GCRI · Labs
Laboratuvara pêşeroja medyayê
Information ecosystems decide what people know about risk: which warnings reach them, which claims they believe and which institutions they trust. This lab studies news, platforms, recommender pergalan, synthetic media and verification practice, and tests media innovations before they scale. This page sets out how misinformation and information gaps develop and spread, how interventions are evaluated, and who decides on the evidence.
Xebat çawa dimeşe.
Testbed operations
Future of media lab · Field lab coast · Campaign 3
Sensor deployment
peravê nimûneyî
Sensors
| S-01 tide | 95 | 0.3 | Nominal |
| S-04 salinity | 86 | 0.8 | Recalibrate |
| S-07 wave | 90 | 0.1 | Nominal |
Sensor vs reference
ŞeklTestbed operations: example names and illustrative values, not a record or a live system.
Future of media lab: how the risk develops.
Information ecosystems and responsible media innovation. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Amplification by design
Ranking pergalan optimise for engagement, and content that provokes strong reactions can travel further. Small changes in ranking rules alter what large audiences see, which makes platform design a research object in its own right.
Synthetic media lowers the cost of deception
Generated images, audio and video can impersonate people and events cheaply. Provenance standards and detection help, but neither is complete, and the existence of fakes also lets genuine evidence be dismissed.
News deserts and trust gaps
Where local journalism closes, fewer sources report on councils, courts and hazards. Gaps are filled by rumour or partisan content, and trust in institutions shapes how warnings are received when they arrive.
Interventions can backfire
Labels, corrections, prebunking and friction aim to reduce false beliefs. Effects measured in surveys may fade, may not change behaviour, or may raise scepticism of accurate content too, which is why field evaluation matters.
Stok, herik, dereng û sînorê guherînê.
Du stok, zext û kapasîteya adaptasyonê, bi xelekeyeke mezinbûnê ya xurtker, bersiveke hevsengiyê ya dereng û zirarê dema ku zext ji kapasîteyê derbas dibe têne girêdan; li derveyî sînor pergal dikare biguhere. Modela fêrkirinê bi nirxên nimûneyî yên diyarkirî, ne pêşbînî.
Kontrol
- Zêdebûna zextê
- 6.0% salê
- Bandora kapasîteyê di kêmkirina zextê de
- 4.0% salê
- Bersiva veberhênanê li hember zextê ya hatî dîtin
- 20%
- Derengiya têgihîştinê
- 4.0 sal
- Sînorê guherînê yê zextê
- 60 indeks
- Zêdebûn li derveyî sînor
- 2.0 carî
- Salên simulekirî
- 50 sal
Xwendin
- Zexta herî bilind
- 113 nîşander
- Kapasîte di dawiyê de
- 186 indeks
- Salên li ser sînor
- 15.8 sal
Jimar, rêbaz û çavkanî
| Sal | Zextê | Kapasîte | Hatî dîtin |
|---|---|---|---|
| 0 | 20.0 | 50.0 | 20.0 |
| 4 | 23.3 | 44.9 | 21.2 |
| 8 | 27.2 | 42.2 | 24.0 |
| 12 | 31.7 | 42.3 | 27.7 |
| 16 | 36.8 | 45.5 | 32.2 |
| 20 | 42.3 | 51.5 | 37.2 |
| 24 | 47.9 | 60.3 | 42.6 |
| 28 | 53.2 | 71.3 | 48.0 |
| 32 | 57.8 | 84.3 | 53.1 |
| 36 | 66.4 | 98.4 | 58.4 |
| 40 | 82.7 | 115.9 | 69.7 |
| 44 | 97.8 | 139.7 | 83.8 |
| 48 | 109.1 | 169.4 | 97.3 |
Rêbaz
- Dînamîkên pergalê Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
- Veguherînên krîtîk û xalên guherînê Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
- Diagrama xelekeya sedemî Sterman (2000), Business Dynamics, McGraw-Hill
- Pankeya nediyarîyê (Monte Carlo ya parametreyan) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- Hesasiyeta yek-yek (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Dane
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.
Li seranserê şeş stûnan, bi pêşengiya Nexus Consortium
- Ceribandin Labs di Labs de
- Hatiye avakirin di Labs di Foundry de
- Tê ceribandin li Labs li Universe
- Hatiye kodkirin di Dane û delîl di Standards de
- Hatiye weşandin di Labs di Reports de
- Tê de tê dayîn Fakulteya Zanista xetereyê û Rêbazan di Academy de
The media lab studies how risk information travels in each domain.
Findings feed the rêzeya Labs Evidence. The UN Global Principles for Yekparebûna Agahiyê (2024) frame the work.
- Media datasets under terms.
- Experiment proposals.
How future of media lab is modelled, measured and tested.
Model, texmînên wî, daneyên wî û nediyariya wî, bi şeweyê ku zanyar an endezyar kontrol bike. Avahî rêbazên weşandî dişopînin. Nirxên hatine xêzkirin nimûneyî ne, ne encam in.
Model, hevkêşe û texmîn
The model structure used for future of media lab, the assumptions it makes and how each is tested.
Ri = f(Hi, Ei, Vi, Ci)metirsî, rûbirûbûn, lawazî û kapasîteya serederiyêEAL = ∑k pk · Lkli ser bûyerên k bi îhtîmala salane pkxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| Texmîn | Çima tê çêkirin | Çawa tê ceribandin |
|---|---|---|
| Metirsî û rûbirûbûn di nav bûyerekê de serbixwe ne | Entegrala windahiyê ji hesabkirinê re maqûl dihêle | Bi lihevhatineke copula li ser tomarên hevbeş re bidin ber hev |
| Girêdan li nêzî xala xebatê xêzik in | Dihêle şok di A de belav bibin | Ji derveyî rêza xêzik tevlihev bike û bide ber hev |
| Xêzên lawaziyê di navbera cihan de têne veguhastin | Xêzikên herêmî kêm tên dîtin | Li hember windahiyên çavdêrîkirî erê bike li cihê ku hene |
- Têketin
- danehevên bi guhertoyan û çavkaniyê
- Derketin
- belavkirin, tu carî jimarên tenê
- Kod
- bi qerta modelê û ceribandinan re hat weşandin
- Zêdebûn
- heman dane, kod, jîngeh û tov
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 | Nîşan |
|---|---|---|---|---|
| Exposure to flagged content | proportion of impressions | per study | Platform researcher data access | ÇAVDÊRÎKIRÎ |
| Belief accuracy | proportion of true and false claims correctly rated | per study | Survey experiments | ÇAVDÊRÎKIRÎ |
| Truth discernment | difference between belief in true and false items | per study | Survey experiments | TEXMÎNKIRÎ |
| Trust in news | percent of respondents | annual | Audience surveys | REPORTED |
| Provenance credential coverage | percent of published media carrying provenance data | annual | Publisher and platform audits | ÇAVDÊRÎKIRÎ |
| Local news coverage | news outlets per local area | annual | News outlet census | TEXMÎNKIRÎ |
| Researcher data access granted | count of approved access requests | annual | Platform transparency reports | DECLARED |
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.
| Çavkanî | What it gives | Licence class |
|---|---|---|
| Reuters Institute Digital News Report (University of Oxford) | Cross-national survey of news use, trust and platform reliance | Report free access; data by request |
| Media Pluralism Monitor (European University Institute) | Risks to media pluralism by country and area | Open access, attribution |
| DSA Transparency Database (European Commission) | Statements of reasons for content moderation decisions by platforms | Free and open, EU reuse terms |
| Code of Conduct on Disinformation transparency centre | Signatory reports on measures against disinformation | Free access, terms of use |
| GDELT Project | Global news event and tone records from monitored media | Free and open, attribution |
| ClaimReview structured data (schema.org) | Structured fact checks published by verification organisations | Mercên bikaranînê |
Who decides, and what the evidence serves.
Labs prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Kî biryarê dide | The question | Evidence this page serves |
|---|---|---|
| Media and communications regulators | Whether platform risk assessments and mitigation measures meet legal duties | Independent audits, transparency database records, researcher access evidence |
| Newsroom editors and publishers | Whether to adopt a verification, labelling or provenance practice | Field evaluations of the practice with audience and trust outcomes |
| Platform trust and safety teams | Which interventions to deploy at scale and how to monitor side effects | Randomised evaluations with effects on accurate as well as false content |
| Public health and emergency communicators | How to issue warnings that reach, and are believed by, different audiences | Reach, trust and comprehension evidence by audience group |
Outputs this topic produces
- Preregistered field and survey experiments on corrections, labels and prebunking
- Audit designs for ranking and recommender pergalan under researcher access rules
- Provenance and verification test protocols for synthetic media
- Maps of local information gaps with sources stated
What experts ask about information ecosystems.
- How do accurate and inaccurate claims spread, and what slows each?
- Which sources do different communities trust during an emergency, and why?
- How do recommendation pergalan and platform design shape what people see?
- Which corrections, labels or warnings change belief or behaviour, and for how long?
- How can synthetic media be detected, and what are the limits of detection?
How information systems behave and fail.
Information spreads through networks of people and platforms. Network science shows that a few highly connected nodes can make diffusion fast and that clustering can create communities where some claims circulate and others do not. Platform design adds feedback: content that draws engagement is shown more, and engagement does not track accuracy.
Trust is a central variable. During disasters and health emergencies, people combine authority warnings with what they hear from family, local leaders and online sources. Where trust in institutions is low, accurate warnings may be ignored and rumours fill the gap. Misinformation, shared in error, and disinformation, shared deliberately, have different drivers and call for different responses.
Generative AI lowers the cost of producing convincing text, images, audio and video. This affects both the volume of misleading content and the credibility of genuine evidence, which can be dismissed as fabricated.
Research methods and their limits.
- 01
Content and network analysis
Mapping how material moves across accounts and platforms. Platform data access is restricted and changes, which limits replication.
- 02
Survey and panel research
Measuring trust, exposure and belief over time. Self-reports are imperfect and samples may miss key groups.
- 03
Randomised experiments
Testing labels, corrections and warning formats under ethics review. Laboratory effects may differ from real-world use.
- 04
Detection evaluation
Testing tools that identify synthetic media, using machine learning methods. Detection is an arms race and accuracy declines as generators improve.
Research on information is itself sensitive. The lab is designed to protect participant privacy, avoid amplifying harmful content and respect freedom of expression.
How it is studied, and what the methods cannot say.
- 01Encamgirtina sedemî û nirxandina bandorêRandomised pilots and stepped rollouts · Quasi-experimental programme evaluationApplied here
- 02Zîrekiya çêkirî û fêrbûna makîneyêAssisted screening for systematic reviews · Surrogate models to guide experimentsApplied here
- 03Îhtîmal û statîstîkSample size and power planning · Meta-analysis with bias assessmentApplied here
- 04Pîvandina nediyariyê û rastandina modelêMeasurement uncertainty budgets · Sensitivity analysis of simulation modelsApplied here
Where this page’s references lead.
Sazî û belgeyên ku ev rûpel li hember wan tê xwendin. Rewş û çap diguherin; her girêdan diçe ba saziya derxistinê.
Pirs û bersiv.
Why not simply measure how much misinformation exists?
Do corrections work?
How do researchers get platform data?
Does the lab rate news sources or decide what is true?
How does the lab handle harmful content in research?
Can a media organisation propose a study?
Ji vê rûpelê delîl bidin.
rêzeya Pergalên Stratejîk 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.
- E-nameDergeh GW-GCRIJi vê rûpelê bişîninDelîl, rastkirin, doz, rêbaz, nirxandin, wergerandin an têketina profîlê. Bi nîşana vê rûpelê digihîje, ji ber vê yekê edîtorê xwediyê wê wê bistîne.
- ZenodoDepoya vekirîWê razînin û ji civakê re bişîninTomara xwe li Zenodo bi ORCID iD ya xwe biweşînin, paşê wê li wir ji civaka Reports re bişînin. Kurator wê vedikolin; DOI ya we dimîne.
- rêzeya Pergalên StratejîkGCRIHemû rêyên beşdariyê bibîneCureyên beşdariyê, her yek çawa tê nirxandin, tu çi navlêkirinê distînî, lîsans û tiştê ku qet nayê qebûlkirin.
Tu carî daneyên şexsî, hûrgiliyên binesaza hesas an tiştekî ku mafê we yê parvekirinê tune ne neşînin. Beşdarî berî bikaranînê tê nirxandin; şandina wê nivîskarî an mafên edîtorî nade û sponsorî tu carî encamekê nakire.
Rêbazek, danehevek an pirsek bîne.
Agahî û pirs îro vekirî ne. Pirsek referansekê distîne û gava piştî wê dide nasîn; serlêdan, drav, veqetandina hesabkirinê û tomarkirin tenê dema ku tîm, mercên wê û kapasîteya vekolînê hebin vedibin.
Belgeyên hiqûqî.
Nexşe, dane û jimarên vê malperê ji bo çarçoveyê û agahiyên giştî têne nîşandan. Ew ne şîret, ne pêşbînî û ne hişyarî ne; sînorên agahiyê di Daxuyaniyên Berpirsiyariyê û Daxuyaniyên Daneyê de hatine diyarkirin.