Reconstructing Detailed Browsing Activities from Browser History
This work provides a method for researchers and developers to infer detailed browsing behavior from readily available browser history, addressing the challenge of collecting granular user activity data without requiring long-term monitoring tools.
This paper reconstructs detailed browsing activities, specifically time spent on sites, using only browser history data. They developed a machine learning algorithm that predicts browser focus and activity with an F1-score of 0.84 and the active domain with 76.2% accuracy. This allows for reconstructing total online time with an R^2 of 0.96 and time per domain with an R^2 of 0.92.
Users' detailed browsing activity - such as what sites they are spending time on and for how long, and what tabs they have open and which one is focused at any given time - is useful for a number of research and practical applications. Gathering such data, however, requires that users install and use a monitoring tool over long periods of time. In contrast, browser extensions can gain instantaneous access months of browser history data. However, the browser history is incomplete: it records only navigation events, missing important information such as time spent or tab focused. In this work, we aim to reconstruct time spent on sites with only users' browsing histories. We gathered three months of browsing history and two weeks of ground-truth detailed browsing activity from 185 participants. We developed a machine learning algorithm that predicts whether the browser window is focused and active at one second-level granularity with an F1-score of 0.84. During periods when the browser is active, the algorithm can predict which the domain the user was looking at with 76.2% accuracy. We can use these results to reconstruct the total time spent online for each user with an R^2 value of 0.96, and the total time each user spent on each domain with an R^2 value of 0.92.