Evidence map›Paper›PMID 41550882›Full record

ArticleSociological methods & research2026

Promises and Limits of Using Targeted Social Media Advertising to Sample Global Migrant Populations: Nigerians at Home and Abroad.

Thomas Soehl, Zhenxiang Chen, Aaron Erlich

Abstract read
In one paragraph

Article in Sociological methods & research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Thomas SoehlDepartment of Sociology, McGill University, Montreal, QC, Canada.ORCID https://orcid.org/0000-0002-6593-3376
Zhenxiang ChenDepartment of Sociology, Saint Mary's University, Halifax, NS, Canada.ORCID https://orcid.org/0000-0001-9648-3542
Aaron ErlichDepartment of Political Science, McGill University, Montreal, QC, Canada.ORCID https://orcid.org/0000-0001-6571-9081

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Survey research on migrants is notoriously challenging, especially if the goal is to collect data across a range of countries. Social networking sites' ability to micro-target advertisements to migrant communities combined with their near-global reach makes them an attractive option. Yet there is little rigorous evaluation of the quality of data thus collected-especially for populations from developing countries. We compare samples of Nigerian emigrants in Canada and Italy and Nigerians (at home) in Nigeria recruited through targeted advertising on Facebook and Instagram to population estimates. We find our samples contain varying degrees of bias in the case of age and gender and systematically miss those with little formal education. How much this affects our samples' representativeness varies across contexts: discrepancies are much smaller for emigrant populations in Canada than in Italy and much larger in Nigeria, where a large share of the population has little formal education and limited literacy. Post-stratifying each sample on age, gender, and education does not ameliorate bias on other variables such as ethnicity, religion, period of migration, or political attitudes. We discuss the potential and limitations of social-media-driven sampling and highlight key considerations for implementing it to collect multi-sited data on migrants.

Indexed as

diasporasFacebookmigrantsNigeriasurveys

Identifiers

PMID41550882
PMCPMC12812017

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.