Significance: We present the Facebook Gender Divide, an inexpensive, real-time instrument for measuring gender differences in Facebook access and activity in 217 countries. The Facebook Gender Divide captures standard indicators of Internet penetration and gender equality indices in education, health, and economic opportunity. We find that the tendency of countries to approach economic gender equality is negatively associated with a high Facebook Gender Divide. Our results suggest that online social networks, while suffering gender imbalance, may lower information access barriers for women and narrow the economic gender gap.

The quest for every time more personalized Internet experience relies on the enriched contextual information about each user. Online advertising also follows this approach. Among the context information that advertising stakeholders leverage, location information is certainly one of them. However, when this information is not directly available from the end users, advertising stakeholders infer it using geolocation databases, matching IP addresses to a position on earth. The accuracy of this approach has often been questioned in the past: however, the reality check on an advertising stakeholder shows that this technique accounts for a large fraction of the served advertisements. In this paper, we revisit the work in the field, that is mostly from almost one decade ago, through the lenses of big data. More specifically, we, i) benchmark two commercial Internet geolocation databases, evaluate the quality of their information using a ground-truth database of user positions containing over 2 billion samples, ii) analyze the internals of these databases, devising a theoretical upper bound for the quality of the Internet geolocation approach, and iii) we run an empirical study that unveils the monetary impact of this technology by considering the costs associated with a real-world ad impressions dataset.

Time series data, spanning applications ranging from climatology to finance to healthcare, presents significant challenges in data mining due to its size and complexity. One open issue lies in time series clustering, which is crucial for processing large volumes of unlabeled time series data and unlocking valuable insights. Traditional and modern analysis methods, however, often struggle with these complexities. To address these limitations, we introduce R-Clustering, a novel method that utilizes convolutional architectures with randomly selected parameters. Through extensive evaluations, R-Clustering demonstrates superior performance over existing methods in terms of clustering accuracy, computational efficiency and scalability. Empirical results obtained using the UCR archive demonstrate the effectiveness of our approach across diverse time series datasets. The findings highlight the significance of R-Clustering in various domains and applications, contributing to the advancement of time series data mining.

Digital marketing is a profitable business generating annual revenue over USD 200B and an inter-annual growth over 20%. The definition of efficient marketing investment strategies across different types of channels and campaigns is a key task in digital marketing. Attribution models are an instrument used to assess the return of investment of different channels and campaigns so that they can assist in the decision-making process. A new generation of more powerful data-driven attribution models has irrupted in the market in the last years. Unfortunately, its adoption is slower than expected. One of the main reasons is that the industry lacks a proper understanding of these models and how to configure them. To solve this issue, in this paper, we present an empirical study to better understand the key properties of user-paths and their impact on attribution models. Our analysis is based on a large-scale dataset including more than 95M user-paths from real advertising campaigns of an international hoteling group. The main contribution of the paper is a set of recommendation to build accurate, interpretable and computationally efficient attribution models such as: (i) the use of linear regression, an interpretable machine learning algorithm, to build accurate attribution models; (ii) user-paths including around 12 events are enough to produce accurate models; (iii) the recency of events considered in the user-paths is important for the accuracy of the model.

Through social media, politicians can personalize their campaigns and target specific groups of voters with an unprecedented precision. We assess the effects of such political micro-targeting by exploiting daily advertising prices on Facebook during the 2016 US presidential campaign. We measure the intensity of online campaigns using variation in ad prices charged to reach certain audiences, defined by political orientation, location, and demographic characteristics. We address two fundamental questions: How intensively did social media political campaigns target each audience? How large were any effects on voters? We find that micro-targeted political ads on social media had significant effects when based on geographical location, ideology, ethnicity, and gender. Exposure to these ads made individuals less likely to change their initial voting intentions, particularly among those who had expressed an intention to vote for Donald Trump. We also find that micro-targeted ads reduced turnout among targeted liberals, whereas they increased turnout and support for Trump among targeted moderates.