123 citations · 475 across the 24 of their papers we have counts for
32 papers
Face the Facts! Evaluating RAG-based Pipelines for Professional Fact-Checking
Daniel Russo, Stefano Menini, Jacopo Staiano +1
Natural Language Processing and Generation systems have recently shown the potential to complement and streamline the costly and time-consuming job of professional fact-checkers. I…
NLP for Counterspeech against Hate: A Survey and How-To Guide
Helena Bonaldi, Yi-Ling Chung, Gavin Abercrombie +1
In recent years, counterspeech has emerged as one of the most promising strategies to fight online hate. These non-escalatory responses tackle online abuse while preserving the fre…
Countering Misinformation via Emotional Response Generation
Daniel Russo, Shane Peter Kaszefski-Yaschuk, Jacopo Staiano +1
The proliferation of misinformation on social media platforms (SMPs) poses a significant danger to public health, social cohesion and ultimately democracy. Previous research has sh…
Weigh Your Own Words: Improving Hate Speech Counter Narrative Generation via Attention Regularization
Helena Bonaldi, Giuseppe Attanasio, Debora Nozza +1
Recent computational approaches for combating online hate speech involve the automatic generation of counter narratives by adapting Pretrained Transformer-based Language Models (PL…
Benchmarking the Generation of Fact Checking Explanations
Daniel Russo, Serra Sinem Tekiroglu, Marco Guerini
Fighting misinformation is a challenging, yet crucial, task. Despite the growing number of experts being involved in manual fact-checking, this activity is time-consuming and canno…
Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering
Helena Bonaldi, Sara Dellantonio, Serra Sinem Tekiroglu +1
Fighting online hate speech is a challenge that is usually addressed using Natural Language Processing via automatic detection and removal of hate content. Besides this approach, c…
Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study
Serra Sinem Tekiroglu, Helena Bonaldi, Margherita Fanton +1
In this work, we present an extensive study on the use of pre-trained language models for the task of automatic Counter Narrative (CN) generation to fight online hate speech in Eng…
Multilingual Counter Narrative Type Classification
Yi-Ling Chung, Marco Guerini, Rodrigo Agerri
The growing interest in employing counter narratives for hatred intervention brings with it a focus on dataset creation and automation strategies. In this scenario, learning to rec…
Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators' Disagreement
Elisa Leonardelli, Stefano Menini, Alessio Palmero Aprosio +2
Since state-of-the-art approaches to offensive language detection rely on supervised learning, it is crucial to quickly adapt them to the continuously evolving scenario of social m…
Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech
Margherita Fanton, Helena Bonaldi, Serra Sinem Tekiroglu +1
Undermining the impact of hateful content with informed and non-aggressive responses, called counter narratives, has emerged as a possible solution for having healthier online comm…
Empowering NGOs in Countering Online Hate Messages
Yi-Ling Chung, Serra Sinem Tekiroglu, Sara Tonelli +1
Studies on online hate speech have mostly focused on the automated detection of harmful messages. Little attention has been devoted so far to the development of effective strategie…
Towards Knowledge-Grounded Counter Narrative Generation for Hate Speech
Yi-Ling Chung, Serra Sinem Tekiroglu, Marco Guerini
Tackling online hatred using informed textual responses - called counter narratives - has been brought under the spotlight recently. Accordingly, a research line has emerged to aut…
Toward Stance-based Personas for Opinionated Dialogues
Thomas Scialom, Serra Sinem Tekiroglu, Jacopo Staiano +1
In the context of chit-chat dialogues it has been shown that endowing systems with a persona profile is important to produce more coherent and meaningful conversations. Still, the…
GePpeTto Carves Italian into a Language Model
Lorenzo De Mattei, Michele Cafagna, Felice Dell'Orletta +2
In the last few years, pre-trained neural architectures have provided impressive improvements across several NLP tasks. Still, generative language models are available mainly for E…
Generating Counter Narratives against Online Hate Speech: Data and Strategies
Serra Sinem Tekiroglu, Yi-Ling Chung, Marco Guerini
Recently research has started focusing on avoiding undesired effects that come with content moderation, such as censorship and overblocking, when dealing with hatred online. The co…
Generating Challenge Datasets for Task-Oriented Conversational Agents through Self-Play
Sourabh Majumdar, Serra Sinem Tekiroglu, Marco Guerini
End-to-end neural approaches are becoming increasingly common in conversational scenarios due to their promising performances when provided with sufficient amount of data. In this…
CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech
Y. L. Chung, E. Kuzmenko, S. S. Tekiroglu +1
Although there is an unprecedented effort to provide adequate responses in terms of laws and policies to hate content on social media platforms, dealing with hatred online is still…
DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques
Oscar Araque, Lorenzo Gatti, Jacopo Staiano +1
Several lexica for sentiment analysis have been developed and made available in the NLP community. While most of these come with word polarity annotations (e.g. positive/negative),…
Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines
Youness Mansar, Lorenzo Gatti, Sira Ferradans +2
In this paper, we describe a methodology to infer Bullish or Bearish sentiment towards companies/brands. More specifically, our approach leverages affective lexica and word embeddi…
Why Do Urban Legends Go Viral?
Marco Guerini, Carlo Strapparava
Urban legends are a genre of modern folklore, consisting of stories about rare and exceptional events, just plausible enough to be believed, which tend to propagate inexorably acro…
Community dynamics in connected time-dependent multilayer networks
Marco Cristoforetti, Marco Guerini, Giuseppe Jurman +1
Different strategies have been considered to extract information from social media about how similarly people react to the same news or event. In this context, a powerful method is…
SentiWords: Deriving a High Precision and High Coverage Lexicon for Sentiment Analysis
Lorenzo Gatti, Marco Guerini, Marco Turchi
Deriving prior polarity lexica for sentiment analysis - where positive or negative scores are associated with words out of context - is a challenging task. Usually, a trade-off bet…
Echoes of Persuasion: The Effect of Euphony in Persuasive Communication
Marco Guerini, Gözde Özbal, Carlo Strapparava
While the effect of various lexical, syntactic, semantic and stylistic features have been addressed in persuasive language from a computational point of view, the persuasive effect…
Deep Feelings: A Massive Cross-Lingual Study on the Relation between Emotions and Virality
Marco Guerini, Jacopo Staiano
This article provides a comprehensive investigation on the relations between virality of news articles and the emotions they are found to evoke. Virality, in our view, is a phenome…