Analysis of Web Browsing Data: A Guide

被引：1

作者：

von Hohenberg, Bernhard Clemm ^{[1
,10
]}

Stier, Sebastian ^{[2
,8
]}

Cardenal, Ana S. ^{[3
]}

Guess, Andrew M. ^{[4
,5
]}

Menchen-Trevino, Ericka ^{[6
]}

Wojcieszak, Magdalena ^{[7
,9
]}

机构：

[1] GESIS Leibniz Inst Social Sci, Cologne, Germany

[2] GESIS Leibniz Inst Social Sci, Computat Social Sci Dept, Cologne, Germany

[3] Univ Oberta Catalunya, Barcelona, Spain

[4] Princeton Univ, Polit & Publ Affairs, Princeton, NJ USA

[5] Princeton Univ, Ctr Informat Technol Policy, Princeton, NJ USA

[6] Amer Univ, Washington, DC USA

[7] Univ Calif Davis, Davis, CA USA

[8] Univ Mannheim, Sch Social Sci, Mannheim, Germany

[9] Univ Amsterdam, Amsterdam Sch Commun Res, Amsterdam, Netherlands

[10] GESIS Leibniz Inst SocialSciences, Dept Computat Social Sci, D-50667 Cologne, Germany

来源：

SOCIAL SCIENCE COMPUTER REVIEW | 2024年

基金：

欧洲研究理事会;

关键词：

web browsing data; digital trace data; web tracking data; computational social science; ONLINE; NEWS;

D O I：

10.1177/08944393241227868

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页数：26

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