Dr. Cristina Chueca Del Cerro

Dr. Cristina Chueca Del Cerro

Research Associate in Computational Social Sciences at Durham University.

Publications

The importance of structure: using targeted rewiring to explore social networks property interdependencies

Published in PLOS One, 2025

Social networks typically have skewed degree distributions and relatively high clustering and assortativity coefficients. Some studies have explored the relationships between these properties, but have given limited attention to social networks and have found conflicting evidence. To expand our understanding of the ways that properties constrain each other in social networks we use separate degree-preserving rewiring algorithms to manipulate assortativity, clustering coefficient and mean geodesic of networks constructed from seven diverse empirical degree sequences. We measured centrality (mean and Gini coefficient of several measures), clustering, assortativity and network distances. Only a small number of property pairs showed a relationship. Further, where interdependencies do exist, they are conditional and occur only for specific value ranges or a subset of the tested networks.

Recommended citation: Chueca Del Cerro, C., & Badham, J. (2025). The importance of structure: using targeted rewiring to explore social networks property interdependencies. PLOS One https://doi.org/10.1371/journal.pone.0336496

Tunable network properties with Hamill and Gilbert’s Social Circles generator

Published in Applied Network Science, 2025

Hamill and Gilbert (J Artif Soc Soc Simul 12, 1–23, 2009) developed the Social Circles algorithm to generate synthetic networks that have properties of real social networks such as skewed degree distribution, positive clustering coefficient, degree assortativity and short path lengths. To assess the viability of Social Circles as a general network generator, we systematically examine the relationship between algorithm parameters and a broader range of structural properties of the generated networks. We varied social reaches for agents, distribution of social reaches in the population, and node density. We find that edge density and centrality measures can be controlled in a predictable way: longer reaches are associated with denser networks, shorter paths, lower degree assortativity (with some exceptions), and smaller variation in centrality measures. However, these network properties changed together and there is limited capacity to control properties separately. Further, clustering coefficient is insensitive to algorithm inputs. Thus, it cannot be used as a general network generator as it stands. If these properties are important, Social Circles could be used to generate starting networks with reasonable social structure, but further steps would be required to refine the structural properties.

Recommended citation: Chueca Del Cerro, C. and Badham, J. (2025) Tunable network properties with Hamill and Gilbert’s Social Circles generator. Applied Network Science https://doi.org/10.1007/s41109-025-00744-5

The power of social networks and social media’s filter bubble in shaping polarisation: an agent-based model

Published in Applied Network Science, 2024

The role social media platforms play on the emergence of polarisation is an ongoing debate in the political communication literature. Social media’s filter bubbles and online echo chambers shape people’s opinions by curating the information they have available. However, the extent to which this is the case remains unclear. Social simulation scholars have provided valuable insights into the subject through opinion dynamics models and agent-based modelling approaches. This article proposes a social simulation approach to the topic of opinion dynamics from a political communication perspective to understand how social network configurations and the media environment contribute to the emergence of national identity polarisation. We built an agent-based simulation model of national identity dynamics with a multilayer multiplex network of interacting agents in a hybrid media environment of both, traditional media and social media platforms. We use the Catalan secessionist movement to ground, contextualise and empirically inform parts of our model. We found that the initial social network setup conditions had a large impact on the emergence of polarisation amongst agents. In particular, homophily-based social networks composed of a majority of like-minded individuals produced greater polarisation compared to random networks. This was aggravated in the presence of social media filtering algorithms, selectively exposing agents to supportive information. These results emphasise the importance of both the selective exposure by social media filtering algorithms and one’s social networks (echo chambers) for polarisation to emerge. This interaction reinforces the influence of social media platforms and social networks have on the emergence of polarisation.

Recommended citation: Chueca Del Cerro, C. (2024) The power of social networks and social media’s filter bubble in shaping polarisation: an agent-based model. Applied Network Science 9, 69, pp.1-32 https://doi.org/10.1007/s41109-024-00679-3

Systems science methods in public health: what can they contribute to our understanding of and response to the cost-of-living crisis?

Published in Journal of Epidemiology and Community Health, 2023

Background Many complex public health evidence gaps cannot be fully resolved using only conventional public health methods. We aim to familiarise public health researchers with selected systems science methods that may contribute to a better understanding of complex phenomena and lead to more impactful interventions. As a case study, we choose the current cost-of-living crisis, which affects disposable income as a key structural determinant of health. Methods We first outline the potential role of systems science methods for public health research more generally, then provide an overview of the complexity of the cost-of-living crisis as a specific case study. We propose how four systems science methods (soft systems, microsimulation, agent-based and system dynamics models) could be applied to provide more in-depth understanding. For each method, we illustrate its unique knowledge contributions, and set out one or more options for studies that could help inform policy and practice responses. Results Due to its fundamental impact on the determinants of health, while limiting resources for population-level interventions, the cost-of-living crisis presents a complex public health challenge. When confronted with complexity, non-linearity, feedback loops and adaptation processes, systems methods allow a deeper understanding and forecasting of the interactions and spill-over effects common with real-world interventions and policies. Conclusions Systems science methods provide a rich methodological toolbox that complements our traditional public health methods. This toolbox may be particularly useful in early stages of the current cost-of-living crisis: for understanding the situation, developing solutions and sandboxing potential responses to improve population health.

Recommended citation: Höhn A, Stokes J, Pollack R, Boyd, J, Chueca Del Cerro, C, Elsenbroich, C, Heppenstall, A, Hjelmskog, A, Inyang, E, Kopasker, D, et al. (2023) Systems science methods in public health: what can they contribute to our understanding of and response to the cost-of-living crisis? Journal of Epidemiology and Community Health,77:610-616. https://doi.org/10.1136/jech-2023-220435