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

Author:

Soehl Thomas1ORCID,Chen Zhenxiang2ORCID,Erlich Aaron3ORCID

Affiliation:

1. Department of Sociology, McGill University, Montreal, QC, Canada

2. Department of Sociology, Saint Mary's University, Halifax, NS, Canada

3. Department of Political Science, McGill University, Montreal, QC, Canada

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.

Funder

Social Sciences and Humanities Research Council of Canada

Canada Research Chairs

Publisher

SAGE Publications

Reference51 articles.

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4. Beauchemin Cris, Ametepe Fofo, Bringe Arnaud, Caporali Arianna, Lejbowicz Tania, Morisset Amandine, Thevenin Marc, Schoumaker Bruno. 2014. Introduction to the MAFE Datasets.

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