems as equivalent risks misunderstanding their respective roles. Fact-checking and validation are not interchangeable. They are based on different principles, produce different kinds of knowledge, and carry different implications for authority and accountability.

Crowdsourced validation can complement professional fact-checking by expanding participation and increasing the visibility of contextual information. But it cannot replace the systematic, evidence-based processes that underpin verification. Recognising this distinction is essential if we are to develop effective, balanced, and democratic responses to disinformation.