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PhD Research Student
Paula Reyero Lobo Photograph

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PhD. Researcher under the Marie Sklodowska-Curie Innovative Training Networks (ITN) programme. She is part of the Social Data Science group and researcher at the "NoBIAS- Artificial Intelligence without Bias" project.

Her PhD research focuses on developing novel techniques leveraging Semantic Web technologies to understand better and mitigate bias in hate speech detection.

Team: Harith Alani, Vaclav Bayer, Gregoire Burel, Enrico Daga, Tracie Farrell, Miriam Fernandez, , Martino Mensio, Angel Pavon Perez, Lara Piccolo, Ali Tavakoli

News

17 Sep 2024


30 Jan 2024


26 Sep 2023


15 Jul 2022


26 Apr 2022

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Publications

Publications | Visit External Site for Details  

Reyero Lobo, P., Daga, E., Alani, H. and Fernandez, M. (2024) Enhancing Hate Speech Annotations with Background Semantics, 27th European Conference on Artificial Intelligence (ECAI 2024) – Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024), Santiago de Compostela, Spain

Publications | Visit External Site for Details  

Reyero Lobo, P., Kwarteng, J., Russo, M., Fahimi, M., Scott, K., Ferrara, A., Sen, I. and Fernandez, M. (2023) A Multidisciplinary Lens of Bias in Hate Speech, The 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Kusadasi, Turkey

Publications | Visit External Site for Details  

Reyero Lobo, P., Daga, E., Alani, H. and Fernandez, M. (2023) Knowledge-Grounded Target Group Language Recognition in Hate Speech, 19th International Conference on Semantic Systems, Leipzig, Germany

Publications | Visit External Site for Details  
Publications | Visit External Site for Details Publications | Visit External Site for Details  

Reyero Lobo, P., Daga, E. and Alani, H. (2022) Supporting Online Toxicity Detection with Knowledge Graphs, Sixteenth International AAAI Conference on Web and Social Media, Atlanta, Georgia

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Research Themes

Latest Seminar
Microsoft Research Cambridge

Actions and their Consequences: Implicit Interactions with Machine Learned Knowledge Bases

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