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.
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.
@cite_0 @cite_9 @cite_1 @cite_4
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Social media platforms have become a major gateway to receive and analyze public opinions. Understanding users can provide invaluable context information of their social media posts and significantly improve traditional opinion analysis models. Demographic attributes, such as ethnicity, gender, age, among others, have been extensively applied to characterize social media users. While studies have shown that user groups formed by demographic attributes can have coherent opinions towards political issues, these attributes are often not explicitly coded by users through their profiles. Previous work has demonstrated the effectiveness of different user signals such as users’ posts and names in determining demographic attributes. Yet, these efforts mostly evaluate linguistic signals from users’ posts and train models from artificially balanced datasets. In this paper, we propose a comprehensive list of user signals: self-descriptions and posts aggregated from users’ friends and followers, users’ profile images, and users’ names. We provide a comparative study of these signals side-by-side in the tasks on inferring three major demographic attributes, namely ethnicity, gender, and age. We utilize a realistic unbalanced datasets that share similar demographic makeups in Twitter for training models and evaluation experiments. Our experiments indicate that self-descriptions provide the strongest signal for ethnicity and age inference and clearly improve the overall performance when combined with tweets. Profile images for gender inference have the highest precision score with overall score close to the best result in our setting. This suggests that signals in self-descriptions and profile images have potentials to facilitate demographic attribute inferences in Twitter, and are promising for future investigation. Users of social media sites like Facebook and Twitter rely on crowdsourced content recommendation systems (e.g., Trending Topics) to retrieve important and useful information. Contents selected for recommendation indirectly give the initial users who promoted (by liking or posting) the content an opportunity to propagate their messages to a wider audience. Hence, it is important to understand the demographics of people who make a content worthy of recommendation, and explore whether they are representative of the media site's overall population. In this work, using extensive data collected from Twitter, we make the first attempt to quantify and explore the demographic biases in the crowdsourced recommendations. Our analysis, focusing on the selection of trending topics, finds that a large fraction of trends are promoted by crowds whose demographics are significantly different from the overall Twitter population. More worryingly, we find that certain demographic groups are systematically under-represented among the promoters of the trending topics. To make the demographic biases in Twitter trends more transparent, we developed and deployed a Web-based service 'Who-Makes-Trends' at twitter-app.mpi-sws.org who-makes-trends. Demographics are widely used in marketing to characterize different types of customers. However, in practice, demographic information such as age, gender, and location is usually unavailable due to privacy and other reasons. In this paper, we aim to harness the power of big data to automatically infer users' demographics based on their daily mobile communication patterns. Our study is based on a real-world large mobile network of more than 7,000,000 users and over 1,000,000,000 communication records (CALL and SMS). We discover several interesting social strategies that mobile users frequently use to maintain their social connections. First, young people are very active in broadening their social circles, while seniors tend to keep close but more stable connections. Second, female users put more attention on cross-generation interactions than male users, though interactions between male and female users are frequent. Third, a persistent same-gender triadic pattern over one's lifetime is discovered for the first time, while more complex opposite-gender triadic patterns are only exhibited among young people. We further study to what extent users' demographics can be inferred from their mobile communications. As a special case, we formalize a problem of double dependent-variable prediction-inferring user gender and age simultaneously. We propose the WhoAmI method, a Double Dependent-Variable Factor Graph Model, to address this problem by considering not only the effects of features on gender age, but also the interrelation between gender and age. Our experiments show that the proposed WhoAmI method significantly improves the prediction accuracy by up to 10 compared with several alternative methods. User profiling is crucial to many online services. Several recent studies suggest that demographic attributes are predictable from different online behavioral data, such as users' "Likes" on Facebook, friendship relations, and the linguistic characteristics of tweets. But location check-ins, as a bridge of users' offline and online lives, have by and large been overlooked in inferring user profiles. In this paper, we investigate the predictive power of location check-ins for inferring users' demographics and propose a simple yet general location to profile (L2P) framework. More specifically, we extract rich semantics of users' check-ins in terms of spatiality, temporality, and location knowledge, where the location knowledge is enriched with semantics mined from heterogeneous domains including both online customer review sites and social networks. Additionally, tensor factorization is employed to draw out low dimensional representations of users' intrinsic check-in preferences considering the above factors. Meanwhile, the extracted features are used to train predictive models for inferring various demographic attributes. We collect a large dataset consisting of profiles of 159,530 verified users from an online social network. Extensive experimental results based upon this dataset validate that: 1) Location check-ins are diagnostic representations of a variety of demographic attributes, such as gender, age, education background, and marital status; 2) The proposed framework substantially outperforms compared models for profile inference in terms of various evaluation metrics, such as precision, recall, F-measure, and AUC. 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. We demonstrate an approach to face attribute detection that retains or improves attribute detection accuracy across gender and race subgroups by learning demographic information prior to learning the attribute detection task. The system, which we call InclusiveFaceNet, detects face attributes by transferring race and gender representations learned from a held-out dataset of public race and gender identities. Leveraging learned demographic representations while withholding demographic inference from the downstream face attribute detection task preserves potential users' demographic privacy while resulting in some of the best reported numbers to date on attribute detection in the Faces of the World and CelebA datasets. Automatically inferring user demographics from social media posts is useful for both social science research and a range of downstream applications in marketing and politics. We present the first extensive study where user behaviour on Twitter is used to build a predictive model of income. We apply non-linear methods for regression, i.e. Gaussian Processes, achieving strong correlation between predicted and actual user income. This allows us to shed light on the factors that characterise income on Twitter and analyse their interplay with user emotions and sentiment, perceived psycho-demographics and language use expressed through the topics of their posts. Our analysis uncovers correlations between different feature categories and income, some of which reflect common belief e.g. higher perceived education and intelligence indicates higher earnings, known differences e.g. gender and age differences, however, others show novel findings e.g. higher income users express more fear and anger, whereas lower income users express more of the time emotion and opinions. Demographic information plays an important role in personalized web applications. However, it is usually not easy to obtain this kind of personal data such as age and gender. In this paper, we made a first approach to predict users' gender and age from their Web browsing behaviors, in which the Webpage view information is treated as a hidden variable to propagate demographic information between different users. There are three main steps in our approach: First, learning from the Webpage click-though data, Webpages are associated with users' (known) age and gender tendency through a discriminative model; Second, users' (unknown) age and gender are predicted from the demographic information of the associated Webpages through a Bayesian framework; Third, based on the fact that Webpages visited by similar users may be associated with similar demographic tendency, and users with similar demographic information would visit similar Webpages, a smoothing component is employed to overcome the data sparseness of web click-though log. Experiments are conducted on a real web click-through log to demonstrate the effectiveness of the proposed approach. The experimental results show that the proposed algorithm can achieve up to 30.4 improvements on gender prediction and 50.3 on age prediction in terms of macro F1, compared to baseline algorithms. User demographics, such as age, gender and ethnicity, are routinely used for targeting content and advertising products to users. Similarly, recommender systems utilize user demographics for personalizing recommendations and overcoming the cold-start problem. Often, privacy-concerned users do not provide these details in their online profiles. In this work, we show that a recommender system can infer the gender of a user with high accuracy, based solely on the ratings provided by users (without additional metadata), and a relatively small number of users who share their demographics. Focusing on gender, we design techniques for effectively adding ratings to a user's profile for obfuscating the user's gender, while having an insignificant effect on the recommendations provided to that user. Understanding the demographics of users of online social networks has important applications for health, marketing, and public messaging. Whereas most prior approaches rely on a supervised learning approach, in which individual users are labeled with demographics for training, we instead create a distantly labeled dataset by collecting audience measurement data for 1,500 websites (e.g., 50 of visitors to gizmodo.com are estimated to have a bachelor's degree). We then fit a regression model to predict these demographics from information about the followers of each website on Twitter. Using patterns derived both from textual content and the social network of each user, our final model produces an average held-out correlation of .77 across seven different variables (age, gender, education, ethnicity, income, parental status, and political preference). We then apply this model to classify individual Twitter users by ethnicity, gender, and political preference, finding performance that is surprisingly competitive with a fully supervised approach. International migration is one of the major determinants of demographic change. Although efforts to produce comparable statistics are underway, estimates of demographic flows are inexistent, outdated, or largely inconsistent, for most countries. We estimate age and gender-specific migration rates using data extracted from a large sample of Yahoo! e-mail messages. Self-reported age and gender of anonymized e-mail users were linked to the geographic locations (mapped from IP addresses) from where users sent e-mail messages over time (2009-2011). The users' country of residence over time was inferred as the one from where most e-mail messages were sent. Our estimates of age profiles of migration are qualitatively consistent with existing administrative data sources. Selection bias generates uncertainty for estimates at one point in time, especially for developing countries. However, our approach allows us to compare in a reliable way migration trends of females and males. We document the recent increase in human mobility and we observe that female mobility has been increasing at a faster pace. Our findings suggest that e-mail data may complement existing migration data, resolve inconsistencies arising from different definitions of migration, and provide new and rich information on mobility patterns and social networks of migrants. The use of digital records for demographic research has the potential to become particularly important for developing countries, where the diffusion of Internet will be faster than the development of mature demographic registration systems. 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). Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper we focus on the population of Mexican mobile phone users. Our first contribution is an observational study of mobile phone usage according to gender and age groups. We were able to detect significant differences in phone usage among different subgroups of the population. Our second contribution is to provide a novel methodology to predict demographic features (namely age and gender) of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph. We provide details of the methodology and show experimental results on a real world dataset that involves millions of users. Twitter provides an extremely rich and open source of data for studying human behaviour at scale. It has been used to advance our understanding of social network structure, the viral flow of information and how new ideas develop. Enriching Twitter with demographic information would permit more precise science and better generalisation to the real world. The only demographic indicators associated with a Twitter account are the free text name, location and description fields. We show how the age of most Twitter accounts can be inferred with high accuracy using the structure of the social graph. Besides classical social science applications, there are obvious privacy and child protection implications to this discovery. Previous work on Twitter age detection has focussed on either user-name or linguistic features of tweets. A shortcoming of the user-name approach is that it requires real names (Twitter names are often false) and census data from each user's (unknown) birth country. Problems with linguistic approaches are that most Twitter users do not tweet (the median number of Tweets is 4) and a different model must be learnt for each language. To address these issues, we devise a language-independent methodology for determining the age of Twitter users from data that is native to the Twitter ecosystem. Roughly 150,000 Twitter users specify an age in their free text description field. We generalize this to the entire Twitter network by showing that age can be predicted based on what or whom they follow. We adopt a Bayesian classification paradigm, which offers a consistent framework for handling uncertainty in our data, e.g., inaccurate age descriptions or spurious edges in the graph. Working within this paradigm we have successfully applied age detection to 700 million Twitter accounts with an F1 Score of 0.86. In this paper, we study follower demographics of Donald Trump and Hillary Clinton, the two leading candidates in the 2016 U.S. presidential race. We build a unique dataset US2016, which includes the number of followers for each candidate from September 17, 2015 to December 22, 2015. US2016 also includes the geographical location of these followers, the number of their own followers and, very importantly, the profile image of each follower. We use individuals’ number of followers and profile images to analyze four dimensions of follower demographics: social status, gender, race and age. Our study shows that in terms of social influence, the Trumpists are more polarized than the Clintonists: they tend to have either a lot of influence or little influence. We also find that compared with the Clintonists, the Trumpists are more likely to be either very young or very old. Our study finds no gender affinity effect for Clinton in the Twitter sphere, but we do find that the Clintonists are more racially diverse. Demographic estimation entails automatic estimation of age, gender and race of a person from his face image, which has many potential applications ranging from forensics to social media. Automatic demographic estimation, particularly age estimation, remains a challenging problem because persons belonging to the same demographic group can be vastly different in their facial appearances due to intrinsic and extrinsic factors. In this paper, we present a generic framework for automatic demographic (age, gender and race) estimation. Given a face image, we first extract demographic informative features via a boosting algorithm, and then employ a hierarchical approach consisting of between-group classification, and within-group regression. Quality assessment is also developed to identify low-quality face images that are difficult to obtain reliable demographic estimates. Experimental results on a diverse set of face image databases, FG-NET ( @math images), FERET ( @math images), MORPH II ( @math images), PCSO ( @math images), and a subset of LFW ( @math images), show that the proposed approach has superior performance compared to the state of the art. Finally, we use crowdsourcing to study the human perception ability of estimating demographics from face images. A side-by-side comparison of the demographic estimates from crowdsourced data and the proposed algorithm provides a number of insights into this challenging problem. The problems of obesity and overweight are commonly cited as the motivation behind recent efforts to develop technology that promotes physical activity. Prompted by the social nature of many of the emerging applications, this paper presents our investigation of the sociality of weight management as experienced by a broad demographic of individuals. Our findings highlight the broad scope of peer involvement, and provide insight into the context and mechanics of related interaction that may prove valuable in informing the next generation of peer-based weight management technology for use in everyday life. 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. The widespread use of online social networks (OSNs) to disseminate information and exchange opinions, by the general public, news media, and political actors alike, has enabled new avenues of research in computational political science. In this paper, we study the problem of quantifying and inferring the political leaning of Twitter users. We formulate political leaning inference as a convex optimization problem that incorporates two ideas: (a) users are consistent in their actions of tweeting and retweeting about political issues, and (b) similar users tend to be retweeted by similar audience. We then apply our inference technique to 119 million election-related tweets collected in seven months during the 2012 U.S. presidential election campaign. On a set of frequently retweeted sources, our technique achieves 94 percent accuracy and high rank correlation as compared with manually created labels. By studying the political leaning of 1,000 frequently retweeted sources, 232,000 ordinary users who retweeted them, and the hashtags used by these sources, our quantitative study sheds light on the political demographics of the Twitter population, and the temporal dynamics of political polarization as events unfold. Online video services, messaging systems, games and social media services are tremendously popular among young people and children in many countries. Most of the digital services offered on the internet are advertising funded, which makes advertising ubiquitous in children’s everyday life. To understand the impact of advertising-based digital services on children, we study the collective behavior of users of YouTube for kids channels and present the demographics of a large number of users. We collected data from 12,848 videos from 17 channels in US and UK and 24 channels in Brazil. The channels in English have been viewed more than 37 billion times. We also collected more than 14 million comments made by users. Based on a combination of text-analysis and face recognition tools, we show the presence of racial and gender biases in our large sample of users. We also identify children actively using YouTube, although the minimum age for using the service is 13 years in most countries. We provide comparisons of user behavior among the three countries, which represent large user populations in the global North and the global South.