It, from the perspective of cohesion, extracts three types of topic-shift markers used in this genre, namely 'change of narrators', 'change of objects being talked about', and 'temporal adverbials&...It, from the perspective of cohesion, extracts three types of topic-shift markers used in this genre, namely 'change of narrators', 'change of objects being talked about', and 'temporal adverbials' to analyze four journalistic reports from the network media and draws the conclusion that cohesion between two topic units is usually weaker than that within a specific topic fragment.展开更多
Purpose:This paper introduces an analysis framework for tracking the evolution of research topics at the selected topics level,covering a research topic’s evolution trend,evolution path and its content changes over t...Purpose:This paper introduces an analysis framework for tracking the evolution of research topics at the selected topics level,covering a research topic’s evolution trend,evolution path and its content changes over time.Design/methodology/approach:After the topics were recovered by the author-topic model,we first built the keyword-topic co-occurrence network to track the dynamics of topic trends.Then a single-mode network was constructed with each node representing a topic and edge indicating the relationship between topics.It was used to illustrate the evolution path and content changes of research topics.A case study was conducted on the digital library research in China to verify the effectiveness of the analysis framework.Findings:The experimental results show that this analysis framework can be used to track evolution of research topics at a micro level and using social network analysis method can help understand research topics’evolution paths and content changes with the passage of time.Research limitations:Using the analysis framework will produce limited results when examining unstructured data such as social media data.In addition,the effectiveness of the framework introduced in this paper needs to be verified with more research topics in information science and in more scientific fields.Practical implications:This analysis framework can help scholars and researchers map research topics’evolution process and gain insights into how a field’s topics have evolved over time.Originality/value:Tbe analysis framework used in this study can help reveal more micro evolution details.The index to measure topic association strength defined in this paper reflects both similarity and dissimilarity between topics,which belps better understand research topics’evolution paths and content changes.展开更多
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
There are many reasons that motivate people to build online communities. The purpose of this study was to identify the topics that learners discuss when they are part of a computer assisted language learning course in...There are many reasons that motivate people to build online communities. The purpose of this study was to identify the topics that learners discuss when they are part of a computer assisted language learning course in order to answer the question “What are they talking about?”. We have examined an e-community of 618 students who were learning the Modern Greek language online. We analyzed their conversation topics directly from the discussion boards of the web-based course and sorted them into the pre-defined topic categories. The results of the study showed that during the first lessons of the course the students contributed more to social discussions which were unrelated to the course material. The reason of this outcome is that the students want to introduce themselves and meet their peers. As they progressed through the course’s lessons, however, their discussion topics became more course material related. The study ends with implications of the results and future research directions.展开更多
Information networks where users join a network, publish their own content, and create links to other users are called Online Social Networks (OSNs). Nowadays, OSNs have become one of the major platforms to promote bo...Information networks where users join a network, publish their own content, and create links to other users are called Online Social Networks (OSNs). Nowadays, OSNs have become one of the major platforms to promote both new and viral applications as well as disseminate information. Social network analysis is the study of these information networks that leads to uncovering patterns of interaction among the entities. In this regard, finding influential users in OSNs is very important as they play a key role in the success above phenomena. Various approaches exist to detect influential users in OSNs, starting from simply counting the immediate neighbors to more complex machine-learning and message-passing techniques. In this paper, we review the recent existing research works that focused on identifying influential users in OSNs.展开更多
Purpose:To reveal the research hotpots and relationship among three research hot topics in b iomedicine,namely CRISPR,iPS(induced Pluripotent Stem)cell and Synthetic biology.Design/methodology/approach:We set up their...Purpose:To reveal the research hotpots and relationship among three research hot topics in b iomedicine,namely CRISPR,iPS(induced Pluripotent Stem)cell and Synthetic biology.Design/methodology/approach:We set up their keyword co-occurrence networks with using three indicators and information visualization for metric analysis.Findings:The results reveal the main research hotspots in the three topics are different,but the overlapping keywords in the three topics indicate that they are mutually integrated and interacted each other.Research limitations:All analyses use keywords,without any other forms.Practical implications:We try to find the information distribution and structure of these three hot topics for revealing their research status and interactions,and for promoting biomedical developments.Originality/value:We chose the core keywords in three research hot topics in biomedicine by using h-index.展开更多
Purpose： This paper suggests a framework to identify important patents for building potential patent portfolios based on patents owned by different assignees so as to highlight the value of individual patents in tech...Purpose： This paper suggests a framework to identify important patents for building potential patent portfolios based on patents owned by different assignees so as to highlight the value of individual patents in technology transfer and identify potential collaborators for patent assignees. Design/methodology/approach： The analysis framework includes the following steps： l） co-classification analysis based on the International Patent Classification （IPC） codes and Derwent Manual Codes （DMC） to detect sub-tech fields, 2） keyword co-occurrence analysis aiming to understand the core technology information in each patent, and 3） social network analysis used for identifying important technologies and partnerships of key assignees. A case study was conducted with 27,401 chemistry patents filed by a Chinese national research institute. Findings： The results show that this framework is effective in building potential technological patent portfolios based on patents owned by different assignees and identifying future collaborators for the assignees. This integrated approach based on topic identification and correlation analysis that combines network-based analysis with keyword-based analysis can reveal important patented technologies and their connections and help understand detailed technological information mentioned in patents. Research limitations： In keywords analysis, only titles and abstracts of patent documents were used and weights of keywords in different parts of the documents were not considered.Practical implications： The analysis framework provides valuable information for decision- makers of large institutions which have many patents with broad application prospects. Originality/value： Different from previous patent portfolio studies based on the use of a combination of patent analysis indicators, this study provides insights into a method of building patent portfolios to discover the potential of individual patents in technology transfer and promote cooperation among different patent assignees.展开更多
高价值专利识别是抢占产业全球战略高地、推动产业持续高效健康发展的重要课题,可为产业关键核心技术的挖掘提供重要线索。本文从专利技术距离测度视角出发,在进行主题聚类提取领域上位类主题基础上,提出一种基于主题知识贡献距离与主...高价值专利识别是抢占产业全球战略高地、推动产业持续高效健康发展的重要课题,可为产业关键核心技术的挖掘提供重要线索。本文从专利技术距离测度视角出发,在进行主题聚类提取领域上位类主题基础上,提出一种基于主题知识贡献距离与主题联系程度双维影响下的高价值专利识别方法。在主题知识贡献距离维度上,构建专利间分层专利引用网络,计算各专利与主题的持续知识贡献值,基于知识贡献时间序列计算主题间的动态时间规整(dynamic time warping,DTW)距离,形成主题知识贡献距离矩阵;在主题联系程度维度上,构建主题与专利二分图网络,结合专利共现频率与引用关系强度进行初始强度与全局逻辑计算,形成主题联系程度矩阵。融合双维度矩阵构建专利技术距离矩阵,基于技术距离矩阵进行各专利的绝对技术距离计算,选取阈值范围内的高绝对技术距离专利作为领域内高技术价值的专利。经验证数据集检验,本文方法的精准率达到0.8218,F1指标达到0.8014。基于此,对“生成式人工智能”领域专利进行实证,识别出产业内具有较高价值的专利1437件,并发现识别出的高价值专利集具有较高的转让比例,转让比例达58.59%。本文基于技术本质的视角对专利间的技术差距进行量化,打破了以往仅从外部特征或简单统计数据判断专利价值的局限性,提升了识别的准确性;同时,提出双维度的技术距离影响机理,进一步提升了识别的可解释性。展开更多
文摘It, from the perspective of cohesion, extracts three types of topic-shift markers used in this genre, namely 'change of narrators', 'change of objects being talked about', and 'temporal adverbials' to analyze four journalistic reports from the network media and draws the conclusion that cohesion between two topic units is usually weaker than that within a specific topic fragment.
文摘Purpose:This paper introduces an analysis framework for tracking the evolution of research topics at the selected topics level,covering a research topic’s evolution trend,evolution path and its content changes over time.Design/methodology/approach:After the topics were recovered by the author-topic model,we first built the keyword-topic co-occurrence network to track the dynamics of topic trends.Then a single-mode network was constructed with each node representing a topic and edge indicating the relationship between topics.It was used to illustrate the evolution path and content changes of research topics.A case study was conducted on the digital library research in China to verify the effectiveness of the analysis framework.Findings:The experimental results show that this analysis framework can be used to track evolution of research topics at a micro level and using social network analysis method can help understand research topics’evolution paths and content changes with the passage of time.Research limitations:Using the analysis framework will produce limited results when examining unstructured data such as social media data.In addition,the effectiveness of the framework introduced in this paper needs to be verified with more research topics in information science and in more scientific fields.Practical implications:This analysis framework can help scholars and researchers map research topics’evolution process and gain insights into how a field’s topics have evolved over time.Originality/value:Tbe analysis framework used in this study can help reveal more micro evolution details.The index to measure topic association strength defined in this paper reflects both similarity and dissimilarity between topics,which belps better understand research topics’evolution paths and content changes.
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There are many reasons that motivate people to build online communities. The purpose of this study was to identify the topics that learners discuss when they are part of a computer assisted language learning course in order to answer the question “What are they talking about?”. We have examined an e-community of 618 students who were learning the Modern Greek language online. We analyzed their conversation topics directly from the discussion boards of the web-based course and sorted them into the pre-defined topic categories. The results of the study showed that during the first lessons of the course the students contributed more to social discussions which were unrelated to the course material. The reason of this outcome is that the students want to introduce themselves and meet their peers. As they progressed through the course’s lessons, however, their discussion topics became more course material related. The study ends with implications of the results and future research directions.
文摘Information networks where users join a network, publish their own content, and create links to other users are called Online Social Networks (OSNs). Nowadays, OSNs have become one of the major platforms to promote both new and viral applications as well as disseminate information. Social network analysis is the study of these information networks that leads to uncovering patterns of interaction among the entities. In this regard, finding influential users in OSNs is very important as they play a key role in the success above phenomena. Various approaches exist to detect influential users in OSNs, starting from simply counting the immediate neighbors to more complex machine-learning and message-passing techniques. In this paper, we review the recent existing research works that focused on identifying influential users in OSNs.
基金the National Natural Science Foundation of China Grant 71673131 for financial support
文摘Purpose:To reveal the research hotpots and relationship among three research hot topics in b iomedicine,namely CRISPR,iPS(induced Pluripotent Stem)cell and Synthetic biology.Design/methodology/approach:We set up their keyword co-occurrence networks with using three indicators and information visualization for metric analysis.Findings:The results reveal the main research hotspots in the three topics are different,but the overlapping keywords in the three topics indicate that they are mutually integrated and interacted each other.Research limitations:All analyses use keywords,without any other forms.Practical implications:We try to find the information distribution and structure of these three hot topics for revealing their research status and interactions,and for promoting biomedical developments.Originality/value:We chose the core keywords in three research hot topics in biomedicine by using h-index.
基金supported by the Science and Technology Service Network Initiative of Chinese Academy of Sciences(Grant No.:KFJ-EW-STS-032)the West Light Foundation of Chinese Academy of Sciences(Grant No.:Y4C0091001)the National Social Science Foundation of China(Grant No.:14CTQ033)
文摘Purpose： This paper suggests a framework to identify important patents for building potential patent portfolios based on patents owned by different assignees so as to highlight the value of individual patents in technology transfer and identify potential collaborators for patent assignees. Design/methodology/approach： The analysis framework includes the following steps： l） co-classification analysis based on the International Patent Classification （IPC） codes and Derwent Manual Codes （DMC） to detect sub-tech fields, 2） keyword co-occurrence analysis aiming to understand the core technology information in each patent, and 3） social network analysis used for identifying important technologies and partnerships of key assignees. A case study was conducted with 27,401 chemistry patents filed by a Chinese national research institute. Findings： The results show that this framework is effective in building potential technological patent portfolios based on patents owned by different assignees and identifying future collaborators for the assignees. This integrated approach based on topic identification and correlation analysis that combines network-based analysis with keyword-based analysis can reveal important patented technologies and their connections and help understand detailed technological information mentioned in patents. Research limitations： In keywords analysis, only titles and abstracts of patent documents were used and weights of keywords in different parts of the documents were not considered.Practical implications： The analysis framework provides valuable information for decision- makers of large institutions which have many patents with broad application prospects. Originality/value： Different from previous patent portfolio studies based on the use of a combination of patent analysis indicators, this study provides insights into a method of building patent portfolios to discover the potential of individual patents in technology transfer and promote cooperation among different patent assignees.
文摘高价值专利识别是抢占产业全球战略高地、推动产业持续高效健康发展的重要课题,可为产业关键核心技术的挖掘提供重要线索。本文从专利技术距离测度视角出发,在进行主题聚类提取领域上位类主题基础上,提出一种基于主题知识贡献距离与主题联系程度双维影响下的高价值专利识别方法。在主题知识贡献距离维度上,构建专利间分层专利引用网络,计算各专利与主题的持续知识贡献值,基于知识贡献时间序列计算主题间的动态时间规整(dynamic time warping,DTW)距离,形成主题知识贡献距离矩阵;在主题联系程度维度上,构建主题与专利二分图网络,结合专利共现频率与引用关系强度进行初始强度与全局逻辑计算,形成主题联系程度矩阵。融合双维度矩阵构建专利技术距离矩阵,基于技术距离矩阵进行各专利的绝对技术距离计算,选取阈值范围内的高绝对技术距离专利作为领域内高技术价值的专利。经验证数据集检验,本文方法的精准率达到0.8218,F1指标达到0.8014。基于此,对“生成式人工智能”领域专利进行实证,识别出产业内具有较高价值的专利1437件,并发现识别出的高价值专利集具有较高的转让比例,转让比例达58.59%。本文基于技术本质的视角对专利间的技术差距进行量化,打破了以往仅从外部特征或简单统计数据判断专利价值的局限性,提升了识别的准确性;同时,提出双维度的技术距离影响机理,进一步提升了识别的可解释性。
