  This article evaluates the quality of data collection in individual-level
desktop information tracking used in the social sciences and shows that the
existing approaches face sampling issues, validity issues due to the lack of
content-level data and their disregard of the variety of devices and long-tail
consumption patterns as well as transparency and privacy issues. To overcome
some of these problems, the article introduces a new academic tracking
solution, WebTrack, an open source tracking tool maintained by a major European
research institution. The design logic, the interfaces and the backend
requirements for WebTrack, followed by a detailed examination of strengths and
weaknesses of the tool, are discussed. Finally, using data from 1185
participants, the article empirically illustrates how an improvement in the
data collection through WebTrack leads to new innovative shifts in the
processing of tracking data. As WebTrack allows collecting the content people
are exposed to on more than classical news platforms, we can strongly improve
the detection of politics-related information consumption in tracking data with
the application of automated content analysis compared to traditional
approaches that rely on the list-based identification of news.
