With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or p...With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.展开更多
陆地碳汇是碳循环的重要组成部分,在全球气候变化背景下其重要性日益凸显,相关研究受到了国内外学术界的广泛关注。采用文献计量方法,以1994—2024年间Web of Science核心合集SCI-E数据库以及CNKI数据库中所收录的共计8431篇相关文献为...陆地碳汇是碳循环的重要组成部分,在全球气候变化背景下其重要性日益凸显,相关研究受到了国内外学术界的广泛关注。采用文献计量方法,以1994—2024年间Web of Science核心合集SCI-E数据库以及CNKI数据库中所收录的共计8431篇相关文献为研究对象,运用CiteSpace软件绘制国内外文献共被引、作者共作以及关键词时间线等可视化图谱,分析了论文时间、学科、期刊以及来源国家的分布情况,给出了高影响机构、高产作者以及重要研究文献,并基于Burst检测探究了不同阶段关键词演化发展过程及未来趋势。结果表明:①近30 a来陆地碳汇发文量显著增长,2008年以后年均增幅12%,2019年以后年均增幅高达15%。②发文量较多的国家依次是中国、美国、德国、英国、加拿大等;高影响的研究机构主要有中国科学院、中国科学院大学、法国国家科学研究中心、美国农业部、巴黎-萨克雷大学等。③关键词演化过程主要分为3个阶段:1994—2008年侧重于碳循环基础理论研究,关键热词是碳循环、碳平衡和涡度相关等;2008—2019年研究热点从地球生态系统逐渐扩展到社会经济等方面,关键热词是净初级生产量、碳交换、生态补偿和低碳经济等;2019年至今紧密围绕全球碳减排目标与生态系统价值实现,关键热词是以碳中和、碳排放、温度敏感性、生态产品核算和碳交易;未来发展方向是碳汇监测核算、减排增汇提升方法、碳交易市场机制、深化国际合作等。研究成果可为厘清全球陆地碳汇发展脉络和研究热点、预测未来发展方向,以及促进我国双碳目标实现提供基础资料和政策建议。展开更多
基金funded by the Hunan Provincial Natural Science Foundation of China(Grant No.2025JJ70105)the Hunan Provincial College Students’Innovation and Entrepreneurship Training Program(Project No.S202411342056)The article processing charge(APC)was funded by the Project No.2025JJ70105.
文摘With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.
文摘陆地碳汇是碳循环的重要组成部分,在全球气候变化背景下其重要性日益凸显,相关研究受到了国内外学术界的广泛关注。采用文献计量方法,以1994—2024年间Web of Science核心合集SCI-E数据库以及CNKI数据库中所收录的共计8431篇相关文献为研究对象,运用CiteSpace软件绘制国内外文献共被引、作者共作以及关键词时间线等可视化图谱,分析了论文时间、学科、期刊以及来源国家的分布情况,给出了高影响机构、高产作者以及重要研究文献,并基于Burst检测探究了不同阶段关键词演化发展过程及未来趋势。结果表明:①近30 a来陆地碳汇发文量显著增长,2008年以后年均增幅12%,2019年以后年均增幅高达15%。②发文量较多的国家依次是中国、美国、德国、英国、加拿大等;高影响的研究机构主要有中国科学院、中国科学院大学、法国国家科学研究中心、美国农业部、巴黎-萨克雷大学等。③关键词演化过程主要分为3个阶段:1994—2008年侧重于碳循环基础理论研究,关键热词是碳循环、碳平衡和涡度相关等;2008—2019年研究热点从地球生态系统逐渐扩展到社会经济等方面,关键热词是净初级生产量、碳交换、生态补偿和低碳经济等;2019年至今紧密围绕全球碳减排目标与生态系统价值实现,关键热词是以碳中和、碳排放、温度敏感性、生态产品核算和碳交易;未来发展方向是碳汇监测核算、减排增汇提升方法、碳交易市场机制、深化国际合作等。研究成果可为厘清全球陆地碳汇发展脉络和研究热点、预测未来发展方向,以及促进我国双碳目标实现提供基础资料和政策建议。
