Research
At the Center for Data Science and Complexity for Society (CDCS), our primary objective is to harness the power of complex systems to tackle diverse challenges. These range from understanding the spread of information on social media platforms, developing models for epidemic outbreaks, and analyzing mobility trends.
We firmly believe in the synergy of network science and complex systems, utilizing this blend to construct mathematical models of intricate dynamics. It's through this integrated approach that we unravel large-scale, pivotal phenomena.
Highlights
Our last paper in Nature
Persistent interaction patterns across social media platforms and over time
Growing concern surrounds the impact of social media platforms on public discourse and their influence on social dynamics, especially in the context of toxicity. Here, to better understand these phenomena, we use a comparative approach to isolate human behavioural patterns across multiple social media platforms. In particular, we analyse conversations in different online communities, focusing on identifying consistent patterns of toxic content. Drawing from an extensive dataset that spans eight platforms over 34 years—from Usenet to contemporary social media—our findings show consistent conversation patterns and user behaviour, irrespective of the platform, topic or time. Notably, although long conversations consistently exhibit higher toxicity, toxic language does not invariably discourage people from participating in a conversation, and toxicity does not necessarily escalate as discussions evolve. Our analysis suggests that debates and contrasting sentiments among users significantly contribute to more intense and hostile discussions. Moreover, the persistence of these patterns across three decades, despite changes in platforms and societal norms, underscores the pivotal role of human behaviour in shaping online discourse.
Team (Academic Children)
Current
Matteo Cinelli, Researcher- Data-Driven Modeling of Social Dynamics - Sapienza Alessandro Galeazzi, PostDoc - Data-Driven Modeling of Social Dynamics - University of PaduaSaverio Storani, PostDoc - Data-Driven Modeling of Social Dynamics - Sapienza Gabriele Etta, PostDoc - Data-Driven Modeling of Social Dynamics - Sapienza
Niccolò Di Marco, PostDoc - Data-Driven Modeling of Social Dyanmics - SapienzaAnita Bonetti, Ph.D Candidate - Meme Dynamics - Sapienza
Emanuele Sangiorgio, Ph.D Candidate - Collective Memory in Online Dynamics - SapienzaShayan Alipour, Ph.D Candidate - The Interplay between meme and news - Sapienza
Edoardo Di Martino, Ph.D Candidate - Toxicity and Polarization Dynamics
Edoardo Loru, Ph.D Candidate - Toxicity and Coordinated Inauthentic Behavior
Giulio Pecile, Ph.D Candidate - Models and Algorithms for Modeling Social Dynamics
Simon Zollo, Ph.D Candidate - Political Opinion Inference from TwitterIrene Scalco, MA Student, Polarization Dynamics
Jacopo Nudo, MA Student, Algorithms on Social Dynamics
Mario Edoardo Pandolfo, MA Student, Language contagion
Completed
Carlo Valensise, PostDoc - Information Spreading and Memes Evolution - Centro FermiEmanuele Brugnoli, PostDoc - Information Spreading PatternsAntonio Peruzzi, Ph.D - Economic Impact of NewsAna Lucia Schmidt. (Supervisor) News spreading on a global scale, IMT LuccaAlessandro Bessi. (Supervisor) Quantitative understanding of online misinformation, IUSS Pavia.Michela Del Vicario. (Supervisor) Data driven modeling of social contagion, IMT LuccaFabiana Zollo. (Supervisor) Sensing social dynamics in the misinformation era, IMT Lucca