AESLA 2024

Paolo Rosso is Full Professor at the Universitat Politècnica de València (UPV), where he is also a member of the Pattern Recognition and Human Language Technology (PRHLT) research center. His research interests are focused on social media text analysis, mainly on fake news and hate speech detection, author profiling, and sarcasm detection. He has published 50+ articles in journals (34 Q1) and 400+ articles in conferences and workshops. He is in the ranking of the top H-index scientists in Spain. In November 2022 he received the UPV Research Award in the category of Excellent Publication in Engineering and Technology for the work on Automatic identification and classification of misogynistic language on Twitter. He has been PI of several national and international research projects funded by EC, US Army Research Office, Qatar National Research Fund, and Vodafone Spain. Some of them addressed the problem of hate speech such as the project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech funded by the Spanish Ministry of science and innovation), the public procurement with OBERAXE (the Spanish Observatory on racism and xenophobia of the Secretary of State for Migration), and the project on Resources and applications for detecting and classifying polarized hate speech in Arabic social media (funded by Qatar National Research Fund). Paolo Rosso helped organising 30+ shared tasks at the PAN Lab at CLEF and FIRE evaluation forums, SemEval, IberLEF and Evalita on topics such as author profiling (e.g. profiling bots, haters, and fake news spreaders), hate speech detection, irony detection, misogyny, sexism and toxic language identification in Twitter. He has been advisor of 26 PhD theses on the above topics and currently he is the advisor of 8 PhD students.

AI for the detection and analysis of disinformation, conspiracy theories and critical thinking

The rise of social media has offered a fast and easy way for the propagation of disinformation and conspiracy theories. Despite the research attention that has received, disinformation detection remains an open problem and users keep sharing texts that contain false statements. In this keynote I will describe how to go beyond textual information to detect disinformation, taking into account also affective and visual information because providing important insights on how disinformation spreaders aim at triggering certain emotions in the readers. I will also describe how psycholinguistic patterns and users' personality traits may play an important role in discriminating disinformation spreaders from fact checkers. Finally, I will comment on some studies on the propagation of conspiracy theories. In the framework of the PAN Lab we will organise a challenge to discriminate between conspiracy narratives and critical thinking. Most of the work was done in the framework of the following research projects: IBERIFIER, the Iberian media research & fact-checking hub on disinformation funded by the European Digital Media Observatory (2020-EU-IA-0252), XAI-DisInfodemics: eXplainable AI for disinformation and conspiracy detection during infodemics (PLEC2021-007681), and FAKEnHATE-PdC: FAKE news and HATE speech (PDC2022-133118-I00), both funded by MCIN/AEI/10.13039/501100011033 and by European Union NextGenerationEU/PRTR.