15 citations · 49 across the 18 of their papers we have counts for
5 papers · 1 filter
Stick-Breaking Embedded Topic Model with Continuous Optimal Transport for Online Analysis of Document Streams
Federica Granese, Serena Villata, Charles Bouveyron
Online topic models are unsupervised algorithms to identify latent topics in data streams that continuously evolve over time. Although these methods naturally align with real-world…
Beating Harmful Stereotypes Through Facts: RAG-based Counter-speech Generation
Greta Damo, Elena Cabrio, Serena Villata
Counter-speech generation is at the core of many expert activities, such as fact-checking and hate speech, to counter harmful content. Yet, existing work treats counter-speech gene…
RooseBERT: A New Deal For Political Language Modelling
Deborah Dore, Elena Cabrio, Serena Villata
The increasing amount of political debates and politics-related discussions calls for the definition of novel computational methods to automatically analyse such content with the f…
Effectiveness of Counter-Speech against Abusive Content: A Multidimensional Annotation and Classification Study
Greta Damo, Elena Cabrio, Serena Villata
Counter-speech (CS) is a key strategy for mitigating online Hate Speech (HS), yet defining the criteria to assess its effectiveness remains an open challenge. We propose a novel co…
Merging Embedded Topics with Optimal Transport for Online Topic Modeling on Data Streams
Federica Granese, Benjamin Navet, Serena Villata +1
Topic modeling is a key component in unsupervised learning, employed to identify topics within a corpus of textual data. The rapid growth of social media generates an ever-growing…