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Facebook Digital Traces for Survey Research: Assessing the Efficiency and Effectiveness of a Facebook Ad–Based Procedure for Recruiting Online Survey Respondents in Niche and Difficult-to-Reach Populations

Academic Article
Publication Date:
2020
Short description:
Facebook Digital Traces for Survey Research: Assessing the Efficiency and Effectiveness of a Facebook Ad–Based Procedure for Recruiting Online Survey Respondents in Niche and Difficult-to-Reach Populations / Iannelli, L., Giglietto, F., Rossi, L., Zurovac, E.. - In: SOCIAL SCIENCE COMPUTER REVIEW. - ISSN 0894-4393. - 38:4(2020), pp. 462-476. [10.1177/0894439318816638]
abstract:
Survey-based studies are increasingly experimenting with strategies that employ digital footprints left by users on social media as entry points for recruiting participants and complementary data sources. In this perspective, the Facebook advertising platform provides unique opportunities and challenges through its marketing tools that target advertisements based on users’ demographics, behaviors, and interests. This article presents a procedure that employed the most recent devel- opments in Facebook marketing tools to evaluate the efficiency and effectiveness of an innovative method for recruiting niche and traditionally hard-to-reach respondents. Although the multiple innovations introduced in the method hinder a proper comparison with previous studies, the survey provides evidence concerning the efficacy of the procedure and offers scholars a set of imple- mentations to design future comparable Facebook ad–based surveys. Challenges, opportunities, and results for effectiveness are discussed in light of a previous survey on Italian adults carried out with a panel-based computer-assisted web interviewing method.
Iris type:
1.1 Articolo in rivista
Keywords:
web survey, Facebook, digital footprints, conspiracy theories
List of contributors:
Iannelli, Laura; Giglietto, Fabio; Rossi, Luca; Zurovac, Elisabetta
Authors of the University:
IANNELLI Laura
Handle:
https://iris.uniss.it/handle/11388/218493
Full Text:
https://iris.uniss.it//retrieve/handle/11388/218493/432853/Iannelli_Giglietto_Rossi_Zurovac_2020_Accepted_Manuscript.pdf
Published in:
SOCIAL SCIENCE COMPUTER REVIEW
Journal
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https://journals.sagepub.com/doi/10.1177/0894439318816638
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