Purpose “Library + cultural tourism” (LCT) is a new direction for the sustainable development of libraries, but few scholars have researched it from a tourist perspective. The purpose of this paper is to identify what factors affect tourist satisfaction (TS) with LCT, reveal the interaction among these factors and provide strategies for better library services. Design/methodology/approach This study collected 5,308 comments on Tianjin Binhai Library from three popular online travel communities. Grounded theory was adopted to identify what factors affect TS with LCT and reveal the interaction among these factors. Findings The results indicated that TS with LCT was affected by complex factors related to tour plans, tour expectations, cultural characteristics, environment, support services and emotions. Cultural characteristics, environment and support services were impacted by tour plans and tour expectations, which directly or indirectly affected TS mediated by emotions. TS further influenced their expectation of their next tour. Originality/value This paper uncovered critical problems with LCT using a case study of Tianjin Binhai Library. The results provide a reference for library practitioners to develop better library services for tourists and regular users/readers.
{"title":"Towards better library services: an investigation of factors affecting tourists' satisfaction with \"library + cultural tourism\"","authors":"Y. Pan, Lia H. Sun, H. Yang, Jianming Zheng","doi":"10.1108/el-03-2021-0070","DOIUrl":"https://doi.org/10.1108/el-03-2021-0070","url":null,"abstract":"\u0000Purpose\u0000“Library + cultural tourism” (LCT) is a new direction for the sustainable development of libraries, but few scholars have researched it from a tourist perspective. The purpose of this paper is to identify what factors affect tourist satisfaction (TS) with LCT, reveal the interaction among these factors and provide strategies for better library services.\u0000\u0000\u0000Design/methodology/approach\u0000This study collected 5,308 comments on Tianjin Binhai Library from three popular online travel communities. Grounded theory was adopted to identify what factors affect TS with LCT and reveal the interaction among these factors.\u0000\u0000\u0000Findings\u0000The results indicated that TS with LCT was affected by complex factors related to tour plans, tour expectations, cultural characteristics, environment, support services and emotions. Cultural characteristics, environment and support services were impacted by tour plans and tour expectations, which directly or indirectly affected TS mediated by emotions. TS further influenced their expectation of their next tour.\u0000\u0000\u0000Originality/value\u0000This paper uncovered critical problems with LCT using a case study of Tianjin Binhai Library. The results provide a reference for library practitioners to develop better library services for tourists and regular users/readers.\u0000","PeriodicalId":330882,"journal":{"name":"Electron. Libr.","volume":"223 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115490516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose This paper aims to study the factors influencing online social network (OSN) information diffusion under different themes helps to understand information diffusion in general. Design/methodology/approach This study collects data from the Web of Science, use the strategic consulting intelligent support system for word frequency analysis and use keyword clustering to classify themes, then research information themes as influencing factors of OSN information diffusion. Findings Five themes of “natural disaster”, “political event”, “product marketing”, “sport and entertainment” and “health-disease” have been identified. It is found that the research objects, research methods and research theories used by scholars under different themes have different focuses, and the factors affecting information diffusion are different. Research limitations/implications The limitation of this paper is that it only focuses on five typical themes, and there may be more themes. Practical implications The research helps other scholars to conduct in-depth research on the diffusion of OSN information under different topics and focus on the content of the research on OSN information diffusion under different topics. Social implications The research helps other scholars to conduct in-depth research on the diffusion of social network information under different topics, so as to better understand and predict the law of information diffusion. Originality/value The research summarizes the research on information diffusion in OSNs from the theme level and analyses the key points and theories and further enriches the research system on information diffusion in OSNs.
目的研究不同主题下网络社交网络(online social network, OSN)信息扩散的影响因素，有助于从总体上理解信息扩散。设计/方法/方法本研究从Web of Science收集数据，利用战略咨询智能支持系统进行词频分析，利用关键词聚类对主题进行分类，研究信息主题作为OSN信息扩散的影响因素。调查结果确定了五个主题:“自然灾害”、“政治事件”、“产品营销”、“体育和娱乐”以及“健康-疾病”。研究发现，不同主题下学者的研究对象、研究方法和研究理论的侧重点不同，影响信息扩散的因素也不同。研究局限性/启示本文的局限性在于只关注了五个典型的主题，可能还会有更多的主题。实践意义本研究有助于其他学者对不同主题下的OSN信息扩散进行深入研究，关注不同主题下OSN信息扩散研究的内容。社会意义本研究有助于其他学者对不同主题下的社会网络信息扩散进行深入研究，从而更好地理解和预测信息扩散规律。本研究从主题层面对网络环境信息扩散的研究进行了总结，分析了研究的重点和理论，进一步丰富了网络环境信息扩散的研究体系。
{"title":"Research on influencing factors of information diffusion in online social networks under different themes","authors":"Ling Zhang, D. Li, Robert J. Boncella","doi":"10.1108/el-12-2020-0329","DOIUrl":"https://doi.org/10.1108/el-12-2020-0329","url":null,"abstract":"\u0000Purpose\u0000This paper aims to study the factors influencing online social network (OSN) information diffusion under different themes helps to understand information diffusion in general.\u0000\u0000\u0000Design/methodology/approach\u0000This study collects data from the Web of Science, use the strategic consulting intelligent support system for word frequency analysis and use keyword clustering to classify themes, then research information themes as influencing factors of OSN information diffusion.\u0000\u0000\u0000Findings\u0000Five themes of “natural disaster”, “political event”, “product marketing”, “sport and entertainment” and “health-disease” have been identified. It is found that the research objects, research methods and research theories used by scholars under different themes have different focuses, and the factors affecting information diffusion are different.\u0000\u0000\u0000Research limitations/implications\u0000The limitation of this paper is that it only focuses on five typical themes, and there may be more themes.\u0000\u0000\u0000Practical implications\u0000The research helps other scholars to conduct in-depth research on the diffusion of OSN information under different topics and focus on the content of the research on OSN information diffusion under different topics.\u0000\u0000\u0000Social implications\u0000The research helps other scholars to conduct in-depth research on the diffusion of social network information under different topics, so as to better understand and predict the law of information diffusion.\u0000\u0000\u0000Originality/value\u0000The research summarizes the research on information diffusion in OSNs from the theme level and analyses the key points and theories and further enriches the research system on information diffusion in OSNs.\u0000","PeriodicalId":330882,"journal":{"name":"Electron. Libr.","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125738951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sayed Mahmood Bakhshayesh, A. Ahmadi, Azadeh Mohebi
Purpose Many search engines in digital libraries are restricted to the terms presented in users’ queries. When users cannot represent their information needs in terms of keywords in a query, the search engine fails to provide appropriate results. In addition, most search engines do not have the ability to visualize search results for users to help them in their information journey. The purpose of this paper is to develop a new approach for search result visualization in digital libraries. The visualization approach enables subject-based visualization of search results and search queries. Design/methodology/approach To enable subject-based visualization of search results in digital libraries, new subject-based document retrieval is proposed in which each document is represented as a vector of subjects as well. Then, using a vector space model for information retrieval, along with the subject-based vector, related documents to the user’s query are retrieved, whilst each document is visualized through a ring chart, showing the inherent subjects within each document and the query. Findings The proposed subject-based retrieval and visualization approach is evaluated from various perspectives to amplify the impact of the visualization approach from users’ opinions. Users have evaluated the performance of the proposed subject-based retrieval and search result visualization, whilst 67% of users prefer subject-based document retrieval and 80% of them believe that the proposed visualization approach is practical. Research limitations/implications This research has provided a subject-based representation scheme for search result visualization in a digital library. The implication of this research can be viewed from two perspectives. First, the subject-based retrieval approach provides an opportunity for the users to understand their information needs, beyond the explicit terms in the query, leading to results, which are semantically relevant to the query. Second, the simple subject-based visualization scheme, helps users to explore the results easily, whilst allowing them to build their knowledge experience. Originality/value A new vectorized subject-based representation of documents and queries is proposed. This representation determines the semantic and subject-based relationship between a given query and documents within a digital scientific library. In addition, it also provides a subject-based representation of the retrieved documents through which users can track the semantic relationship between the query and retrieve documents, visually.
{"title":"A new subject-based retrieval and search result visualization approach for scientific digital libraries","authors":"Sayed Mahmood Bakhshayesh, A. Ahmadi, Azadeh Mohebi","doi":"10.1108/el-08-2020-0243","DOIUrl":"https://doi.org/10.1108/el-08-2020-0243","url":null,"abstract":"\u0000Purpose\u0000Many search engines in digital libraries are restricted to the terms presented in users’ queries. When users cannot represent their information needs in terms of keywords in a query, the search engine fails to provide appropriate results. In addition, most search engines do not have the ability to visualize search results for users to help them in their information journey. The purpose of this paper is to develop a new approach for search result visualization in digital libraries. The visualization approach enables subject-based visualization of search results and search queries.\u0000\u0000\u0000Design/methodology/approach\u0000To enable subject-based visualization of search results in digital libraries, new subject-based document retrieval is proposed in which each document is represented as a vector of subjects as well. Then, using a vector space model for information retrieval, along with the subject-based vector, related documents to the user’s query are retrieved, whilst each document is visualized through a ring chart, showing the inherent subjects within each document and the query.\u0000\u0000\u0000Findings\u0000The proposed subject-based retrieval and visualization approach is evaluated from various perspectives to amplify the impact of the visualization approach from users’ opinions. Users have evaluated the performance of the proposed subject-based retrieval and search result visualization, whilst 67% of users prefer subject-based document retrieval and 80% of them believe that the proposed visualization approach is practical.\u0000\u0000\u0000Research limitations/implications\u0000This research has provided a subject-based representation scheme for search result visualization in a digital library. The implication of this research can be viewed from two perspectives. First, the subject-based retrieval approach provides an opportunity for the users to understand their information needs, beyond the explicit terms in the query, leading to results, which are semantically relevant to the query. Second, the simple subject-based visualization scheme, helps users to explore the results easily, whilst allowing them to build their knowledge experience.\u0000\u0000\u0000Originality/value\u0000A new vectorized subject-based representation of documents and queries is proposed. This representation determines the semantic and subject-based relationship between a given query and documents within a digital scientific library. In addition, it also provides a subject-based representation of the retrieved documents through which users can track the semantic relationship between the query and retrieve documents, visually.\u0000","PeriodicalId":330882,"journal":{"name":"Electron. Libr.","volume":"227 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124508109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose This study furthers metadata quality research by providing complementary network-based metrics and insights to analyze metadata records and identify areas for improvement. Design/methodology/approach Metadata record graphs apply network analysis to metadata field values; this study evaluates the interconnectedness of subjects within each Hub aggregated into the Digital Public Library of America. It also reviews the effects of NACO normalization – simulating revision of values for consistency – and breaking up pre-coordinated subject headings – to simulate applying the Faceted Application of Subject Terminology to Library of Congress Subject Headings. Findings Network statistics complement count- or value-based metrics by providing context related to the number of records a user might actually find starting from one item and moving to others via shared subject values. Additionally, connectivity increases through the normalization of values to correct or adjust for formatting differences or by breaking pre-coordinated subject strings into separate topics. Research limitations/implications This analysis focuses on exact-string matches, which is the lowest-common denominator for searching, although many search engines and digital library indexes may use less stringent matching methods. In terms of practical implications for evaluating or improving subjects in metadata, the normalization components demonstrate where resources may be most effectively allocated for these activities (depending on a collection). Originality/value Although the individual components of this research are not particularly novel, network analysis has not generally been applied to metadata analysis. This research furthers previous studies related to metadata quality analysis of aggregations and digital collections in general.
{"title":"Investigating the use of metadata record graphs to analyze subject headings in the digital public library of America","authors":"M. Phillips, H. Tarver","doi":"10.1108/el-11-2020-0317","DOIUrl":"https://doi.org/10.1108/el-11-2020-0317","url":null,"abstract":"\u0000Purpose\u0000This study furthers metadata quality research by providing complementary network-based metrics and insights to analyze metadata records and identify areas for improvement.\u0000\u0000\u0000Design/methodology/approach\u0000Metadata record graphs apply network analysis to metadata field values; this study evaluates the interconnectedness of subjects within each Hub aggregated into the Digital Public Library of America. It also reviews the effects of NACO normalization – simulating revision of values for consistency – and breaking up pre-coordinated subject headings – to simulate applying the Faceted Application of Subject Terminology to Library of Congress Subject Headings.\u0000\u0000\u0000Findings\u0000Network statistics complement count- or value-based metrics by providing context related to the number of records a user might actually find starting from one item and moving to others via shared subject values. Additionally, connectivity increases through the normalization of values to correct or adjust for formatting differences or by breaking pre-coordinated subject strings into separate topics.\u0000\u0000\u0000Research limitations/implications\u0000This analysis focuses on exact-string matches, which is the lowest-common denominator for searching, although many search engines and digital library indexes may use less stringent matching methods. In terms of practical implications for evaluating or improving subjects in metadata, the normalization components demonstrate where resources may be most effectively allocated for these activities (depending on a collection).\u0000\u0000\u0000Originality/value\u0000Although the individual components of this research are not particularly novel, network analysis has not generally been applied to metadata analysis. This research furthers previous studies related to metadata quality analysis of aggregations and digital collections in general.\u0000","PeriodicalId":330882,"journal":{"name":"Electron. Libr.","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132314048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose This paper aims to discuss the challenges encountered in collecting, cleaning and analyzing the large data set of bibliographic metadata records in machine-readable cataloging [MARC 21] format. Possible solutions are presented. Design/methodology/approach This mixed method study relied on content analysis and social network analysis. The study examined subject representation in MARC 21 metadata records created in 2020 in WorldCat – the largest international database of “big smart data.” The methodological challenges that were encountered and solutions are examined. Findings In this general review paper with a focus on methodological issues, the discussion of challenges is followed by a discussion of solutions developed and tested as part of this study. Data collection, processing, analysis and visualization are addressed separately. Lessons learned and conclusions related to challenges and solutions for the design of a large-scale study evaluating MARC 21 bibliographic metadata from WorldCat are given. Overall recommendations for the design and implementation of future research are suggested. Originality/value There are no previous publications that address the challenges and solutions of data collection and analysis of WorldCat’s “big smart data” in the form of MARC 21 data. This is the first study to use a large data set to systematically examine MARC 21 library metadata records created after the most recent addition of new fields and subfields to MARC 21 Bibliographic Format standard in 2019 based on resource description and access rules. It is also the first to focus its analyzes on the networks formed by subject terms shared by MARC 21 bibliographic records in a data set extracted from a heterogeneous centralized database WorldCat.
目的探讨机器可读编目[MARC 21]格式的大型书目元数据记录的收集、清理和分析所面临的挑战。提出了可能的解决方案。设计/方法/方法这种混合方法研究依赖于内容分析和社会网络分析。该研究检查了2020年在世界最大的“大智能数据”国际数据库WorldCat中创建的MARC 21元数据记录中的主题表示。所遇到的方法上的挑战和解决方案进行了审查。在这篇以方法论问题为重点的综述文章中，首先讨论了挑战，然后讨论了作为本研究一部分开发和测试的解决方案。数据的收集、处理、分析和可视化分别进行了讨论。本文给出了设计一项评估WorldCat MARC 21书目元数据的大规模研究的经验教训和相关的挑战和解决方案。对未来研究的设计和实施提出了总体建议。原创性/价值以前没有出版物以MARC 21数据的形式解决WorldCat的“大智能数据”的数据收集和分析的挑战和解决方案。这是第一个使用大型数据集系统检查MARC 21图书馆元数据记录的研究，该记录是在2019年根据资源描述和访问规则向MARC 21书目格式标准添加新字段和子字段后创建的。它也是第一个集中分析由从异构中央数据库WorldCat提取的数据集中的MARC 21书目记录共享的主题术语所形成的网络。
{"title":"Collecting and evaluating large volumes of bibliographic metadata aggregated in the WorldCat database: a proposed methodology to overcome challenges","authors":"Vyacheslav Zavalin, Shawne D. Miksa","doi":"10.1108/el-11-2020-0316","DOIUrl":"https://doi.org/10.1108/el-11-2020-0316","url":null,"abstract":"\u0000Purpose\u0000This paper aims to discuss the challenges encountered in collecting, cleaning and analyzing the large data set of bibliographic metadata records in machine-readable cataloging [MARC 21] format. Possible solutions are presented.\u0000\u0000\u0000Design/methodology/approach\u0000This mixed method study relied on content analysis and social network analysis. The study examined subject representation in MARC 21 metadata records created in 2020 in WorldCat – the largest international database of “big smart data.” The methodological challenges that were encountered and solutions are examined.\u0000\u0000\u0000Findings\u0000In this general review paper with a focus on methodological issues, the discussion of challenges is followed by a discussion of solutions developed and tested as part of this study. Data collection, processing, analysis and visualization are addressed separately. Lessons learned and conclusions related to challenges and solutions for the design of a large-scale study evaluating MARC 21 bibliographic metadata from WorldCat are given. Overall recommendations for the design and implementation of future research are suggested.\u0000\u0000\u0000Originality/value\u0000There are no previous publications that address the challenges and solutions of data collection and analysis of WorldCat’s “big smart data” in the form of MARC 21 data. This is the first study to use a large data set to systematically examine MARC 21 library metadata records created after the most recent addition of new fields and subfields to MARC 21 Bibliographic Format standard in 2019 based on resource description and access rules. It is also the first to focus its analyzes on the networks formed by subject terms shared by MARC 21 bibliographic records in a data set extracted from a heterogeneous centralized database WorldCat.\u0000","PeriodicalId":330882,"journal":{"name":"Electron. Libr.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125659500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xintong Zhao, Jane Greenberg, V. Meschke, E. Toberer, Xiaohua Hu
Purpose The output of academic literature has increased significantly due to digital technology, presenting researchers with a challenge across every discipline, including materials science, as it is impossible to manually read and extract knowledge from millions of published literature. The purpose of this study is to address this challenge by exploring knowledge extraction in materials science, as applied to digital scholarship. An overriding goal is to help inform readers about the status knowledge extraction in materials science. Design/methodology/approach The authors conducted a two-part analysis, comparing knowledge extraction methods applied materials science scholarship, across a sample of 22 articles; followed by a comparison of HIVE-4-MAT, an ontology-based knowledge extraction and MatScholar, a named entity recognition (NER) application. This paper covers contextual background, and a review of three tiers of knowledge extraction (ontology-based, NER and relation extraction), followed by the research goals and approach. Findings The results indicate three key needs for researchers to consider for advancing knowledge extraction: the need for materials science focused corpora; the need for researchers to define the scope of the research being pursued, and the need to understand the tradeoffs among different knowledge extraction methods. This paper also points to future material science research potential with relation extraction and increased availability of ontologies. Originality/value To the best of the authors’ knowledge, there are very few studies examining knowledge extraction in materials science. This work makes an important contribution to this underexplored research area.
{"title":"An exploratory analysis: extracting materials science knowledge from unstructured scholarly data","authors":"Xintong Zhao, Jane Greenberg, V. Meschke, E. Toberer, Xiaohua Hu","doi":"10.1108/el-11-2020-0320","DOIUrl":"https://doi.org/10.1108/el-11-2020-0320","url":null,"abstract":"\u0000Purpose\u0000The output of academic literature has increased significantly due to digital technology, presenting researchers with a challenge across every discipline, including materials science, as it is impossible to manually read and extract knowledge from millions of published literature. The purpose of this study is to address this challenge by exploring knowledge extraction in materials science, as applied to digital scholarship. An overriding goal is to help inform readers about the status knowledge extraction in materials science.\u0000\u0000\u0000Design/methodology/approach\u0000The authors conducted a two-part analysis, comparing knowledge extraction methods applied materials science scholarship, across a sample of 22 articles; followed by a comparison of HIVE-4-MAT, an ontology-based knowledge extraction and MatScholar, a named entity recognition (NER) application. This paper covers contextual background, and a review of three tiers of knowledge extraction (ontology-based, NER and relation extraction), followed by the research goals and approach.\u0000\u0000\u0000Findings\u0000The results indicate three key needs for researchers to consider for advancing knowledge extraction: the need for materials science focused corpora; the need for researchers to define the scope of the research being pursued, and the need to understand the tradeoffs among different knowledge extraction methods. This paper also points to future material science research potential with relation extraction and increased availability of ontologies.\u0000\u0000\u0000Originality/value\u0000To the best of the authors’ knowledge, there are very few studies examining knowledge extraction in materials science. This work makes an important contribution to this underexplored research area.\u0000","PeriodicalId":330882,"journal":{"name":"Electron. Libr.","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121908409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose The purpose of this study is to explore the Twitter use of public libraries during the early period of lockdown due to the COVID-19 pandemic to evaluate the focus of Twitter communication. Design/methodology/approach A total of 57 active, public library Twitter accounts were used for data collection and analysis. The tweets examined were a combination of original tweets (n = 1,465) and retweets (n = 516) posted from other Twitter accounts on the public libraries’ Twitter feeds. A content analysis scheme was developed to analyse topical aspects of the tweets. Findings The most frequent tweets were about library events, programmes and activities. However, there was a relatively low focus on sharing community information and addressing information related to the pandemic. The study suggests that public libraries could use Twitter to provide library resources and services to their patrons, whilst also acting as a virtual community centre safely keeping patrons engaged in the face of a global pandemic. By doing so, Twitter could be used as an integral part of promoting the mission of public libraries. Research limitations/implications The study examined a limited number of public libraries’ Twitter posts. Whilst the study carried out a random sampling of 10% of public libraries from the five states that had the highest COVID-19 cases in the month of April 2020, the study only examined tweets of 57 public libraries being active in posting. Thus, the findings of the study are not for generalizing. Practical implications The content scheme includes content types regarding library services and community information. The content category scheme is general to reflect themes of content during a normal time and any emergency. Thus, this framework could be helpful for the content development of public libraries in planning social media use. Originality/value The study used a new content analysis framework to examine both original tweets and retweets for information sharing of library services and community information. The approach of content analysis is distinctive to examine libraries’ communication trends on social media not only in normal times but also in times of crisis as well. The study also incorporated additional measures to assess Twitter practices including hashtags.
{"title":"Public library Twitter use during the early period of the COVID-19 lockdown in the United States","authors":"Youngok Choi, Sung Un Kim","doi":"10.1108/EL-03-2021-0067","DOIUrl":"https://doi.org/10.1108/EL-03-2021-0067","url":null,"abstract":"\u0000Purpose\u0000The purpose of this study is to explore the Twitter use of public libraries during the early period of lockdown due to the COVID-19 pandemic to evaluate the focus of Twitter communication.\u0000\u0000\u0000Design/methodology/approach\u0000A total of 57 active, public library Twitter accounts were used for data collection and analysis. The tweets examined were a combination of original tweets (n = 1,465) and retweets (n = 516) posted from other Twitter accounts on the public libraries’ Twitter feeds. A content analysis scheme was developed to analyse topical aspects of the tweets.\u0000\u0000\u0000Findings\u0000The most frequent tweets were about library events, programmes and activities. However, there was a relatively low focus on sharing community information and addressing information related to the pandemic. The study suggests that public libraries could use Twitter to provide library resources and services to their patrons, whilst also acting as a virtual community centre safely keeping patrons engaged in the face of a global pandemic. By doing so, Twitter could be used as an integral part of promoting the mission of public libraries.\u0000\u0000\u0000Research limitations/implications\u0000The study examined a limited number of public libraries’ Twitter posts. Whilst the study carried out a random sampling of 10% of public libraries from the five states that had the highest COVID-19 cases in the month of April 2020, the study only examined tweets of 57 public libraries being active in posting. Thus, the findings of the study are not for generalizing.\u0000\u0000\u0000Practical implications\u0000The content scheme includes content types regarding library services and community information. The content category scheme is general to reflect themes of content during a normal time and any emergency. Thus, this framework could be helpful for the content development of public libraries in planning social media use.\u0000\u0000\u0000Originality/value\u0000The study used a new content analysis framework to examine both original tweets and retweets for information sharing of library services and community information. The approach of content analysis is distinctive to examine libraries’ communication trends on social media not only in normal times but also in times of crisis as well. The study also incorporated additional measures to assess Twitter practices including hashtags.\u0000","PeriodicalId":330882,"journal":{"name":"Electron. Libr.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132044295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose Owing to the uneven distribution of annotated corpus among different languages, it is necessary to bridge the gap between low resource languages and high resource languages. From the perspective of entity relation extraction, this paper aims to extend the knowledge acquisition task from a single language context to a cross-lingual context, and to improve the relation extraction performance for low resource languages. Design/methodology/approach This paper proposes a cross-lingual adversarial relation extraction (CLARE) framework, which decomposes cross-lingual relation extraction into parallel corpus acquisition and adversarial adaptation relation extraction. Based on the proposed framework, this paper conducts extensive experiments in two tasks, i.e. the English-to-Chinese and the English-to-Arabic cross-lingual entity relation extraction. Findings The Macro-F1 values of the optimal models in the two tasks are 0.880 1 and 0.789 9, respectively, indicating that the proposed CLARE framework for CLARE can significantly improve the effect of low resource language entity relation extraction. The experimental results suggest that the proposed framework can effectively transfer the corpus as well as the annotated tags from English to Chinese and Arabic. This study reveals that the proposed approach is less human labour intensive and more effective in the cross-lingual entity relation extraction than the manual method. It shows that this approach has high generalizability among different languages. Originality/value The research results are of great significance for improving the performance of the cross-lingual knowledge acquisition. The cross-lingual transfer may greatly reduce the time and cost of the manual construction of the multi-lingual corpus. It sheds light on the knowledge acquisition and organization from the unstructured text in the era of big data.
{"title":"Towards an entity relation extraction framework in the cross-lingual context","authors":"Chuanming Yu, Haodong Xue, Manyi Wang, Lu An","doi":"10.1108/el-10-2020-0304","DOIUrl":"https://doi.org/10.1108/el-10-2020-0304","url":null,"abstract":"\u0000Purpose\u0000Owing to the uneven distribution of annotated corpus among different languages, it is necessary to bridge the gap between low resource languages and high resource languages. From the perspective of entity relation extraction, this paper aims to extend the knowledge acquisition task from a single language context to a cross-lingual context, and to improve the relation extraction performance for low resource languages.\u0000\u0000\u0000Design/methodology/approach\u0000This paper proposes a cross-lingual adversarial relation extraction (CLARE) framework, which decomposes cross-lingual relation extraction into parallel corpus acquisition and adversarial adaptation relation extraction. Based on the proposed framework, this paper conducts extensive experiments in two tasks, i.e. the English-to-Chinese and the English-to-Arabic cross-lingual entity relation extraction.\u0000\u0000\u0000Findings\u0000The Macro-F1 values of the optimal models in the two tasks are 0.880 1 and 0.789 9, respectively, indicating that the proposed CLARE framework for CLARE can significantly improve the effect of low resource language entity relation extraction. The experimental results suggest that the proposed framework can effectively transfer the corpus as well as the annotated tags from English to Chinese and Arabic. This study reveals that the proposed approach is less human labour intensive and more effective in the cross-lingual entity relation extraction than the manual method. It shows that this approach has high generalizability among different languages.\u0000\u0000\u0000Originality/value\u0000The research results are of great significance for improving the performance of the cross-lingual knowledge acquisition. The cross-lingual transfer may greatly reduce the time and cost of the manual construction of the multi-lingual corpus. It sheds light on the knowledge acquisition and organization from the unstructured text in the era of big data.\u0000","PeriodicalId":330882,"journal":{"name":"Electron. Libr.","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120967494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose This paper aims to identify data set entities in scientific literature. To address poor recognition caused by a lack of training corpora in existing studies, a distant supervised learning-based approach is proposed to identify data set entities automatically from large-scale scientific literature in an open domain. Design/methodology/approach Firstly, the authors use a dictionary combined with a bootstrapping strategy to create a labelled corpus to apply supervised learning. Secondly, a bidirectional encoder representation from transformers (BERT)-based neural model was applied to identify data set entities in the scientific literature automatically. Finally, two data augmentation techniques, entity replacement and entity masking, were introduced to enhance the model generalisability and improve the recognition of data set entities. Findings In the absence of training data, the proposed method can effectively identify data set entities in large-scale scientific papers. The BERT-based vectorised representation and data augmentation techniques enable significant improvements in the generality and robustness of named entity recognition models, especially in long-tailed data set entity recognition. Originality/value This paper provides a practical research method for automatically recognising data set entities in scientific literature. To the best of the authors’ knowledge, this is the first attempt to apply distant learning to the study of data set entity recognition. The authors introduce a robust vectorised representation and two data augmentation strategies (entity replacement and entity masking) to address the problem inherent in distant supervised learning methods, which the existing research has mostly ignored. The experimental results demonstrate that our approach effectively improves the recognition of data set entities, especially long-tailed data set entities.
{"title":"Data set entity recognition based on distant supervision","authors":"Pengcheng Li, Qikai Liu, Qikai Cheng, Wei Lu","doi":"10.1108/el-10-2020-0301","DOIUrl":"https://doi.org/10.1108/el-10-2020-0301","url":null,"abstract":"\u0000Purpose\u0000This paper aims to identify data set entities in scientific literature. To address poor recognition caused by a lack of training corpora in existing studies, a distant supervised learning-based approach is proposed to identify data set entities automatically from large-scale scientific literature in an open domain.\u0000\u0000\u0000Design/methodology/approach\u0000Firstly, the authors use a dictionary combined with a bootstrapping strategy to create a labelled corpus to apply supervised learning. Secondly, a bidirectional encoder representation from transformers (BERT)-based neural model was applied to identify data set entities in the scientific literature automatically. Finally, two data augmentation techniques, entity replacement and entity masking, were introduced to enhance the model generalisability and improve the recognition of data set entities.\u0000\u0000\u0000Findings\u0000In the absence of training data, the proposed method can effectively identify data set entities in large-scale scientific papers. The BERT-based vectorised representation and data augmentation techniques enable significant improvements in the generality and robustness of named entity recognition models, especially in long-tailed data set entity recognition.\u0000\u0000\u0000Originality/value\u0000This paper provides a practical research method for automatically recognising data set entities in scientific literature. To the best of the authors’ knowledge, this is the first attempt to apply distant learning to the study of data set entity recognition. The authors introduce a robust vectorised representation and two data augmentation strategies (entity replacement and entity masking) to address the problem inherent in distant supervised learning methods, which the existing research has mostly ignored. The experimental results demonstrate that our approach effectively improves the recognition of data set entities, especially long-tailed data set entities.\u0000","PeriodicalId":330882,"journal":{"name":"Electron. Libr.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126296901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose Because of the extensive user coverage of news sites and apps, greater social and commercial value can be realized if users can access their favourite news as easily as possible. However, news has a timeliness factor; there are serious cold start and data sparsity in news recommendation, and news users are more susceptible to recent topical news. Therefore, this study aims to propose a personalized news recommendation approach based on topic model and restricted Boltzmann machine (RBM). Design/methodology/approach Firstly, the model extracts the news topic information based on the LDA2vec topic model. Then, the implicit behaviour data are analysed and converted into explicit rating data according to the rules. The highest weight is assigned to recent hot news stories. Finally, the topic information and the rating data are regarded as the conditional layer and visual layer of the conditional RBM (CRBM) model, respectively, to implement news recommendations. Findings The experimental results show that using LDA2vec-based news topic as a conditional layer in the CRBM model provides a higher prediction rating and improves the effectiveness of news recommendations. Originality/value This study proposes a personalized news recommendation approach based on an improved CRBM. Topic model is applied to news topic extraction and used as the conditional layer of the CRBM. It not only alleviates the sparseness of rating data to improve the efficient in CRBM but also considers that readers are more susceptible to popular or trending news.
{"title":"Personalized news recommendation based on an improved conditional restricted Boltzmann machine","authors":"Linxia Zhong, Wei Wei, Shixuan Li","doi":"10.1108/EL-06-2020-0165","DOIUrl":"https://doi.org/10.1108/EL-06-2020-0165","url":null,"abstract":"\u0000Purpose\u0000Because of the extensive user coverage of news sites and apps, greater social and commercial value can be realized if users can access their favourite news as easily as possible. However, news has a timeliness factor; there are serious cold start and data sparsity in news recommendation, and news users are more susceptible to recent topical news. Therefore, this study aims to propose a personalized news recommendation approach based on topic model and restricted Boltzmann machine (RBM).\u0000\u0000\u0000Design/methodology/approach\u0000Firstly, the model extracts the news topic information based on the LDA2vec topic model. Then, the implicit behaviour data are analysed and converted into explicit rating data according to the rules. The highest weight is assigned to recent hot news stories. Finally, the topic information and the rating data are regarded as the conditional layer and visual layer of the conditional RBM (CRBM) model, respectively, to implement news recommendations.\u0000\u0000\u0000Findings\u0000The experimental results show that using LDA2vec-based news topic as a conditional layer in the CRBM model provides a higher prediction rating and improves the effectiveness of news recommendations.\u0000\u0000\u0000Originality/value\u0000This study proposes a personalized news recommendation approach based on an improved CRBM. Topic model is applied to news topic extraction and used as the conditional layer of the CRBM. It not only alleviates the sparseness of rating data to improve the efficient in CRBM but also considers that readers are more susceptible to popular or trending news.\u0000","PeriodicalId":330882,"journal":{"name":"Electron. Libr.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124978787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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