At the Society Complexity and Data Science Laboratory (CDCS Lab), our primary objective is to harness the power of data science to tackle a diverse array of challenges. These range from understanding the spread of information on social media platforms to 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.


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) 


Matteo Cinelli, Researcher- Data-Driven Modeling of Social Dynamics - Sapienza Alessandro Galeazzi, PostDoc - Data-Driven Modeling of Social Dynamics - Ca'FoscariMichele Avalle, PostDoc - Data-Driven Modeling of Social Dynamics - Sapienza
Saverio Storani, PostDoc - Data-Driven Modeling of Social Dynamics - Sapienza 
Gabriele Etta, Ph.D Candidate - Data-Driven Modeling of Social Dynamics  - Sapienza
Niccolò Di Marco, Ph. D. Candidate - Data-Driven Modeling of Social Dyanmics - Università di Firenze
Anita Bonetti, Ph.D Candidate - Meme Dynamics - Sapienza
Emanuele Sangiorgio, Ph.D Candidate - Collective Memory in Online Dynamics - Sapienza
Shayan 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 Twitter


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