Abstract. Social connections are conduits through which individuals communicate, information propagates, and diseases spread. Identifying individuals who a

SODAS is delighted to host Daniel Juhász Vigild and Stephanie Brandl for the Fall 2025 Data Discussion series!

High-resolution human settlement maps provide detailed delineations of where people live and are vital for scientific and practical purposes, such as rapid disaster response, allocation of humanitarian resources, and international development. The increased availability of high-resolution satellite imagery, combined with powerful techniques from machine learning and artificial intelligence (AI), has spurred the creation of a wealth of settlement datasets. The agreement and alignment between these datasets has not been studied in detail. We compare three settlement maps developed by Google (Open Buildings), Meta (High Resolution Population Density Maps) and Microsoft (Global Building Footprints), and uncover which factors drive mismatch. Our study focuses on 44 African countries. We build a global machine learning model to predict where datasets agree, and find that geographic and socio-economic factors considerably impact overlap. However, we also find there is great variability across countries, suggesting complex interactions between country morphology and dataset overlap. It is vital to understand the shortcomings of AI-derived settlement layers as international organizations, governments, and NGOs are already experimenting with incorporating these into programmatic work. We anticipate our work to be a starting point for more critical and detailed analyses of AI derived datasets for humanitarian, policy, and scientific purposes.

Concerns about polarization and hate speech on social media are widespread. Affective polarization, i.e., hostility among partisans, is crucial in this regard as it links political disagreements to hostile language online. However, only a few methods are available to measure how affectively polarized an online debate is, and the existing approaches do not investigate jointly two defining features of affective polarization: hostility and social distance. To address this methodological gap, we propose a network-based measure of affective polarization that combines both aspects – which allows them to be studied independently. We show that our measure accurately captures the relation between the level of disagreement and the hostility expressed towards others (affective component) and whom individuals choose to interact with or avoid (social distance component). Applying our measure to a large-scale Twitter data set on COVID-19, we find that affective polarization was low in February 2020 and increased to high levels as more users joined the Twitter discussion in the following months.