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.

This research aims to compare the performance of automatic deception detection in synchronous computer-mediated communication (CMC) with and without incorporating structural features. In addition, the development of deception detection models draws on the linguistic features that have been widely studied in the online deception literature. The results suggest that structural features can be effective in detecting deception and combining the structural features with linguistic features can improve the performance of detecting deception.

One of the main goals of the electronic health record (EHR) system is to empower patients to access to their own medical decisions. However, medical data is largely coming from clinical institutions so there is no way for them to control or maintain their own medical record. Patients or guardians may need to keep track of their medical data such as observed symptoms or measurements that may not be available in the EHR. Additionally, clinical decision without patient medical history can be error-prone and even be detrimental. Personal medical condition history is considered as the one of the weakest links in the current healthcare systems. For those reasons, it is necessary to have an effective and efficient personal health record system (PHRS) that allows patients or guardians to constantly monitor and control the personal health record. We propose a cloud based personal health record system that allows constant monitoring capability by supporting dynamic creation of clinical document architecture (CDA) document from a mobile device. The generated CDA document may be used to assess current health against major diseases through a clinical decision support system. We provide constant monitoring capability by using easy uploading module and decision support system. Our proposed system uses medical coding standards such as ICD-9-CM, SNOMED CT, etc. to achieve interoperability between different electronic health record systems.