Paolo Rosso’s research while affiliated with University of Valencia and other places

This study investigates the dissemination of disinformation on social media platforms during the DANA event (DANA is a Spanish acronym for Depresion Aislada en Niveles Altos, translating to high-altitude isolated depression) that resulted in extremely heavy rainfall and devastating floods in Valencia, Spain, on October 29, 2024. We created a novel dataset of 650 TikTok and X posts, which was manually annotated to differentiate between disinformation and trustworthy content. Additionally, a Few-Shot annotation approach with GPT-4o achieved substantial agreement (Cohen's kappa of 0.684) with manual labels. Emotion analysis revealed that disinformation on X is mainly associated with increased sadness and fear, while on TikTok, it correlates with higher levels of anger and disgust. Linguistic analysis using the LIWC dictionary showed that trustworthy content utilizes more articulate and factual language, whereas disinformation employs negations, perceptual words, and personal anecdotes to appear credible. Audio analysis of TikTok posts highlighted distinct patterns: trustworthy audios featured brighter tones and robotic or monotone narration, promoting clarity and credibility, while disinformation audios leveraged tonal variation, emotional depth, and manipulative musical elements to amplify engagement. In detection models, SVM+TF-IDF achieved the highest F1-Score, excelling with limited data. Incorporating audio features into roberta-large-bne improved both Accuracy and F1-Score, surpassing its text-only counterpart and SVM in Accuracy. GPT-4o Few-Shot also performed well, showcasing the potential of large language models for automated disinformation detection. These findings demonstrate the importance of leveraging both textual and audio features for improved disinformation detection on multimodal platforms like TikTok.

Equitable access to reliable health information is vital for public health, but the quality of online health resources varies by language, raising concerns about inconsistencies in Large Language Models (LLMs) for healthcare. In this study, we examine the consistency of responses provided by LLMs to health-related questions across English, German, Turkish, and Chinese. We largely expand the HealthFC dataset by categorizing health-related questions by disease type and broadening its multilingual scope with Turkish and Chinese translations. We reveal significant inconsistencies in responses that could spread healthcare misinformation. Our main contributions are 1) a multilingual health-related inquiry dataset with meta-information on disease categories, and 2) a novel prompt-based evaluation workflow that enables sub-dimensional comparisons between two languages through parsing. Our findings highlight key challenges in deploying LLM-based tools in multilingual contexts and emphasize the need for improved cross-lingual alignment to ensure accurate and equitable healthcare information.

Artificial intelligence is reshaping the legal landscape, with software tools now impacting various aspects of legal work. The intersection of Natural Language Processing (NLP) and law holds potential to transform how legal professionals, including lawyers and judges, operate, resolve disputes, and retrieve case information to formulate their decisions. To identify the current state of the applications of Transformers (also known as Large Language Models or LLMs ) in the legal domain, we analysed the existing literature from 2017 to 2023 through a database search and snowballing method. From 61 selected publications, we identified key application categories such as legal document analysis, case prediction, and contract review, along with their main characteristics. We observed a discernible upsurge in the volume of scholarly publications, a diversification of tasks undertaken (e.g., legal research, contract analysis, and regulatory compliance), and an increased range of languages considered. There has been a notable enhancement in the methodological sophistication employed by researchers in practical applications. The performance of models grounded in the Generative Pre-trained Transformer (GPT) architecture has consistently improved across various legal domains, including contract review, legal document summarization, and case outcome prediction. This paper makes several significant contributions to the field. Firstly, it identifies emerging trends in the application of LLMs within the legal domain, highlighting the growing interest and investment in this area. Secondly, it pinpoints methodological gaps in current research, suggesting areas where further development and refinement are needed. Lastly, it discusses the broader implications of these advancements for real-world legal tasks, offering insights into how LLM-based AI can enhance legal practice while addressing the associated challenges.

This is the third book in the Online Hate Speech Trilogy. It focuses on presenting methods for detecting, analysing, and combating toxic language on the Internet. Alongside the legal dilemmas born from a desire to punish hate speech disseminators, identifying online hate speech is one of the biggest challenges in the field of studies on violent narratives and virtual attacks. The authors analyse the challenges of identifying violent narratives through automation, the advantages of manually coding social media posts, and the opportunities offered by AI in this field of research.