Speakers

Talk title: Abusive Language Detection on Social Media: Are We Far from the Shallow Now?

Abstract: In recent years, abusive language and in particular the phenomenon of online hatred against marginalized and vulnerable groups are exponentially increasing in social media platforms becoming a relevant social problem that needs to be monitored. Computational linguistics techniques have been applied to monitoring and counteracting toxic speech online, with particular emphasis on the development of linguistic resources and automatic tools for analyzing, detecting, and contrasting various forms of abusive language, ranging from anti-immigrants discourse to misogynistic behaviors. The research field encompasses several challenges that will be discussed. The targeted nature of online hatred and the multilingual environment pose challenges related to the development of robust approaches for abusive language detection in multidomain and multilingual settings. Additionally, continuous monitoring requires evaluating the temporal robustness of models over time. Pragmatic aspects associated with the use of profanity must also be addressed. Moreover, abusive language is often expressed and to be interpreted in the context of widespread social phenomena of stereotyping and gender/ethnic discrimination, and we are interested in recognizing implicit forms of abusive language. Two key lines of reflections will be proposed. The first line addresses the challenges related to the need of applying semantic grids of finer-grained analysis, encompassing the examination of intersectional hatred expressions and the identification of underlying phenomena, such as prejudices, unintended biases, and subtle forms of abuse like microaggressions. In this context, the importance of inclusive design in corpus development will be discussed, aiming to incorporate the perspectives of marginalized groups, in accordance with the new perspectivist data manifesto. On the other hand, we will delve into the potential of leveraging computational linguistics methods to actively facilitate interventions against online hate speech. This involves the creation of positive counter-narratives, dedicated to raising awareness about toxic discourse online and empowering individuals facing discrimination to express themselves both in virtual and real-world settings.

Bio: Viviana Patti  is Associate Professor of Computer Science at the University of Turin and part of the scientific board and executive committee of the Center for Logic, Language, and Cognition. Her main research interests are in the areas of NLP, Computational Linguistics and Affective Computing, and include sentiment analysis, emotion recognition and irony detection, with a focus on social media texts and the relation between language and social structure. She is applying her research in the field of hate speech monitoring, with a special interest on hate speech against migrants, populist rhetoric and automatic misogyny identification. She has been and is involved in funded projects on fighting hate speech and stereotypes against immigrants. She leads the development of Twitter corpora and models for multilingual hate speech detection and sentiment analysis exploited in shared tasks and international evaluation campaigns for different languages. She coordinated the pilot project "EVALITA4ELG: EVALITA Italian language reference resources, NLP services and tools for the ELG platform" funded by the European Language Grid H2020 project. She has (co-)authored 100+ peer-reviewed-publications, and serves regularly on boards and committees in NLP/AI journals and conferences. Since 2016 she is a member of the board of directors of the Italian Association of Computational Linguistics (AILC), and she serves in the role of vice-president in the new board 2022-25.

Talk title: One size does not fit all: building NLP models for real-world applications

Abstract: Despite the recent and impressive advances in the Natural Language Processing and Machine Learning areas, most models are still trained on general / large-scale data, assuming an "one-size-fits-all" approach. However, for most real-world applications, personalised, task-oriented models are more suitable and can bring significant improvements as well as reduce unwanted biases. In this talk, I will present mine and my team's work on NLP models for applications that require personalisation and / or are task oriented. I will present our methods and results for classification-based (e.g. disinformation classification) and generation-based (e.g. machine translation) tasks, discussing the challenges of researching personalised, task-oriented NLP. 

Bio: Carolina Scarton is a Senior Lecturer in Natural Language Processing at the Department of Computer Science, University of Sheffield, UK. She is a member of the Natural Language Processing group and part of the GATE team. Previously, she worked as an Academic Fellow at the University of Sheffield and as a Research Associate for the WeVerify and SIMPATICO European projects. In 2017, she was awarded a PhD degree in Computer Science from the University of Sheffield, funded by the EXPERT project (a Marie Curie ITN network). She also has a MSc and a BSc degree from the University of São Paulo, Brazil. Dr Scarton is particularly interested in online misinformation detection and verification, text adaptation, machine translation, evaluation of NLP task outputs, and multilingual and multimodal NLP models. She is the Sheffield PI of two research project on the area of disinformation analysis (EDMO Ireland, funded by the European Commission and HORIZON EU VIGILANT, funded by Innovate UK) and acts as a CO-I for one project in disinformation analysis (HORIZON EU vera.ai, funded by Innovate UK) and one project in idiomaticity processing (funded EPSRC).