TY - JOUR
T1 - Analysis of Web Browsing Data
T2 - A Guide
AU - Clemm von Hohenberg, Bernhard
AU - Stier, Sebastian
AU - Cardenal, Ana S.
AU - Guess, Andrew M.
AU - Menchen-Trevino, Ericka
AU - Wojcieszak, Magdalena
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - computational social science
KW - digital trace data
KW - web browsing data
KW - web tracking data
UR - http://www.scopus.com/inward/record.url?scp=85184883182&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85184883182&partnerID=8YFLogxK
U2 - 10.1177/08944393241227868
DO - 10.1177/08944393241227868
M3 - Article
AN - SCOPUS:85184883182
SN - 0894-4393
JO - Social Science Computer Review
JF - Social Science Computer Review
ER -