2025 on

Detecting bias in algorithms used to disseminate information in social networks

Sun, 26 Oct 2025 00:00:00 +0000

Authors: Vedran Sekara, Ivan Dotu, Manuel Cebrian, Esteban Moro, and Manuel Garcia−Herranz
Publication: PNAS Nexus, 2025, 4, pgaf291 [Journal | ArXiv]

Abstract: Social connections are conduits through which individuals communicate, information propagates, and diseases spread. Identifying individuals who are more likely to adopt ideas and spread them is essential in order to develop effective information campaigns, maximize the reach of resources, and fight epidemics. Consequently, a lot of work has focused on identifying influencers in social networks with various influence maximization algorithms being proposed. Based on extensive computer simulations on synthetic and 10 diverse real-world social networks we show that seeding information in social networks using state-of-the-art influence maximization methods creates information gaps. Our results show that these algorithms select influencers who do not disseminate information equitably, threatening to create an increasingly unequal society. To overcome this issue, we devise a multiobjective algorithm which both maximizes influence and information equity. Our results demonstrate it is possible to reduce vulnerability at a relatively low trade-off with respect to spread. This highlights that in our search for maximizing the spread of information we do not need to compromise on information equality.

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How Algorithmic outreach lead to information inequality, Health Medicine Network
Algorithmic Outreach Drives Growing Information Inequality, Scienmag

Using human mobility data to quantify experienced urban inequalities

Wed, 26 Feb 2025 00:00:00 +0000

Authors: Fengli Xu, Qi Wang, Esteban Moro, Arianna Salazar Miranda, Marta C. González, Chaoming Song, Carlo Ratti, Luis Bettencourt, James Evans
Publication: Nature Human Behavior (2025) LINK

Abstract: The lived experience of urban life is shaped by personal mobility through dynamic relationships and resources, marked not only by access and opportunity, but also inequality and segregation. The recent availability of fine-grained mobility data and context attributes ranging from venue type to demographic mixture offer researchers a deeper understanding of experienced inequalities at scale, and pose many new questions. Here we review emerging uses of urban mobility behaviour data, and propose an analytic framework to represent mobility patterns as a temporal bipartite network between people and places. As this network reconfigures over time, analysts can track experienced inequality along three critical dimensions: social mixing with others from specific demographic backgrounds, access to different types of facilities, and spontaneous adaptation to unexpected events, such as epidemics, conflicts or disasters. This framework traces the dynamic, lived experiences of urban inequality and complements prior work on static inequalities experience at home and work.

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Human mobility is well described by closed-form gravity-like models learned automatically from data

Fri, 07 Feb 2025 00:00:00 +0000

Authors: Oriol Cabanas-Tirapu, Lluís Danús, Esteban Moro, Marta Sales-Pardo & Roger Guimerà
Publication: Nature Communications 16, 1336 (2025). LINK

Abstract: Modeling human mobility is critical to address questions in urban planning, sustainability, public health, and economic development. However, our understanding and ability to model flows between urban areas are still incomplete. At one end of the modeling spectrum we have gravity models, which are easy to interpret but provide modestly accurate predictions of flows. At the other end, we have machine learning models, with tens of features and thousands of parameters, which predict mobility more accurately than gravity models but do not provide clear insights on human behavior. Here, we show that simple machine-learned, closed-form models of mobility can predict mobility flows as accurately as complex machine learning models, and extrapolate better. Moreover, these models are simple and gravity-like, and can be interpreted similarly to standard gravity models. These models work for different datasets and at different scales, suggesting that they may capture the fundamental universal features of human mobility.

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