  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.
