How we browse: Measurement and analysis of digital behavior
Yuliia Lut, Michael Wang, Elissa M. Redmiles, Rachel Cummings

TL;DR
This study combines self-reports and observational data from 31 participants over 14 days to analyze online browsing patterns, revealing demographic differences, perception biases, and security-related behaviors.
Contribution
It provides a comprehensive measurement of browsing behavior using combined data sources, highlighting demographic influences and perception accuracy, with implications for security and user modeling.
Findings
Significant activity differences by age, not race or gender.
Distinct behavior on security concern websites.
Users overestimate online time but perceive their behavior accurately.
Abstract
Accurately analyzing and modeling online browsing behavior play a key role in understanding users and technology interactions. In this work, we design and conduct a user study to collect browsing data from 31 participants continuously for 14 days and self-reported browsing patterns. We combine self-reports and observational data to provide an up-to-date measurement study of online browsing behavior. We use these data to empirically address the following questions: (1) Do structural patterns of browsing differ across demographic groups and types of web use?, (2) Do people have correct perceptions of their behavior online?, and (3) Do people change their browsing behavior if they are aware of being observed? In response to these questions, we find significant differences in level of activity based on user age, but not based on race or gender. We also find that users have significantly…
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Taxonomy
TopicsSocial Media and Politics · Spam and Phishing Detection · Misinformation and Its Impacts
