气候变化领域集成服务门户(CCPortal): Predicting political attitudes from web tracking data: a machine learning approach

CCPortal

DOI	10.1080/19331681.2024.2316679
	Predicting political attitudes from web tracking data: a machine learning approach
	Kirkizh, Nora; Ulloa, Roberto; Stier, Sebastian; Pfeffer, Juergen
发表日期	2024
ISSN	1933-1681
EISSN	1933-169X
英文摘要	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.
英文关键词	Political attitudes; web tracking data; machine learning; surveys; life-style, immigration, climate change, democracy, European union
语种	英语
WOS研究方向	Communication ; Government & Law
WOS类目	Communication ; Political Science
WOS记录号	WOS:001169749800001
来源期刊	JOURNAL OF INFORMATION TECHNOLOGY & POLITICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/294870
作者单位	Technical University of Munich; Leibniz Institut fur Sozialwissenschaften (GESIS); Leibniz Institut fur Sozialwissenschaften (GESIS); University of Mannheim; Technical University of Munich; Technical University of Munich
推荐引用方式 GB/T 7714	Kirkizh, Nora,Ulloa, Roberto,Stier, Sebastian,et al. Predicting political attitudes from web tracking data: a machine learning approach[J],2024.
APA	Kirkizh, Nora,Ulloa, Roberto,Stier, Sebastian,&Pfeffer, Juergen.(2024).Predicting political attitudes from web tracking data: a machine learning approach.JOURNAL OF INFORMATION TECHNOLOGY & POLITICS.
MLA	Kirkizh, Nora,et al."Predicting political attitudes from web tracking data: a machine learning approach".JOURNAL OF INFORMATION TECHNOLOGY & POLITICS (2024).