Network science and computational social science allow us to understand collective human behaviour by analysing data and making models of the digital interactions we have online. The closing gap between off- and online activities lets us perform this task better than ever, bringing both knowledge of the large-scale structure of society, and challenges in predicting the future behaviour of individuals. Here are some of my research lines in this amazing field
Many complex systems develop rankings of their elements that emerge from networked interactions. These rankings evolve according to system-dependent mechanisms of interaction and reflect the relevance of elements in performing a function in the system. By analysing ranking data on many social, biological, and economic systems, as well as simple models of rank dynamics, we show there are generic features of rank stability that allow us to model and predict patterns of ranking behaviour across complex systems
We use large-scale data from online social platforms to study and predict the temporal evolution of social contagion
processes and cascading behaviour related to the use of innovations and new digital markets.
By considering data features like weighted or multiplex social interactions, product competition,
and temporal social networks, we aim at increasing the explanatory and predictability power of social contagion models
With a mean-field bifurcation analysis of a simple model of social network evolution, we estimate how much of the observed homophily in friendship and communication networks is an amplification due to triadic closure, which explains the rise of segregated groups in society. Homophily-based models also allow us to infer attitudinal space embeddings for social network data.
We validate opinion-formation models with data to emulate the
temporal evolution of conflicts between editors in Wikipedia. We find three typical scenarios for the way editors disagree while writing articles: short conflicts, plateaus of consensus among fights, and uninterrupted controversy. We wish not only to identify the ways by which people manage to collaborate despite their differences, but to propose efficient measures of prevention and control to minimise the harmful effects of social conflict
Opinions synthesize our perceptions and the knowledge we have of the external world, other people, and ourselves. To better understand the impact opinions have on social interactions and decision-making, we perform small controlled experiments and analyse idealized models of opinion formation, deception, and network change, potentially under the effect of algorithmic bias, letting us gauge how opinions influence the structure and dynamics of off- and online society
By combining large-scale data on multilayer urban mobility and socioeconomic networks with toy models and optimization algorithms, we devise data-modelling frameworks to explore the effect of external policies on urban and socioeconomic development. These platforms will suggest optimal policies given societal and economic constraints to, say, maximize use of bikes while minimizing changes to the mobility network
In collaboration with gaming companies, we want to co-develop games designed to test hypotheses of social activity related to cooperative versus competitive behavior, homophilous interactions, and social group formation. With statistical inference methods and machine-learning algorithms, we want to identify the most relevant features of individuals and game design that predict engagement. This analysis will help us validate long-standing theories of social behavior, as well as improve business models of partner gaming companies
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