Purpose: Based on the weak tie theory, this paper proposes a series of connection indicators Acof weak tie subnets and weak tie nodes to detect research topics, recognize their connections, and understand their evolut...Purpose: Based on the weak tie theory, this paper proposes a series of connection indicators Acof weak tie subnets and weak tie nodes to detect research topics, recognize their connections, and understand their evolution.Design/methodology/approach: First, keywords are extracted from article titles and preprocessed. Second, high-frequency keywords are selected to generate weak tie co-occurrence networks. By removing the internal lines of clustered sub-topic networks, we focus on the analysis of weak tie subnets’ composition and functions and the weak tie nodes’ roles.Findings: The research topics’ clusters and themes changed yearly; the subnets clustered with technique-related and methodology-related topics have been the core, important subnets for years; while close subnets are highly independent, research topics are generally concentrated and most topics are application-related; the roles and functions of nodes and weak ties are diversified.Research limitations: The parameter values are somewhat inconsistent; the weak tie subnets and nodes are classified based on empirical observations, and the conclusions are not verified or compared to other methods.Practical implications: The research is valuable for detecting important research topics as well as their roles, interrelations, and evolution trends. Originality/value: To contribute to the strength of weak tie theory, the research translates weak and strong ties concepts to co-occurrence strength, and analyzes weak ties’ functions. Also, the research proposes a quantitative method to classify and measure the topics’ clusters and nodes.展开更多
文章关注公众科学分类标注项目,运用系统综述方法,在Web of Science核心合集中筛选出45篇文献作为证据来源,并根据EBLIP证据等级模型为证据进行评级,进而提出公众科学分类标注项目设计建议,即合理设置任务粒度,平衡数据质量与参与难度;...文章关注公众科学分类标注项目,运用系统综述方法,在Web of Science核心合集中筛选出45篇文献作为证据来源,并根据EBLIP证据等级模型为证据进行评级,进而提出公众科学分类标注项目设计建议,即合理设置任务粒度,平衡数据质量与参与难度;适当设置“不知道”选项,平衡数据质量与分类效率;加入游戏化元素,提升参与过程的趣味性;广泛招募公众,重视但不能依赖“超级参与者”;设置物质和精神激励,吸引公众加入并持续参与;采用通俗语言而非专业术语,关注非正式沟通渠道;提供教程指导,发挥科普作用;设置分类标注次数阈值,平衡数据质量和项目进度;以算法汇总参与者共识,获得最终分类结果。展开更多
基金funded by the National Social Science Youth Project “Study on the Interdisciplinary Subject Identification and Prediction” (Grant No.:14CTQ033)
文摘Purpose: Based on the weak tie theory, this paper proposes a series of connection indicators Acof weak tie subnets and weak tie nodes to detect research topics, recognize their connections, and understand their evolution.Design/methodology/approach: First, keywords are extracted from article titles and preprocessed. Second, high-frequency keywords are selected to generate weak tie co-occurrence networks. By removing the internal lines of clustered sub-topic networks, we focus on the analysis of weak tie subnets’ composition and functions and the weak tie nodes’ roles.Findings: The research topics’ clusters and themes changed yearly; the subnets clustered with technique-related and methodology-related topics have been the core, important subnets for years; while close subnets are highly independent, research topics are generally concentrated and most topics are application-related; the roles and functions of nodes and weak ties are diversified.Research limitations: The parameter values are somewhat inconsistent; the weak tie subnets and nodes are classified based on empirical observations, and the conclusions are not verified or compared to other methods.Practical implications: The research is valuable for detecting important research topics as well as their roles, interrelations, and evolution trends. Originality/value: To contribute to the strength of weak tie theory, the research translates weak and strong ties concepts to co-occurrence strength, and analyzes weak ties’ functions. Also, the research proposes a quantitative method to classify and measure the topics’ clusters and nodes.
文摘文章关注公众科学分类标注项目,运用系统综述方法,在Web of Science核心合集中筛选出45篇文献作为证据来源,并根据EBLIP证据等级模型为证据进行评级,进而提出公众科学分类标注项目设计建议,即合理设置任务粒度,平衡数据质量与参与难度;适当设置“不知道”选项,平衡数据质量与分类效率;加入游戏化元素,提升参与过程的趣味性;广泛招募公众,重视但不能依赖“超级参与者”;设置物质和精神激励,吸引公众加入并持续参与;采用通俗语言而非专业术语,关注非正式沟通渠道;提供教程指导,发挥科普作用;设置分类标注次数阈值,平衡数据质量和项目进度;以算法汇总参与者共识,获得最终分类结果。
