Assessing the impact of contextual information in hate speech detection

In recent years, hate speech has gained great relevance in social networks and other virtual media because of its intensity and its relationship with violent acts against members of protected groups. Due to the great amount of content generated by users, great effort has been made in the research and development of automatic tools to aid the analysis and moderation of this speech, at least in its most threatening forms. One of the limitations of current approaches to automatic hate speech detection is the lack of context. Most studies and resources are performed on data without context; that is, isolated messages without any type of conversational context or the topic being discussed. This restricts the available information to define if a post on a social network is hateful or not. In this work, we provide a novel corpus for contextualized hate speech detection based on user responses to news posts from media outlets on Twitter. This corpus was collected in the Rioplatense dialectal variety of Spanish and focuses on hate speech associated with the COVID-19 pandemic. Classification experiments using state-of-the-art techniques show evidence that adding contextual information improves hate speech detection performance for two proposed tasks (binary and multi-label prediction). We make our code, models, and corpus available for further research

Beyond borders: transnational Italy

These three short articles investigate how exhibitions function as primary research outputs. They present critical reflections on the preparation and realisation of a series of exhibitions that drew on material produced by a large research team. Written from the perspectives of the professional curators and an academic researcher, the essays set out the theoretical premises of the exhibition as a medium of communication, and research tool. The exhibitions were part of the AHRC-funded large grant project ‘Transnationalizing Modern Languages. Mobility, Identity and Translation in Modern Italian Cultures’ (2014–17). The exhibition was first held at the British School at Rome between October 26 and November 11 2016, and then at the Italian Cultural Institute in London from December 5 2016 to January 14 2017. Further iterations have been staged at the Calandra Institute, New York, the Museo Italiano, Melbourne, and at the Italian Cultural Institute in Addis Abeba and Tunis

B.Motion. Spazio di riflessione fuori e dentro le arti performative

Ricognizione di temi legati ai performance studies in relazione alla nozione di territorio, confine, visione, performativit\ue0. La metodologia adottata per questo volume \ue8 trasversale

roots§routes - research on visual culture

Ogni numero di roots§routes ha un filo conduttore in base al quale vengono chiesti dei contributi a diversi ricercatori, teorici e artisti. Come redattore mi sono occupata insieme al resto del team della selezione dei contributi e del loro editing seguendone tutto il processo fino alla pubblicazione. Ho curato la strategia di social media management per la promozione della rivista tramite i canali facebook e twitter sia dal punto di vista progettuale che esecutivo producendo e selezionando i contenuti audiovisivi a corredo degli articoli

Contextualized Hate Speech

Resultados de un estudio sobre detección del discurso de odio en redes sociales, desde una perspectiva interdisciplinaria, abordando el discurso de odio tanto cuantitativa como cualitativamente, durante el marco temporal de la pandemia de COVID-19. Se construyó un corpus original en la variante "rioplatense" del español centrado en el discurso de odio asociado a la pandemia de COVID-19. Una muestra de este corpus fue anotada manualmente utilizando pautas cuidadosamente diseñadas. Los experimentos de clasificación realizados, utilizando técnicas de aprendizaje automático basadas en transformadores de última generación muestran evidencia de que agregar información contextual mejora el rendimiento de la detección del discurso de odio para dos tareas propuestas: predicción binaria y de múltiples etiquetas, aumentando su Macro F1 en 4,2 y 5,5. puntos, respectivamente. Estos resultados resaltan la importancia del uso de información contextual en la detección del discurso de odio, en este caso las noticias que dieron lugar a los comantarios en la red social Twitter. Código, modelos y corpus están disponibles para futuras investigaciones.Fil: Pérez, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Cotik, Viviana Erica. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Luque, Franco Martín. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Moro, Agustín. Universidad Nacional del Centro de la Provincia de Buenos Aires; ArgentinaFil: Serrati, Pablo Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Miguel, Paula Gabriela. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Debandi, Natalia. Universidad Nacional de Río Negro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gravano, Agustin. Universidad Torcuato Di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin