Predicting political attitudes from web tracking data: a machine learning approach

Abstract

Anecdotal evidence suggests that the surge of populism and subsequent political polarization might make voters' political preferences more detectable from digital trace data. This potential scenario could expose voters to the risk of being targeted and easily influenced by political actors. This study investigates the linkage between over 19,000,000 website visits, tracked from 1,003 users in Germany, and their survey responses to explore whether website choices can accurately predict political attitudes across five dimensions: Immigration, democracy, issues (such as climate and the European Union), populism, and trust. Our findings indicate a limited ability to identify political attitudes from individuals' website visits. Our most effective machine learning algorithm predicted interest in politics and attitudes toward democracy but with dependency on model parameters. Although website categories exhibited suggestive patterns, they only marginally distinguished between individuals with anti- or pro-immigration attitudes, as well as those with populist or mainstream attitudes. This further confirm the reliability of surveys in measuring attitudes compared to digital trace data and, from a normative perspective, suggests that the potential to extract sensitive political information from online behavioral data, which could be utilized for microtargeting, remains limited.