New Avenues in Computational Irony Detection in Social Media

Resumen

PhD thesis in Computer Science focused on irony detection in social media, written by Reynier Ortega Bueno under the supervision of Prof. Paolo Rosso, at the Universitat Politècnica de València. This thesis investigates irony detection as a multifaceted linguistic, computational, and social challenge, addressing multilingual variation, multimodality, and corpus bias. The work introduces an attentive LSTM architecture integrating linguistic and deep features for Spanish irony and satire detection, and proposes an end-to-end model combining textual and visual transformers for multimodal irony detection in social media content. This work further analyses topic bias in irony corpora, demonstrating its detrimental impact on model generalisation and showing gains achieved through bias identification and mitigation. The defense took place in Valencia, Spain, on July 25th, 2025. The doctoral committee was composed by Rafael Berlanga Llavori (Universitat Jaume I), Els Lefever (Ghent University), and Tony Veale (University College Dublin). The thesis received an international mention, an excellent qualification, and the distinction of Cum Laude.