This past summer, members of the DISSINET team attended conferences in Paris, Leeds, Cracow, Siena, and Mazamet, as well as summer schools in Varna and Manchester, showcasing the diversity of our research that applies network analysis, AI-assisted data capture, and various other quantitative approaches to inquisitorial records.
This summer, the DISSINET team was kept busy presenting the results of our innovative research projects and improving our own skillsets. Our researchers attended five major international conferences, showcasing various aspects of our cutting-edge research on medieval religious dissidence and inquisitorial records. Additionally, some of our researchers attended specialised summer programmes to receive additional training that will help expand DISSINET’s methodological capacities.
At Sunbelt 2025 in Paris (June 23–July 5), the annual gathering for the International Network for Social Network Analysis, Zoltán Brys presented “Modeling Medieval Incrimination Networks: Quasi-States, Events or Hyperevents?”, discussing how various network analysis approaches – Exponential Random Graph Models (ERGMs), Dynamic Network Actors Models (DyNAMs), and Relational Hyperevent Models (RHEMs) – can best be applied to study patterns of incrimination across large sets of inquisition records.
During the International Medieval Congress in Leeds (July 7–10), DISSINET co-sponsored three of the four sessions in the panel series “The Social Contexts of Religious Dissidence and Its Repression” centred on the themes of language and the social, local economies and local politics, and socio-cultural consequences. Team members presented on topics ranging from gendered patterns in heresy trials (David Zbíral), the socioeconomic profiles of dissidents (Katia Riccardo), prosecuting anti-inquisitorial resistance (Stanislaw Banach), and AI-assisted data capture (Robert Shaw).
At the IAHR World Congress in Cracow (August 24–30), a conference of the International Association for the History of Religions, three DISSINET researchers explored computational methods in the study of religions. Presentations dealt with gender analysis of inquisition registers (David Zbíral), mapping collective social categories across inquisition records (Tomáš Hampejs), and ritual diversity in Catharism (Katalin Suba).
Between August 31 and September 5, Tomáš Hampejs participated in the 21st Conference on Complex Systems in Siena, Italy, presenting his research on modeling sentencing patterns in medieval inquisitorial records.
Finally, between 5 and 7 September, DISSINET co-organized a conference hosted by the Collectif International de Recherche sur le CAtharisme (CIRCAED) titled Portraits of Heresy: Medieval Religious Dissidence through the Lens of Polemical and Inquisitorial Sources in the French town of Mazamet. The team presented on quantitative approaches to studying gendered actions in inquisitorial records (David Zbíral), discourses of guilt in the Languedocian inquisition (Robert Shaw), Cathar ritual formulae (Katalin Suba), and the social profiles of dissidents in Bologna (Katia Riccardo).
In addition, DISSINET researchers joined two summer schools: between 2 and 8 September, Gideon Kotzé attended workshops on Recent Advances in Natural Language Processing in Varna, Bulgaria, where he and his teammates won a text-mining competition for offensive language recognition – in other words, hate speech detection – across records in multiple languages. This success bodes well for DISSINET’s future work, which, in the coming years, will focus on examining medieval hate speech against heterodox Christians.
Meanwhile, Katia Riccardo participated in the Statistical Network Modelling for Social Sciences summer school in Manchester, UK (September 15–19), improving her capabilities in applying the SNA methods at the heart of DISSINET’s research.
As part of DISSINET's corpus work, we have procured a transcription of the hitherto unpublished register of John Galand and William of Saint-Seine, 1283–1302, that we have made available in the Zenodo repository.
Large Language Models (LLMs) hold significant promise for extracting information from historical texts. If used appropriately, they can speed up the research process, compensating for resource limitations and helping researchers achieve ambitious objectives in quantitative cultural history.
