Corrado Monti

Thesis proposals

These projects are proposals for interested Master's students who would like to work with me. Students from Università di Torino are encouraged to get in contact, but remote or visiting students are welcomed as well.

Here are some examples of published works that I developed with interested students.

This page might not be complete with the most recent opportunities, so if you are interested in my research topics feel free to drop me an email!

Mapping Political Violence in the Italian Parliament, 1921–1922

How did political actors connect episodes of violence into competing narratives during the crisis of the Italian liberal state? Parliamentary debates from 1921–1922 provide unusually detailed accounts of fascist violence, including references to specific attacks, locations, organisations, victims, and alleged perpetrators. However, these episodes were framed in markedly different ways: liberal government representatives presented them as geographically circumscribed public-order problems, while other factions connected them to broader and mutually incompatible narratives of political conflict.

The first step of the project will be the construction of a machine-readable corpus from the historical proceedings of the Italian Chamber of Deputies from the available PDFs [1, 2]. Parliamentary speeches will be segmented by speaker and enriched through the semi-automatic extraction of people, organisations, places, dates, and episodes through named entity recognition. Each episode will be represented through structured attributes, including its location, participants, attributed causes, political characterisation, and the speakers or groups mentioning it.

These data will then be used to construct a temporal multilayer network connecting:

The main methodological question is whether different strategies for aggregating local episodes into more or less systemic representations of political violence can be measured quantitatively, following recent works in this direction [3, 4]. Community detection, bipartite projections [5], centrality measures will be used to compare the narrative structures produced by different political actors. Drawing on recent operationalizations of “open narratives,” [6] the project will treat narratives as recurrent processes through which political actors select discrete events and connect them to stable interpretative structures. Narrative construction will therefore be measured through the breadth, frequency, intensity, and relational structure of the episodes, actors, locations, and causal or moral attributes invoked by each political group.

Requirements: Good knowledge of Python (pandas, numpy, scikit-learn, Jupyter), and willingness to learn graph-analysis tools.

References

[1] P. Leifeld. “Policy Debates and Discourse Network Analysis: A Research Agenda.” Politics and Governance, 2020.

[2] M. Puren et al. “An NLP-friendly TEI Model for Historical Parliamentary Proceedings.” Digital Scholarship in the Humanities, 2025.

[3] C. Rollo, G. De Francisci Morales, Corrado Monti, and A. Panisson. “Communities, Gateways, and Bridges: Measuring Attention Flow in the Reddit Political Sphere.” Social Informatics, 2022.

[4] Y. Mejova, A. Capozzi, Corrado Monti, and G. De Francisci Morales. “Narratives of War: Ukrainian Memetic Warfare on Twitter.” Proceedings of the ACM on Human-Computer Interaction, 2025.

[5] B. Curran, K. Higham, E. Ortiz, and D. Vasques Filho. “Look Who’s Talking: Bipartite Networks as Representations of a Topic Model of New Zealand Parliamentary Speeches.” 2017.

[6] Howland, C. B. (2025). On the Narrative Construction of Reality in Political Discourse: A Theory, a Method, and Empirical Study. PhD dissertation, University of Pennsylvania.

Creative Identities and Skills in AI-Mediated Artistic Practice

Creative technologies increasingly influence which abilities artists cultivate, how they understand their own contribution, and how they develop a recognizable artistic practice. These questions are particularly relevant for digital and new-media artists whose work takes material or situated forms: installations, projected images, light works, audiovisual performances, event-based video, interactive systems, and other works created for specific spaces and audiences. Recent Human-Computer Interaction (HCI) research has examined how visual artists develop creative identities and seek inspiration through online platforms [1]. Previous work has shown that creative identity is sustained through recurring interactions with artworks, audiences, peers, and platform infrastructures. Less is known about how creative identity develops in local artistic communities whose works are not primarily produced for online circulation.

This project would investigate how artists working with material and situated creative practices understand each others' skill and identity as AI becomes integrated into their practice. It would do so with a mixed-method research approach, combining in-depth interviews with artists with large corpus text analysis.

First, a series of in-depth interviews would reconstruct artists’ working processes and technological trajectories. Focus would be on which skills artists consider central to their practice, how expertise moves between execution, direction, selection, editing, programming, and coordination, how artists combine digital and AI-mediated processes with materials and spaces, how they maintain continuity and recognizability across different media and how widely shared technologies affect their effort to develop an individual practice.

Second, the considerations emerged from such interviews would be validated by a longitudinal analysis of Reddit discussions over the years. The corpus will be constructed by selecting Reddit communities related to new-media art, interactive art, projection mapping, and audiovisual production (for instance: r/creativecoding, r/TouchDesigner, r/processing, r/generative, r/Projection_Mapping, r/lightingdesign, r/videoart, and r/ContemporaryArt). Such textual analysis would examine how these communities discuss artistic skill, technical expertise, individual style, and the relationships of these concept with AI-mediated artistic production.

Methods previously used to study linguistic and narrative change in online communities support the identification of recurring vocabularies, differences between communities, and changes associated with the diffusion of new technologies [4, 5], in order to answer the research question: how does AI-mediated artistic practice reshape artists’ understandings of skill and creative identity when digital processes contribute to materially and spatially situated artworks?

Requirements: Good knowledge of Python and data analysis, interest in qualitative interviewing and thematic analysis, familiarity with Natural Language Processing, willingness to learn big-data techniques such as PySpark.

References:

[1] Ellen Simpson and Bryan Semaan. “Infrastructures for Inspiration: The Routine of Creative Identity Through Inspiration on the Creative Internet.” In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). DOI: 10.1145/3706598.3713105.

[2] Charlotte Bird. “Artists and AI: Creative Interactions and Tensions.” In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems (CHI EA ’24). Association for Computing Machinery, New York, 2024, 1–6. DOI: 10.1145/3613905.3651041.

[3] Corey Ford and Nick Bryan-Kinns. “Towards a Reflection in Creative Experience Questionnaire.” In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). Association for Computing Machinery, New York, 2023. DOI: 10.1145/3544548.3581077.

[4] Corrado Monti, Luca Maria Aiello, Gianmarco De Francisci Morales, and Francesco Bonchi. “The Language of Opinion Change on Social Media under the Lens of Communicative Action.” Scientific Reports, 12, 17920, 2022.

[5] Yelena Mejova, Arturo Capozzi, Corrado Monti, and Gianmarco De Francisci Morales. “Narratives of War: Ukrainian Memetic Warfare on Twitter.” In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). 2025, 1–28.

Learning causal models of wild animal behavior from video data

Understanding how wild animals adapt their behavior to environmental conditions is a central problem in ecology and ethology. Weather, terrain, vegetation, time of day, and the presence of other animals may all influence whether an animal moves, feeds, rests, or remains vigilant. However, most machine-learning methods for animal monitoring focus on recognizing behavior from video, rather than learning the stochastic behavioral mechanisms that could have generated the observed sequences.

The publicly-available MammAlps data set [1] offers an opportunity to study this problem from a generative, agent-based modelling (ABM) perspective. It contains multi-view camera-trap recordings of wild mammals in the Swiss National Park, with individual tracks, species and behavior annotations, meteorological information, and reference segmentation maps of the observed environment. In particular, its long-term ecological sequences can be represented as temporal graphs in which animals are nodes, their state includes position, species, and current behavior, and connections encode spatial proximity or other possible interactions.

The aim of this project is to develop a data-driven agent-based model of animal behavior using Graph Diffusion Networks [2]. Graph Diffusion Networks combine graph neural networks (GNNs), which represent local interactions, with diffusion models, which capture the stochasticity of individual decisions. Rather than predicting a single next action, the model would learn a distribution over possible behavioral and movement transitions, i.e.:

ℙ( position at t+1 | position at t, environment at t , other animals at t).

A first part of the project would focus on constructing a suitable representation of the MammAlps sequences. Environmental features could include terrain and vegetation classes extracted from the reference scene maps, meteorological conditions, site, season, and time of day. Animal-level variables could include species, position, velocity, previous behavior, and the presence of nearby animals. Then, the project would develop a generative model of wild animal behavior. Besides its ecological application, the project would investigate a broader methodological question: whether Graph Diffusion Networks, originally developed to learn artificial agent-based models, can recover useful behavioral mechanisms directly from real-world observations.

Requirements: Good knowledge of Python and machine learning; familiarity with PyTorch is useful. Background in graph neural networks, generative models, causal inference, is welcome but not required.

References:

[1] Valentin Gabeff, Haozhe Qi, Brendan Flaherty, Gencer Sumbül, Alexander Mathis, and Devis Tuia. “MammAlps: A Multi-view Video Behavior Monitoring Dataset of Wild Mammals in the Swiss Alps.” CVPR 2025

[2] Francesco Cozzi, Marco Pangallo, Alan Perotti, André Panisson, and Corrado Monti. “Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks.” NeurIPS 2025