GCRI · Society and Human Development
Technology and social cohesion
Social cohesion is the trust, shared norms and ties that let people cooperate across differences. Technology can strengthen it by connecting people and improving services, or weaken it through polarisation, surveillance, exclusion and loss of shared information. This page sets out how technology affects trust and participation, how cohesion is measured, and what evidence serves the governments and civil society organisations concerned.
Technology and social cohesion on the record.
Technology and social cohesion: how the risk develops.
Trust, misinformation and public outcomes. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Networks connect and sort
Digital networks let people find others with shared interests, which can build communities and also sort people into like-minded groups.
Surveillance erodes trust
When people believe they are watched, they may withdraw from public participation, especially groups that have been targeted before.
Exclusion divides
People without access or skills are left out of digital public services and conversations, widening social gaps.
Institutions anchor trust
Trust in institutions shapes whether people follow authority guidance in crises and accept collective decisions they disagree with.
People and exposure in Latin America and the Caribbean.
Population density, night lights and land cover over Latin America and the Caribbean, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
People and exposure, for this page
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
- Provider
- NASA LAADS DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite radiance, corrected for moonlight and atmosphere
- Newest at build
- 2026-10-12
- Resolution
- about 500 m grid
- Uncertainty
- Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (persistent cloud still leaves gaps)
- Source
- VIIRS NOAA-20 Level 3, 8-day (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- 8-day; Satellite 8-day composite; persistent cloud still leaves gaps
- Newest at build
- 2026-09-14
- Resolution
- about 1 km grid
- Uncertainty
- Satellite skin temperature of the ground over the 8 days ending on the date shown, a heat exposure indicator; not air temperature, heat index or a heat warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- MODIS Terra EVI (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite index; clouds leave gaps
- Newest at build
- 2026-10-13
- Resolution
- about 250 m grid
- Uncertainty
- Satellite vegetation index for the 8 days to the date shown, a habitat condition indicator; not a measure of species or biodiversity.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
People and exposure, framed on Latin America and the Caribbean.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA JPL OPERA DIST-ALERTSources and licences
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- Air quality forecast, 5 days, PM2.5 (CAMS via Open-Meteo): Contains modified Copernicus Atmosphere Monitoring Service information 2026.
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (9 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE · 13 Oct 2026
Technology can strengthen or weaken cohesion around shared risks.
Findings are designed for the Evidence Brief series. The UN Global Principles for Information Integrity (2024) frame the field; the page takes no political position.
- Trust surveys.
- Cases of cohesion under stress.
A threshold model of collective behaviour.
Each agent acts once enough of its contacts have; small changes in the mean threshold or the early movers decide whether the behaviour stays local or cascades through the population. Teaching model with stated illustrative values, not a prediction. The map on this page shows Latin America and the Caribbean; the model itself is not tied to a place.
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
- 13
Numbers, methods and sources
| Step | Share who have acted (%) |
|---|---|
| 0 | 0.8 |
| 1 | 3.5 |
| 2 | 11.0 |
| 3 | 22.5 |
| 4 | 39.0 |
| 5 | 54.5 |
| 6 | 68.8 |
| 7 | 81.0 |
| 8 | 90.5 |
| 9 | 95.5 |
| 10 | 97.8 |
| 11 | 99.5 |
| 12 | 100.0 |
| 13 | 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 Society and Human Development in Foundry
- Tested at Society and Human Development in Universe
- Codified in Governance, Security and Society in Standards
- Published in Society and Human Development in Reports
- Taught in Faculty of Health and Society 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 |
|---|---|---|---|---|
| Generalised trust | percent agreeing most people can be trusted | per survey | Values surveys | REPORTED |
| Trust in government | percent expressing confidence | per survey | Trust surveys | REPORTED |
| Affective polarisation | difference in feelings toward own and other groups | per survey | Election and social surveys | REPORTED |
| Civic participation | percent participating in community activities | per survey | Social surveys | REPORTED |
| Internet shutdowns | count of incidents | annual | Shutdown trackers | REPORTED |
| Digital public service use by group | percent of population | per survey | Household surveys | REPORTED |
Model technology and social cohesion as a system. Run the cascade.
Assembled from the Nexus Standards area profile Societal and information risk standards: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Civil unrest
- Supply-chain disruption (set off downstream)
- Currency crisis (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-H117Hazard family: Social cohesion and atrocity risk (NHS-1 soc.cohesion)NRS-X7Information integrity and public trustNRS-D2Society, inclusion and human development riskNRS-H52Hazard family: Information-integrity attacks (NHS-1 cyber.integrity)NRS-H79Hazard family: Mental health crises (NHS-1 health.mental)NRS-H59Hazard family: Economic shocks and governance failure (NHS-1 soc.economic)
Observation
- WorldPop gridded population (listed for reference)
- World Bank Open Data and World Development Indicators API (listed for reference)
- Global SDG Indicators Database (listed for reference)
- Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population
- Proportion of people living below 50 per cent of median income
- Number of countries that adopt and implement guarantees for public access to information
Example geography
- The Horn of Africa: 253 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
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 |
|---|---|---|
| World Values Survey | Trust, values and participation across countries | Free for non-commercial use, terms to check |
| OECD Survey on Drivers of Trust in Public Institutions | Trust in government and its drivers | Public, OECD terms |
| Access Now KeepItOn shutdown data | Internet shutdown incidents | Public, terms to check |
| Afrobarometer, Latinobarómetro and other regional barometers | Public opinion on trust and governance | Free for non-commercial use, terms per survey |
| Pew Research Center global surveys | Attitudes toward technology and institutions | Public, terms per release |
How technology and social cohesion is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in technology and social cohesion: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
Who decides, and what the evidence serves.
Society and Human Development 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 |
|---|---|---|
| Governments | How to design digital services and policies that build trust | Inclusion reviews; trust evidence |
| Civil society organisations | Where to focus on participation and inclusion | Participation and cohesion evidence |
| Regulators | How to address online harms without restricting rights | Studies of technology effects |
Outputs this topic produces
- Trust and cohesion profiles
- Studies of technology effects on participation
- Inclusion reviews of digital public services
- Evidence notes for social cohesion strategies
The questions experts ask.
- How does trust in institutions affect compliance with public health or evacuation advice?
- How does misinformation spread, and what reduces its effects?
- Who is excluded by digital-only services, and with what consequences?
- How do online networks affect polarisation and social cohesion?
- How does automated decision-making affect fairness in public services?
Trust, information and exclusion.
Trust shapes behaviour. During epidemics and disasters, people are more likely to follow guidance from sources they trust, and trust is built or lost by how institutions act over time. Misinformation spreads through social networks, often faster than corrections, especially when it is emotionally charged or aligned with existing beliefs. Prebunking, accurate and timely information from trusted messengers and community engagement are among the approaches with supporting evidence.
Digital exclusion follows lines of age, income, disability, gender and geography. When services move online, people without connectivity, devices, skills or digital identity can lose access to benefits and information. Algorithmic systems used in public services can reproduce bias in their training data. Cohesion is also affected by perceived fairness: crises that appear to burden some groups unfairly erode solidarity.
Design choices matter. Services that keep offline and assisted channels, explain automated decisions and allow appeal tend to preserve trust and inclusion, while opaque systems that cannot be challenged erode both, even when they are efficient on average.
Methods and limits.
Trust and attitudes
Surveys track trust, beliefs and behaviour; responses can differ from actions.
Information diffusion
Analysis of how content spreads on platforms; access to platform data is restricted.
Interventions
Field and online experiments test messaging and correction; effects can fade.
Limits: platform data access is uneven and changing, causal effects of exposure to misinformation are hard to isolate, and findings from one country or platform may not transfer.
How it is studied, and what the methods cannot say.
- 01Social and behavioural scienceSurvey design for hard-to-reach groups · Qualitative and participatory researchApplied here
- 02Causal inference and impact evaluationEvaluating cash transfers · Effects of disasters on households · Education programme evaluationApplied here
- 03EconometricsPoverty dynamics with panel data · Price shock incidence · Inequality decompositionApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
What is social cohesion?
Does social media cause polarisation?
Why does trust matter in a crisis?
Does the platform fact-check content?
Is technology good or bad for cohesion?
Does the platform study specific platforms?
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