Nowcasting Daily Population Displacement in Ukraine through Social Media Advertising Data
Bibliographic Data
| ID | 4120913 |
|---|---|
| Authors | Douglas Ryan Leasure (0000-0002-8768-2811, corresponding author), Ridhi Kashyap (0000-0003-0615-2868), Francesco Rampazzo (0000-0002-5071-7048), Claire A Dooley (0000-0003-2415-9895), Benjamin Elbers (0000-0001-5392-3448), Maria Bondarenko (0000-0003-4958-6551), Megan Verhagen (0000-0003-2746-0309), A Frey (0000-0002-5044-1432), Jiani Yan (0000-0002-9379-1547), Evelina T Akimova (0000-0001-8733-3745), Masoomali Fatehkia (0000-0002-2387-9084), Robert Trigwell (0000-0002-6782-4405), Andrew J Tatem (0000-0002-7270-941X), Ingmar Weber (0000-0003-4169-2579), Melinda C Mills (0000-0003-1704-0001) |
| Year | 2023 |
| Volume | 49 |
| Issue | 2 |
| Pages | 231-254 |
| Publication date | 2023-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Population and Development Review (JOURNAL) |
| Journal identifiers | ISSN: 0098-7921 • E-ISSN: 1728-4457 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/padr.12558 |
| OpenAlex | W4362677642 |
| Language | EN |
| Citations received | 27 |
| References cited | 29 |
In times of crisis, real-time data mapping population displacements are invaluable for targeted humanitarian response. The Russian invasion of Ukraine on February 24, 2022, forcibly displaced millions of people from their homes including nearly 6 million refugees flowing across the border in just a few weeks, but information was scarce regarding displaced and vulnerable populations who remained inside Ukraine. We leveraged social media data from Facebook's advertising platform in combination with preconflict population data to build a real-time monitoring system to estimate subnational population sizes every day disaggregated by age and sex. Using this approach, we estimated that 5.3 million people had been internally displaced away from their baseline administrative region in the first three weeks after the start of the conflict. Results revealed four distinct displacement patterns: large-scale evacuations, refugee staging areas, internal areas of refuge, and irregular dynamics. While the use of social media provided one of the only quantitative estimates of internal displacement in the conflict setting in virtual real time, we conclude by acknowledging risks and challenges of these new data streams for the future
Advertising · Business · Cartography · Demographic economics · Displaced person · Displacement (psychology · Economics · Forced migration · Geography · Internally displaced person · Nowcasting · Political science · Population · Refugee · Scale (ratio · Social media · Sociology · COVID-19 Digital Contact Tracing · Data-Driven Disease Surveillance · Demography · Human Mobility and Location-Based Analysis · Psychology
Internal and International Migration of Scholars in Times of Conflict
Comparing and integrating human mobility data sources for measles transmission modeling in Zambia
Analyzing Online Migration Forums
The Societal Consequences of War
Prediction of changes in war-induced population and CO2 emissions in Ukraine using social media
Monitoring changes in nighttime lights and anthropogenic CO2 emissions during geopolitical conflicts from a remote sensing perspective
Estimating internal displacement in Ukraine from high-frequency GPS mobile phone data
Disaggregating census data for population mapping using a Bayesian Additive Regression Tree model
Demographic figures at risk in the digital era
Effects of blast exposure on anxiety and symptoms of post-traumatic stress disorder (PTSD) among displaced Ukrainian populations
Tackling public health data gaps through Bayesian high-resolution population estimation
Leveraging High‐Frequency Digital Data to Analyze Forced Displacement Dynamics
Who leaves and who returns? IDPs and returnees after the Russian invasion of Ukraine
Where have Ukrainian refugees gone? Identifying potential settlement areas across European regions integrating digital and traditional geographic data
Two implications of survey research mode during war
Researching Russia with Digital Trace Data
Using free remotely sensed data to assess war-induced damage to agricultural cultivation
Using satellite imagery and a farmer registry to assess agricultural support in conflict settings
Impact of the Russian invasion on Ukrainian small and medium farmers’ productivity
The Uncertainty of Forced Displacement
Search for a New Home
Assessing Timely Migration Trends Through Digital Traces
Using Organic Data in Migration Research
The Value of Cultural Similarity for Predicting Migration
New Data Sources for Demographic Research
Global Gender Gaps in the International Migration of Professionals on LinkedIn
Measuring short-term mobility patterns in North America using Facebook advertising data, with an application to adjusting Covid-19 mortality rates
Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data
Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data
What we do know (and could know) about estimating population sizes of internally displaced people
A Framework for Estimating Migrant Stocks Using Digital Traces and Survey Data
Promises and Pitfalls of Using Digital Traces for Demographic Research
Has demography witnessed a data revolution? Promises and pitfalls of a changing data ecosystem
Leveraging Facebook's Advertising Platform to Monitor Stocks of Migrants
The Impact of Hurricane Maria on Out-migration from Puerto Rico
Introducing Acled
Are rapid population estimates accurate? A field trial of two different assessment methods
| Unique citing works | 27 |
|---|---|
| Citations per year | 13,5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 24 |