Corrado Monti

Probabilistic Models

Likelihood-Based Methods Improve Parameter Estimation in Opinion Dynamics Models

Jacopo Lenti, Corrado Monti, Gianmarco De Francisci Morales.

Proceedings of the 17th ACM International Conference on Web Search and Data Mining (WSDM '24).

Link | PDF | GitHub

On Learning Agent-Based Models from Data

Corrado Monti, Marco Pangallo, Gianmarco De Francisci Morales, Francesco Bonchi.

Scientific Reports 13 (1), June 2023 (Nature Publishing Group)

Link | PDF | GitHub

Evidence of Demographic rather than Ideological Segregation in News Discussion on Reddit

Corrado Monti, Jacopo D'Ignazi, Michele Starnini, Gianmarco De Francisci Morales

Proceedings of the ACM Web Conference 2023 (WWW2023), May 1-5, 2023, Austin, TX, USA. ACM

Link | PDF | GitHub | Dataset | Short video

The Pursuit of Peer Support for Opioid Use Recovery on Reddit

Duilio Balsamo, Paolo Bajardi, Gianmarco De Francisci Morales, Corrado Monti, Rossano Schifanella.

International AAAI Conference on Weblogs and Social Media (ICWSM2023). AAAI, 2023.

Link | PDF

Cascade-Based Echo Chamber Detection

Marco Minici, Federico Cinus, Corrado Monti, Giuseppe Manco, and Francesco Bonchi.

Proceedings of the 31th ACM International Conference on Information and Knowledge Management (CIKM 2022). ACM, 2022.

Link | PDF

Learning Ideological Embeddings from Information Cascades

Corrado Monti, Giuseppe Manco, Cigdem Aslay, and Francesco Bonchi.

Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021). ACM, 2021.

Link | PDF | GitHub | Talk

Learning Opinion Dynamics From Social Traces

Corrado Monti, Gianmarco De Francisci Morales, and Francesco Bonchi.

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD2020). ACM, 2020.

Link | PDF | GitHub | Short video

Social classes and Italian elections

Italian, not peer-reviewed.

Corrado Monti. “Classi Sociali Nelle Elezioni 2018 e 2019: Un’analisi Bayesiana Del Voto.” Centro Studi Argo, 2019.

Link | GitHub