International audience

International audience

Social media platforms and online communities have become essential sources for sharing information on medical issues and expressing personal health experiences. Platforms like Reddit’s r/health, health boards, and Quora serve as vital spaces where researchers and health-interested individuals can gather and exchange information for various purposes.According to official web statistics from Quora and Reddit, Reddit hosts over 3 million niche communities and receives approximately 1.9 billion monthly visits, making it a central hub for diverse discussions, including health-related topics. Quora, with around 300 million monthly active users, is another significant platform, offering a wealth of Q&A discussions that provide valuable insights into health issues and personal experiences.In recent years, evaluating health literacy has become an important area of research. Researchers are increasingly focused on assessing users’ abilities to manage, express, and engage with health-related information.This paper aims to cover the most common definitions of health literacy, the opportunities and threats it presents, as well as the available datasets for these studies, without neglecting the challenges inherent in this field of research.

One of the most important factors influencing patient outcomes is health literacy (HL), which is the capacity to obtain, comprehend, and use health information. Disparities still exist despite the abundance of digital health resources because of complicated medical terminology, a lack of personalization, and a lack of multilingual support. By utilizing diverse data sources, such as electronic health records (EHRs), online health communities (like Reddit), and medical ontologies (like UMLS, SNOMED-CT), this study examines how artificial intelligence (AI) can close the HL gap. We examine cutting-edge methods like large language models (LLMs) for text simplification (e.g., grade-level adaptation in GPT-4) and natural language processing (NLP) for HL classification (e.g., linguistic profiling in the ECLIPPSE study). We draw attention to issues such as cultural biases in HL evaluation, oversimplification of medical information, and difficulties integrating data. To personalize the delivery of health information, our suggested framework integrates AIdriven methods such as automatic HL level identification, concept mapping, and semantic enrichment. This work attempts to improve accessibility while maintaining clinical accuracy by combining structured (EHRs) and unstructured (social media) data. To guarantee equitable health communication, future directions include multilingual adaptation and real-world validation.

In this paper, we report on the results of a full-size evaluation campaign of various MT systems. This campaign is novel compared to the classical DARPA/NIST MT evaluation campaigns in the sense that French is the target language, and that it includes an experiment of meta-evaluation of various metrics claiming to better predict different attributes of translation quality. We first describe the campaign, its context, its protocol and the data we used. Then we summarise the results obtained by the participating systems and discuss the meta-evaluation of the metrics used.