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.
In modern software development, efficient program comprehension is essential for maintaining and evolving software systems. Developers often dedicate over $50 \%$ of their time to understanding code due to the complexity and time demands involved. Code summarization-generating concise natural language descriptions of source code-has emerged as a potential solution. However, existing automated summarization techniques frequently produce summaries that are incomplete or lack accuracy. Moreover, as software evolves, documentation often becomes outdated, leading to discrepancies between code and comments. To address these challenges, we present DeepKnowCode, an automated approach that utilizes a DEEP learning technique based on a large language model and crowdsourced KNOWledge for CODE summarization. This approach aids developers by generating summaries that elucidate (1) the internal behavior of the code, (2) the rationale behind its implementation, and (3) practical guidelines for its use. We implemented a research prototype to assess real-world applicability and rigorously evaluated DeepKnowCode against state-of-the-art approaches. In our evaluation, DeepKnowCode demonstrates performance improvements in BLEU scores of 38.8% and 39.2% over two baselines, with statistical validation underscoring its effectiveness. By incorporating crowdsourced knowledge, DeepKnowCode captures essential elements of code semantics, context, and patterns, enhancing its ability to produce accurate and contextually relevant code summaries that facilitate program comprehension.
This study addresses the challenge of hate speech detection in Tunisian Arabic text, a significant concern for ensuring online safety and effective moderation. Utilizing the capabilities of the AraBERT model, we fine-tuned and evaluated its performance against the Bi-LSTM model across four distinct datasets: T-HSAB, TNHS, TUNIZI-Dataset, and a newly compiled dataset with diverse labels such as Offensive Language, Racism, and Religious Intolerance. Our experimental results demonstrate that AraBERT significantly outperforms Bi-LSTM in terms of Recall, Precision, F1-Score, and Accuracy across all datasets. The findings underline the robustness of AraBERT in capturing the nuanced features of Tunisian Arabic and its superior capability in classification tasks. This research not only advances the technology for hate speech detection but also provides practical implications for social media moderation and policy-making in Tunisia. Future work will focus on expanding the datasets and exploring more sophisticated architectures to further enhance detection accuracy, thus promoting safer online interactions.
The quality of services in early childhood education institutions is closely linked to the educational competence of early childhood teachers. This competence should be emphasized not only in their teaching duties but also in their creative role performance as educators. Unlike formal academic education, early childhood education operates with a child-centered, play-based, and integrated approach, without standardized guidelines. Due to the unstructured nature of the curriculum, early childhood teachers must demonstrate creative role performance by restructuring the curriculum to provide education that considers the interests, performance, and developmental levels of individual children, as well as making decisions that align with the contextual circumstances of the classroom. Given these demands, early childhood teachers require significant effort and expertise to fulfill their roles effectively. At this juncture, research is necessary to improve the qualitative level of creative role performance among early childhood teachers. This study identifies job autonomy and teacher efficacy as determinants of job crafting among early childhood teachers and aims to examine the structural relationships between these variables. The findings are expected to provide fundamental insights for enhancing the creative role performance of teachers in early childhood education settings.
In recent years, with the innovation of Internet technology and the deep popularization of mobile Internet, it has become extremely convenient for people to obtain, publish and disseminate information. At the same time, the number of users of social networking platforms has shown a rapid growth, covering the world. People’s dependence on social platforms has deepened, and many people even use it as the preferred channel for obtaining information. However, the drawbacks of the Internet and social platforms being flooded with a large amount of false news are gradually becoming prominent, which not only damages the network ecology, but also causes a series of social problems. In view of the fact that it is very difficult for ordinary people to accurately distinguish the massive information on the Internet and are easily misled and deceived, people in related industries and research circles have called for the establishment of an automatic detection framework based on new artificial intelligence technologies to enable the governance of online false news. This study will be carried out by integrating knowledge into the study of false news mitigation. Knowledge fusion refers to the knowledge that integrates more information content within a unit time and density. Knowledge fusion includes but is not limited to language knowledge, world knowledge, domain knowledge, etc. False news mitigation refers to further improving the ability to mitigate false news through factual error correction, two - way factual error correction, and nonfactual content confrontation. Therefore, the focus of this study is to introduce knowledge fusion, improve the ability to mitigate false news through false news detection, factual error correction, and non-factual content confrontation.
