text: Understanding the demographics of app users is crucial, for example, for app developers, who wish to target their advertisements more effectively. Our work addresses this need by studying the predictability of user demographics based on the list of a user's apps which is readily available to many app developers. We extend previous work on the problem on three frontiers: (1) We predict new demographics (age, race, and income) and analyze the most informative apps for four demographic attributes included in our analysis. The most predictable attribute is gender (82.3 accuracy), whereas the hardest to predict is income (60.3 accuracy). (2) We compare several dimensionality reduction methods for high-dimensional app data, finding out that an unsupervised method yields superior results compared to aggregating the apps at the app category level, but the best results are obtained simply by the raw list of apps. (3) We look into the effect of the training set size and the number of apps on the predictability and show that both of these factors have a large impact on the prediction accuracy. The predictability increases, or in other words, a user's privacy decreases, the more apps the user has used, but somewhat surprisingly, after 100 apps, the prediction accuracy starts to decrease.
  @cite_0: Every second, the thoughts and feelings of millions of people across the world are recorded in the form of 140-character tweets using Twitter. However, despite the enormous potential presented by this remarkable data source, we still do not have an understanding of the Twitter population itself: Who are the Twitter users? How representative of the overall population are they? In this paper, we take the first steps towards answering these questions by analyzing data on a set of Twitter users representing over 1 of the U.S. population. We develop techniques that allow us to compare the Twitter population to the U.S. population along three axes (geography, gender, and race ethnicity), and find that the Twitter population is a highly non-uniform sample of the population.
  @cite_9: We analyze a large query log of 2.3 million anonymous registered users from a web-scale U.S. search engine in order to jointly analyze their on-line behavior in terms of who they might be (demographics), what they search for (query topics), and how they search (session analysis). We examine basic demographics from registration information provided by the users, augmented with U.S. census data, analyze basic session statistics, classify queries into types (navigational, informational, transactional) based on click entropy, classify queries into topic categories, and cluster users based on the queries they issued. We then examine the resulting clusters in terms of demographics and search behavior. Our analysis of the data suggests that there are important differences in search behavior across different demographic groups in terms of the topics they search for, and how they search (e.g., white conservatives are those likely to have voted republican, mostly white males, who search for business, home, and gardening related topics; Baby Boomers tend to be primarily interested in Finance and a large fraction of their sessions consist of simple navigational queries related to online banking, etc.). Finally, we examine regional search differences, which seem to correlate with differences in local industries (e.g., gambling related queries are highest in Las Vegas and lowest in Salt Lake City; searches related to actors are about three times higher in L.A. than in any other region).
  @cite_1: 
  @cite_4: 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.
summary: Characterizing the demographics of Twitter users has been studied by @cite_0 who infer geography, gender, and race of the users based on self-reported locations and the names of the users. They find large deviances from the demographic distribution of the overall population. @cite_1 provide a more extensive demographic comparison of five social media platforms based on telephone interviews. @cite_4 look into the demographics and behavior of web users, whereas @cite_9 study the same for search-engine users.