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Despite significant investments in access network infrastructure, universal access to high-quality Internet connectivity remains a challenge. Policymakers often rely on large-scale, crowdsourced measurement datasets to assess the…
The Internet has become indispensable to daily activities, such as work, education and health care. Many of these activities require Internet access data rates that support real-time video conferencing. However, digital inequality persists…
Modeling and estimation for spatial data are ubiquitous in real life, frequently appearing in weather forecasting, pollution detection, and agriculture. Spatial data analysis often involves processing datasets of enormous scale. In this…
Crowdsourcing has been widely used to efficiently obtain labeled datasets for supervised learning from large numbers of human resources at low cost. However, one of the technical challenges in obtaining high-quality results from…
Online data sources offer tremendous promise to demography and other social sciences, but researchers worry that the group of people who are represented in online datasets can be different from the general population. We show that by…
The use of crowdsourced data has been finding practical use for enhancing situational awareness during disasters. While recent studies have shown promising results regarding the potential of crowdsourced data for flood mapping, little…
Webcam-based eye tracking is a cost-effective, scalable method for remote research that effectively reaches broader populations. However, uncontrolled environments and hardware diversity lead to inconsistent data quality in crowdsourcing.…
As the exploration of digital behavioral data revolutionizes communication research, understanding the nuances of data collection methodologies becomes increasingly pertinent. This study focuses on one prominent data collection approach,…
Algorithms deployed in education can shape the learning experience and success of a student. It is therefore important to understand whether and how such algorithms might create inequalities or amplify existing biases. In this paper, we…
Online crowdsourcing provides a scalable and inexpensive means to collect knowledge (e.g. labels) about various types of data items (e.g. text, audio, video). However, it is also known to result in large variance in the quality of recorded…
Machine learning systems are increasingly deployed in high-stakes domains, yet they remain vulnerable to bias systematic disparities that disproportionately impact specific demographic groups. Traditional bias detection methods often depend…
Understanding how urban density impacts public perceptions of urban service is important for informing livable, accessible, and equitable urban planning. Conventional methods such as surveys are limited by their sampling scope, time…
We investigate the evolution of Internet speed and its implications for access to key digital services, as well as the resilience of the network during crises, focusing on six major Brazilian cities: Belo Horizonte, Bras\'ilia, Fortaleza,…
While extremely useful (e.g., for COVID-19 forecasting and policy-making, urban mobility analysis and marketing, and obtaining business insights), location data collected from mobile devices often contain data from a biased population…
Comparing ISPs on broadband speed is challenging, since measurements can vary due to subscriber attributes such as operation system and test conditions such as access capacity, server distance, TCP window size, time-of-day, and network…
Most sampling techniques for online social networks (OSNs) are based on a particular sampling method on a single graph, which is referred to as a statistics. However, various realizing methods on different graphs could possibly be used in…
As crowdsourcing emerges as an efficient and cost-effective method for obtaining labels for machine learning datasets, it is important to assess the quality of crowd-provided data, so as to improve analysis performance and reduce biases in…
Collecting and analyzing meaningful data in mobile networks is the key to assessing network performance. Crowdsourced Network Measurements (CNMs) provide insights beyond the network layer and offer performance and other measurements at the…
Online sampling-supported visual analytics is increasingly important, as it allows users to explore large datasets with acceptable approximate answers at interactive rates. However, existing online spatiotemporal sampling techniques are…
Microtask crowdsourcing has enabled dataset advances in social science and machine learning, but existing crowdsourcing schemes are too expensive to scale up with the expanding volume of data. To scale and widen the applicability of…