Applying Google trends to analyze electoral Outcomes: A 2024 cross-national perspective - Dmitry Erokhin (Social Sciences and Humanities Open, 2025)
This study analyzes whether Google Trends data, when applied in a cross-country context, offers a consistent and meaningful indicator of electoral outcomes across different national elections. To do this, it examines how Google Trends data in national, single-round elections held in 2024 correspond to the relationship between search volumes for candidates or political parties in the week preceding elections and key electoral metrics such as vote share, winning status, and candidate ranking. The analysis demonstrates that online search behavior serves as a valuable proxy for gauging public interest and helps illustrate patterns of voter engagement. By employing adjusted Google Trends scores, which calculate each candidate's or party's proportion of the total search interest for all major contenders on a given day (so that the combined search shares for all included candidates or parties sum to 100 % of the total search volume for that day, hereafter “proportional representation”), these metrics reduce data noise and outliers. The study also demonstrates that these refined metrics exhibit stronger associations with electoral outcomes compared to the unadjusted search data. The main contribution of this study lies in its cross-country approach, offering a comparative perspective on how search interest may relate to voting behavior across diverse contexts. Moreover, the study discusses inherent limitations, including the inability of Google Trends to differentiate between positive and negative search intent and its sensitivity to demographic and regional variations in search behavior. By conducting a comprehensive cross-country analysis of multiple elections, this research contributes to the expanding literature on the application of digital data analytics in social and political research and underscores the descriptive utility of search data across different electoral contexts.