Beyond Surveys: Leveraging Real-World Events to Validate Behavioral Measures of News Exposure

Abstract Do people learn about the political world through online media? We address this question by exploiting an unexpected global news event—the Russian invasion of Ukraine—and by developing exogenous measures of media exposure based on three months of web-tracking data from five advanced democracies. Our analysis differentiates between visits to general news domains and visits to politically relevant and Ukraine specific articles, with the latter identified using machine-learning techniques. We validate these exposure measures through multiple approaches, including their capacity to predict knowledge about the Russian invasion. Our findings underscore the importance of granularity. Visits to—and time spent on—Ukraine-related articles emerge as the only significant predictor of surveillance knowledge, whereas broader indicators, such as domain-level visits, show no significant effects once self-reported exposure and other key covariates are taken into account. We conclude by discussing the substantive and methodological implications of these findings.

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Publication Details

Journal
Public Opinion Quarterly
Published
2025-11-19
DOI
https://doi.org/10.1093/poq/nfaf068
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
0.00
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Beyond Surveys: Leveraging Real-World Events to Validate Behavioral Measures of News Exposure

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Beyond Surveys: Leveraging Real-World Events to Validate Behavioral Measures of News Exposure

Sílvia Majó-Vázquez, Ludovic Terren, Ana Sofía Cardenal, David Nicolas Hopmann, Alon Zoizner, Peter Van Aelst
article en

Abstract

Abstract Do people learn about the political world through online media? We address this question by exploiting an unexpected global news event—the Russian invasion of Ukraine—and by developing exogenous measures of media exposure based on three months of web-tracking data from five advanced democracies. Our analysis differentiates between visits to general news domains and visits to politically relevant and Ukraine specific articles, with the latter identified using machine-learning techniques. We validate these exposure measures through multiple approaches, including their capacity to predict knowledge about the Russian invasion. Our findings underscore the importance of granularity. Visits to—and time spent on—Ukraine-related articles emerge as the only significant predictor of surveillance knowledge, whereas broader indicators, such as domain-level visits, show no significant effects once self-reported exposure and other key covariates are taken into account. We conclude by discussing the substantive and methodological implications of these findings.

Public Opinion Quarterly
Universitat Oberta de Catalunya (ES), University of Antwerp (BE), University of Southern Denmark (DK), Vrije Universiteit Amsterdam (NL), University of Haifa (IL)
Openalex Percentile: Top 11%
Misinformation and Its Impacts
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