Who Does What on the Web: A Large-Scale Study of Browsing Behavior
Keywords:demographics, browsing, user modeling, computational social science
As the Web has become integrated into daily life, understanding how individuals spend their time online impacts domains ranging from public policy to marketing. It is difficult, however, to measure even simple aspects of browsing behavior via conventional methods---including surveys and site-level analytics---due to limitations of scale and scope. In part addressing these limitations, large-scale Web panel data are a relatively novel means for investigating patterns of Internet usage. In one of the largest studies of browsing behavior to date, we pair Web histories for 250,000 anonymized individuals with user-level demographics---including age, sex, race, education, and income---to investigate three topics. First, we examine how behavior changes as individuals spend more time online, showing that the heaviest users devote nearly twice as much of their time to social media relative to typical individuals. Second, we revisit the digital divide, finding that the frequency with which individuals turn to the Web for research, news, and healthcare is strongly related to educational background, but not as closely tied to gender and ethnicity. Finally, we demonstrate that browsing histories are a strong signal for inferring user attributes, including ethnicity and household income, a result that may be leveraged to improve ad targeting.