GRF · Information Integrity and Public Trust
Misinformation and disinformation
Misinformation is false or misleading content shared without intent to deceive; disinformation is spread deliberately, often in coordinated campaigns. Both exploit attention, emotion and gaps in trusted information. Responses range from labelling and prebunking to platform rules and law, and each carries risks to free expression. This page sets out how false information spreads and is countered, the records that measure it, and who decides.
Misinformation and disinformation on the record.
Misinformation and disinformation: how the risk develops.
Information harms and analytical limits. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Engagement rewards the sensational
Ranking systems that optimise for engagement favour emotive and novel content, which false claims often are. Reach grows faster than corrections can follow.
Coordination amplifies
Networks of accounts, paid influencers and automated tools make fringe narratives look popular. Coordinated inauthentic behaviour is a pattern of conduct as much as a content problem.
Gaps invite false claims
Where authority information is late, unclear or distrusted, people fill the gap with whatever circulates. Data voids on new topics are exploited quickly.
Remedies risk overreach
Laws against false information can be used against journalists and critics. Responses that target behaviour and transparency carry less risk to expression than bans on content.
What tips a population into acting.
How the share of early movers and the thresholds people hold decide whether a protective behaviour, or a false narrative, stays local or spreads to most people. Teaching model with stated illustrative values, not a prediction.
Controls
- Mean threshold (share of neighbours)
- 18%
- Spread of thresholds
- 8 points
- Early movers
- 2.0% of people
- Contacts per person
- 8
Readouts
- Share who acted in the end
- 100%
- Global cascade
- Yes
- Steps to settle
- 11
Numbers, methods and sources
| Step | Share who have acted (%) |
|---|---|
| 0 | 1.8 |
| 1 | 5.3 |
| 2 | 14.0 |
| 3 | 29.8 |
| 4 | 49.8 |
| 5 | 67.3 |
| 6 | 80.5 |
| 7 | 89.8 |
| 8 | 97.8 |
| 9 | 99.8 |
| 10 | 100.0 |
| 11 | 100.0 |
Methods
- Threshold models of collective behaviour Granovetter (1978), American Journal of Sociology 83
- Global cascades on random networks Watts (2002), PNAS 99
- Complex contagions Centola and Macy (2007), American Journal of Sociology 113
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme's own evidence.
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Information Integrity and Public Trust in Foundry
- Tested at Information Integrity and Public Trust in Universe
- Codified in Governance, Security and Society in Standards
- Published in Information Integrity and Public Trust in Reports
- Taught in Faculty of Governance, Policy and International Affairs in Academy
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Coordinated inauthentic behaviour networks removed | count by platform and origin | quarterly | Platform threat reports | REPORTED |
| Fact-checks published | count by country and topic | monthly | Fact-checking databases | REPORTED |
| Exposure to false content | share of users exposed | per study | Research audits and panels | ESTIMATED |
| Prevalence of misinformation in sampled content | share of sampled items | per study | Content audits | ESTIMATED |
| Laws penalising false information | count by country | on change | Legal databases | Published statistics |
| Self-reported concern about what is real online | share of respondents | annual | Digital News Report survey | ESTIMATED |
From mandate to evaluation, by pillar.
The steps an instrument takes from mandate to evaluation, each with the pillar that runs it, and the decisions that stay with the authority. Teaching model with stated illustrative values, not a prediction.
- Mandate and problem framing Competent authority (outside the programme)
A competent authority frames the problem and the mandate under its own rules. Howlett, Ramesh and Perl (2009) - Evidence and system model Foundry
The system is modelled with its feedbacks, data and stated limits. OECD (2014), principle 2 - Options appraised with scenarios Foundry
Instruments are compared under scenarios, with uncertainty shown. Hood (1983) - Exercise with stakeholders Universe
The options are tested in an exercise with those who would implement and bear them. OECD (2014), principle 3 - Standard or guidance drafted Nexus Standards
What works is codified as a standard or guidance, open to comment. Nexus Standards process - Decision by the authority Competent authority (outside the programme)
The authority decides, outside the programme, with the evidence on record. Howlett, Ramesh and Perl (2009) - Capacity and training Risk Academy
Those who implement are trained on the decided instrument and its data. Sendai Framework (2015), priority 2 - Monitoring and public report Reports
Indicators are published with sources, limits and corrections. OECD (2014), principle 5 - Evaluation and revision Nexus Standards
Results return to the standard and the model: the cycle closes. Howlett, Ramesh and Perl (2009)
Methods
- Policy cycle Howlett, Ramesh and Perl (2009), Studying Public Policy, Oxford University Press
- Governance of critical risks OECD (2014), Recommendation of the Council on the Governance of Critical Risks
- Layered drawing of directed graphs Sugiyama, Tagawa and Toda (1981), IEEE SMC 11
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Information Integrity and Public Trust in Foundry
- Tested at Information Integrity and Public Trust in Universe
- Codified in Governance, Security and Society in Standards
- Published in Information Integrity and Public Trust in Reports
- Taught in Faculty of Governance, Policy and International Affairs in Academy
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| EU Disinformation Code transparency centre reports | Signatories' reports on implementing commitments | Free to read, terms to check |
| Platform threat and coordinated behaviour reports | Networks removed and tactics observed | Free to read, terms per platform |
| Google Fact Check Explorer and ClaimReview data | Published fact-checks tagged with structured data | Free to access, terms to check |
| Reuters Institute Digital News Report | Survey of news use and concern about misinformation across many countries | Free to read, terms to check |
| EUvsDisinfo database | Cases of pro-Kremlin disinformation compiled by the EEAS East StratCom Task Force | Free to read, EU terms |
How misinformation and disinformation is governed: instruments, mandates and commitments.
The instruments, mandates, actors and accountability arrangements a policymaker, diplomat or development agency works with, citing the agreements that set them. Structures follow the frameworks named. Any state or progress drawn is illustrative, not a record.
Accountability cycle
How commitments on misinformation and disinformation are held to account, from commitment to correction.
- CommitGovernmentA commitment with a mandate and a legal basis.
- PlanLead agencyA national plan with owners and milestones.
- ResourceMinistry of financeA budget line or financing on record.
- ImplementAgencies and local governmentDelivery against the plan.
- ReportStatistics officeIndicators reported in agreed definitions.
- ReviewLegislature, audit, civil societyIndependent review of results and gaps.
- CorrectGovernmentChanges to the plan, published with reasons.
Who decides, and what the evidence serves.
Information Integrity and Public Trust prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Legislatures | Whether and how law addresses false information without restricting expression | Comparative laws and their documented use |
| Platform trust and safety teams | Which behaviours and content to act on and how | Network analyses and transparency practice |
| Digital services regulators | Whether platforms assess and mitigate systemic risks adequately | Risk assessments, audits and researcher evidence |
| Electoral authorities | How to protect elections from manipulation | Lessons from past elections and platform reports |
Outputs this topic produces
- Guide to the difference between misinformation, disinformation and malinformation
- Comparative notes on legal and platform responses and their risks to expression
- Dialogue briefs on prebunking and media literacy evidence
- Questions for regulators assessing disinformation measures
What experts ask.
Researchers on misinformation and disinformation ask questions that resist easy answers.
- How prevalent is false or misleading content, and how much of it do people actually see?
- Who shares it, why, and through which networks?
- Does exposure change beliefs and behaviour, and for whom?
- How are coordinated disinformation campaigns organised, and how can they be detected?
- Which responses reduce harm without suppressing legitimate speech?
Spread, susceptibility and context.
False content can spread through social networks, especially when it is novel, emotional or confirms existing beliefs. Network research shows that a small number of highly active accounts often account for much of the sharing. Coordinated campaigns, sometimes using automated or inauthentic accounts, can amplify narratives and make fringe views appear widespread.
Exposure does not translate simply into belief or action. Research suggests that effects are concentrated among those already predisposed, that people use many sources, and that distrust in institutions makes alternative narratives more attractive. Measuring harm is difficult, and estimates of prevalence depend heavily on definitions and data access. Overstating the problem can itself erode trust and justify disproportionate responses.
Why now: the UN Global Principles for Information Integrity (2024) and new platform rules, such as the European Union's Digital Services Act fully applicable since 2024, have set expectations, while generative AI lowers the cost of false content. The platform is designed to compare responses that protect freedom of expression; it does not fact-check.
How the evidence is built, and where it stops.
- 01
Content and network analysis
Tracing how content spreads using network science. Platform data access is limited and uneven.
- 02
Automated detection
Classifiers that flag likely false or coordinated content using machine learning. Error rates and biases need disclosure.
- 03
Experimental research
Testing effects of exposure and interventions with social and behavioural science. Lab settings may not reflect real behaviour.
- 04
Case investigation
Detailed study of specific campaigns. Attribution to actors is often uncertain.
How it is studied, and what the methods cannot say.
- 01Social and behavioural scienceMeasuring trust and exposure · Testing interventions · Qualitative study of information needsApplied here
- 02Network science and contagionDetecting coordinated behaviour · Tracing narrative spread · Mapping media ecosystemsApplied here
- 03Artificial intelligence and machine learningClassifier error in moderation · Synthetic media detection limits · Recommender auditsApplied here
Questions and answers.
What is the difference between misinformation and disinformation?
What is prebunking?
Do laws against false news work?
Is all false information harmful?
Can disinformation be reliably attributed to its source?
Do fact-checks work?
Contribute evidence from this page.
Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
- EmailGateway GW-GRFSend it from this pageEvidence, a correction, a case, a method, a review, a translation or profile input. It arrives tagged with this page, so the editor who owns it receives it.
- ZenodoOpen depositDeposit it and submit to the communityPublish your own record on Zenodo with your ORCID iD, then submit it to the Reports community there. Curators review it; the DOI stays yours.
- ReportsGlobal Risks ForumSee every way to contributeThe contribution types, how each is reviewed, what credit you receive, the licence and what is never accepted.
Never send personal data, sensitive infrastructure detail or anything you have no right to share. A contribution is reviewed before use; sending it confers no authorship or editorial rights, and sponsorship never buys a finding.
Bring a governance question to the Forum.
Information and enquiries are open today. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.
Legal documents.
Show all 18 legal documents
Maps, data and figures on this site are shown for context and general information. They are not advice, forecasts or warnings; the limits of the information are set out in the Disclaimers and Data Notices.