Analysis of Web Browsing Data: A Guide

Bernhard Clemm von Hohenberg, Sebastian Stier, Ana S. Cardenal, Andrew M. Guess, Ericka Menchen-Trevino, Magdalena Wojcieszak

Research output: Contribution to journalArticlepeer-review


The use of individual-level browsing data, that is, the records of a person’s visits to online content through a desktop or mobile browser, is of increasing importance for social scientists. Browsing data have characteristics that raise many questions for statistical analysis, yet to date, little hands-on guidance on how to handle them exists. Reviewing extant research, and exploring data sets collected by our four research teams spanning seven countries and several years, with over 14,000 participants and 360 million web visits, we derive recommendations along four steps: preprocessing the raw data; filtering out observations; classifying web visits; and modelling browsing behavior. The recommendations we formulate aim to foster best practices in the field, which so far has paid little attention to justifying the many decisions researchers need to take when analyzing web browsing data.

Original languageEnglish (US)
JournalSocial Science Computer Review
StateAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • General Social Sciences
  • Computer Science Applications
  • Library and Information Sciences
  • Law


  • computational social science
  • digital trace data
  • web browsing data
  • web tracking data


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