Aspect Sentiment Quad Prediction (ASQP) is the most complex subtask of Aspect-based Sentiment Analysis (ABSA), aiming to predict all sentiment quadruples within the given sentence. Due to the complexity of sentence syntaxes and the diversity of sentiment expressions, generative methods gradually become the mainstream approach in ASQP. However, existing generative models are constrained in the effectiveness of demonstrations. Semantically similar demonstrations help in judging sentiment categories and polarities but may confuse the model in recognizing aspect and opinion terms, which are more related to sentence syntaxes. To this end, we first develop Syn2Vec, a method for calculating syntactic vectors to support the retrieval of syntactically similar demonstrations. Then, we propose Syntactic and Semantic Similarity Retrieval Prompting (SimRP) to construct effective prompts by retrieving the most related demonstrations that are syntactically and semantically similar. With these related demonstrations, pre-trained generative models, especially Large Language Models (LLMs), can fully release their potential to recognize sentiment quadruples. Extensive experiments in Supervised Fine-Tuning (SFT) and In-context Learning (ICL) paradigms demonstrate the effectiveness of SimRP. Furthermore, we find that LLMs' capabilities in ASQP are severely underestimated by biased data annotations and the exact matching metric. We propose a novel constituent subtree-based fuzzy metric for more accurate and rational quadruple recognition.

This study uses Twitter data from the early stages of the pandemic to analyze the evolution of topics during different time periods and attempts to investigate the content and emotional impact of opinion leaders on public opinion evolution under different themes, in order to understand their role in shaping public discourse and emotions. Divide the life cycle into three stages; NLTK emotional analysis and dynamic topic models (DTMs) are employed to extract and analyze topic words. The results showed that there were significant differences between opinion leaders and followers in terms of hot topics and their evolution trends: (1) In terms of hot topics, opinion leaders have always been paying attention to measures and methods aimed at the public, while followers usually have persist in seeking information and dissatisfaction. (2) In terms of identifying and evolving hot topics, opinion leaders have shifted from the impact of the epidemic on individuals and resources to government responses and policies, while followers are more inclined to express people's growing concerns and dissatisfaction with crisis management. The content of opinion leaders has a significant relationship with evolving public opinion, highlighting the importance of understanding their role in crisis communication. Opinion leaders are also categorized into five types, each with different audience sizes, contents, emotions, and network structures, and they impact public opinion differently. This study identifies and analyzes the characteristics and impact mechanisms of opinion leaders in crisis communication. It hopes to contribute to understanding crisis communication dynamics in the digital era and provide insights into effective communication strategies during crises.

Background: Despite studies investigating the publication rates and factors influencing publication outcomes of clinical trials in some disease fields, there is a notable lack of research focusing on chronic obstructive pulmonary disease (COPD) clinical trials. This study aims to explore the characteristics of COPD-related clinical trials and identify factors associated with publication status and publication time. Methods: A systematic search was conducted on the World Health Organization International Clinical Trials Registry Platform on April 28, 2022, to identify completed interventional clinical trials related to COPD. Various trial features were analyzed, and factors influencing publication status and time were examined. Results: A total of 2577 completed interventional clinical trials focusing on COPD were identified. A total of 42.76% of trials enrolled <= 50 participants. The majority of trials were randomized (81.72%), blind (57.39%), parallel-assignment (59.14%), single-center (51.30%), multi-arm (83.86%), nonindustry funded (52.00%), and conducted for therapeutic purposes (73.11%). The 2-year cumulative publication rate was found to be 27.9%. The median time of study duration, dissemination lag, and publication lag were 17.27, 21.07, and 24.70 months, respectively. Multivariate analysis revealed that sample size, blind design, and study phase significantly influenced the likelihood of publication, while intervention model, primary purpose, study phase, funder, and study duration were significant factors affecting publication time. Conclusion: The findings highlight the inadequacy of large multi-center interventional clinical trials for COPD and indicate a low 2-year cumulative publication rate. Strengthening collaboration among investigators and adopting scientifically robust designs for larger phase 3 clinical trials are crucial to advancing COPD research and enhancing publication outcomes.

Purpose: To reveal the typical features of text duplication in papers from four medical fields: basic medicine, health management, pharmacology and pharmacy, and public health and preventive medicine. To analyze the reasons for duplication and provide suggestions for the management of medical academic misconduct. Design/methodology/approach: In total, 2,469 representative Chinese journal papers were included in our research, which were submitted by researchers in 2020 and 2021. A plagiarism check was carried out using the Academic Misconduct Literature Check System (AMLC). We generated a corrected similarity index based on the AMLC general similarity index for further analysis. We compared the similarity indices of papers in four medical fields and revealed their trends over time; differences in similarity index between review and research articles were also analyzed according to the different fields. Further analysis of 143 papers suspected of plagiarism was also performed from the perspective of sections containing duplication and according to the field of research. Findings: Papers in the field of pharmacology and pharmacy had the highest similarity index (8.67 ± 5.92%), which was significantly higher than that in other fields, except health management. The similarity index of review articles (9.77 ± 10.28%) was significantly higher than that of research articles (7.41 ± 6.26%). In total, 143 papers were suspected of plagiarism (5.80%) with similarity indices ≥ 15%; most were papers on health management (78, 54.55%), followed by public health and preventive medicine (38, 26.58%); 90.21% of the 143 papers had duplication in multiple sections, while only 9.79% had duplication in a single section. The distribution of sections with duplication varied among different fields; papers in pharmacology and pharmacy were more likely to have duplication in the data/methods and introduction/background sections, however, papers in health management were more likely to contain duplication in the introduction/background or results/discussion sections. Different structures for papers in different fields may have caused these differences. Research limitations: There were three limitations to our research. Firstly, we observed that a small number of papers have been checked early. It is unknown who conducted the plagiarism check as this can be included in other evaluations, such as applications for Science and technology projects or awards. If the authors carried out the check, text with high similarity indices may have been excluded before submission, meaning the similarity index in our research may have been lower than the original value. Secondly, there were only four medical fields included in our research. Additional analysis on a wider scale is required in the future. Thirdly, only a general similarity index was calculated in our study; other similarity indices were not tested. Practical implications: A comprehensive analysis of similarity indices in four medical fields was performed. We made several recommendations for the supervision of medical academic misconduct and the formation of criteria for defining suspected plagiarism for medical papers, as well as for the improved accuracy of text duplication checks. Originality/value: We quantified the differences between the AMLC general similarity index and the corrected index, described the situation around text duplication and plagiarism in papers from four medical fields, and revealed differences in similarity indices between different article types. We also revealed differences in the sections containing duplication for papers with suspected plagiarism among different fields.