Pub Date : 2023-10-24DOI: 10.1177/01655515231203511
Philip Hider, Deborah Lee
We examine how music subgenres are differentiated from each other within seven parent genres – classical, folk, reggae, country, blues, electronic and jazz – according to two different sources, AllMusic and the Library of Congress Genre/Form Terms. Medium was by far the most common differentiator, but there were many others, with most subgenres defined according to multiple characteristic types, the use of which varied greatly across genres. Overall, differentiation was based more on characteristics intrinsic to the music, but prominent extrinsic characteristic types included culture and period. Also prominent was the identification of characteristics associated with other subgenres and genres, representing hybridisation. The resulting codebook of characteristics only partly overlaps with the major facets of music identified in the knowledge organisation literature. Our research conceptualises the musical subgenre, suggesting that music subgenres are differentiated from and connected to other subgenres, and to higher-level genres, in complex, familial ways – horizontally, vertically and obliquely.
{"title":"A polyphony of characteristics: An analysis of the categorisation of music’s subgenres","authors":"Philip Hider, Deborah Lee","doi":"10.1177/01655515231203511","DOIUrl":"https://doi.org/10.1177/01655515231203511","url":null,"abstract":"We examine how music subgenres are differentiated from each other within seven parent genres – classical, folk, reggae, country, blues, electronic and jazz – according to two different sources, AllMusic and the Library of Congress Genre/Form Terms. Medium was by far the most common differentiator, but there were many others, with most subgenres defined according to multiple characteristic types, the use of which varied greatly across genres. Overall, differentiation was based more on characteristics intrinsic to the music, but prominent extrinsic characteristic types included culture and period. Also prominent was the identification of characteristics associated with other subgenres and genres, representing hybridisation. The resulting codebook of characteristics only partly overlaps with the major facets of music identified in the knowledge organisation literature. Our research conceptualises the musical subgenre, suggesting that music subgenres are differentiated from and connected to other subgenres, and to higher-level genres, in complex, familial ways – horizontally, vertically and obliquely.","PeriodicalId":54796,"journal":{"name":"Journal of Information Science","volume":"399 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135273574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-21DOI: 10.1177/01655515231188341
Mustafa Mhamed, Richard Sutcliffe, Husam Quteineh, Xia Sun, Eiad Almekhlafi, Ephrem Afele Retta, Jun Feng
Arabic sentiment analysis has become an important research field in recent years. Initially, work focused on Modern Standard Arabic (MSA), which is the most widely used form. Since then, work has been carried out on several different dialects, including Egyptian, Levantine and Moroccan. Moreover, a number of data sets have been created to support such work. However, up until now, no work has been carried out on Sudanese Arabic, a dialect which has 32 million speakers. In this article, two new public data sets are introduced, the two-class Sudanese Sentiment Data set (SudSenti2) and the three-class Sudanese Sentiment Data set (SudSenti3). In the preparation phase, we establish a Sudanese stopword list. Furthermore, a convolutional neural network (CNN) architecture, Sentiment Convolutional MMA (SCM), is proposed, comprising five CNN layers together with a novel Mean Max Average (MMA) pooling layer, to extract the best features. This SCM model is applied to SudSenti2 and SudSenti3 and shown to be superior to the baseline models, with accuracies of 92.25% and 85.23% (Experiments 1 and 2). The performance of MMA is compared with Max, Avg and Min and shown to be better on SudSenti2, the Saudi Sentiment Data set and the MSA Hotel Arabic Review Data set by 1.00%, 0.83% and 0.74%, respectively (Experiment 3). Next, we conduct an ablation study to determine the contribution to performance of text normalisation and the Sudanese stopword list (Experiment 4). For normalisation, this makes a difference of 0.43% on two-class and 0.45% on three-class. For the custom stoplist, the differences are 0.82% and 0.72%, respectively. Finally, the model is compared with other deep learning classifiers, including transformer-based language models for Arabic, and shown to be comparable for SudSenti2 (Experiment 5).
近年来，阿拉伯语情感分析已成为一个重要的研究领域。最初，工作重点是现代标准阿拉伯语(MSA)，这是最广泛使用的形式。从那以后，对几种不同的方言进行了研究，包括埃及语、黎凡特语和摩洛哥语。此外，还建立了一些数据集来支持这项工作。然而，到目前为止，还没有对苏丹阿拉伯语进行任何研究，这是一种有3200万人使用的方言。本文介绍了两个新的公共数据集，两类苏丹情感数据集(SudSenti2)和三类苏丹情感数据集(SudSenti3)。在准备阶段，我们建立了苏丹语停词表。此外，提出了一种卷积神经网络(CNN)架构，即情感卷积MMA (SCM)，该架构由五个CNN层和一个新颖的Mean Max Average (MMA)池化层组成，用于提取最佳特征。该SCM模型应用于SudSenti2和SudSenti3，结果显示优于基线模型，准确率为92.25%和85.23%(实验1和2)。MMA的性能与Max、Avg和Min进行了比较，结果显示在SudSenti2、沙特情绪数据集和MSA酒店阿拉伯评论数据集上分别提高了1.00%、0.83%和0.74%(实验3)。我们进行了消融研究，以确定文本规范化和苏丹停顿词列表对性能的贡献(实验4)。对于规范化，这使得两类和三类的差异分别为0.43%和0.45%。对于自定义停车表，差异分别为0.82%和0.72%。最后，将该模型与其他深度学习分类器(包括阿拉伯语的基于转换器的语言模型)进行比较，并证明与SudSenti2具有可比性(实验5)。
{"title":"A deep CNN architecture with novel pooling layer applied to two Sudanese Arabic sentiment data sets","authors":"Mustafa Mhamed, Richard Sutcliffe, Husam Quteineh, Xia Sun, Eiad Almekhlafi, Ephrem Afele Retta, Jun Feng","doi":"10.1177/01655515231188341","DOIUrl":"https://doi.org/10.1177/01655515231188341","url":null,"abstract":"Arabic sentiment analysis has become an important research field in recent years. Initially, work focused on Modern Standard Arabic (MSA), which is the most widely used form. Since then, work has been carried out on several different dialects, including Egyptian, Levantine and Moroccan. Moreover, a number of data sets have been created to support such work. However, up until now, no work has been carried out on Sudanese Arabic, a dialect which has 32 million speakers. In this article, two new public data sets are introduced, the two-class Sudanese Sentiment Data set (SudSenti2) and the three-class Sudanese Sentiment Data set (SudSenti3). In the preparation phase, we establish a Sudanese stopword list. Furthermore, a convolutional neural network (CNN) architecture, Sentiment Convolutional MMA (SCM), is proposed, comprising five CNN layers together with a novel Mean Max Average (MMA) pooling layer, to extract the best features. This SCM model is applied to SudSenti2 and SudSenti3 and shown to be superior to the baseline models, with accuracies of 92.25% and 85.23% (Experiments 1 and 2). The performance of MMA is compared with Max, Avg and Min and shown to be better on SudSenti2, the Saudi Sentiment Data set and the MSA Hotel Arabic Review Data set by 1.00%, 0.83% and 0.74%, respectively (Experiment 3). Next, we conduct an ablation study to determine the contribution to performance of text normalisation and the Sudanese stopword list (Experiment 4). For normalisation, this makes a difference of 0.43% on two-class and 0.45% on three-class. For the custom stoplist, the differences are 0.82% and 0.72%, respectively. Finally, the model is compared with other deep learning classifiers, including transformer-based language models for Arabic, and shown to be comparable for SudSenti2 (Experiment 5).","PeriodicalId":54796,"journal":{"name":"Journal of Information Science","volume":"14 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135513261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-19DOI: 10.1177/01655515231199929
Lien-Fa Lin, Yung-Ming Li, Yen-Chen Lin
In recent years, social networks have grown rapidly, and their applications in the healthcare domain are increasingly proposed. Using the crowd wisdom generated from social networks, we can find similar and reliable people sharing helpful experiences. The existing dedicated social networking services for health mainly focus on sharing, but not categorising and extracting. In this research, we construct an environment for social knowledge sharing and expert referring. Analysing queries from online public health databases and the factors of health similarity, social reliability and social intimacy, we extract health knowledge to recommend relevant social knowledge (also called threads) and helpful experts providing consulting. Specifically, the proposed social diagnosis mechanism helps the health seeker to identify relevant threads and recommends enthusiastic experts for healthcare support. Experimental results reveal that the proposed mechanism can effectively improve healthcare knowledge sharing and realise diagnosis support from the crowd.
{"title":"A social diagnosis mechanism for healthcare knowledge sharing","authors":"Lien-Fa Lin, Yung-Ming Li, Yen-Chen Lin","doi":"10.1177/01655515231199929","DOIUrl":"https://doi.org/10.1177/01655515231199929","url":null,"abstract":"In recent years, social networks have grown rapidly, and their applications in the healthcare domain are increasingly proposed. Using the crowd wisdom generated from social networks, we can find similar and reliable people sharing helpful experiences. The existing dedicated social networking services for health mainly focus on sharing, but not categorising and extracting. In this research, we construct an environment for social knowledge sharing and expert referring. Analysing queries from online public health databases and the factors of health similarity, social reliability and social intimacy, we extract health knowledge to recommend relevant social knowledge (also called threads) and helpful experts providing consulting. Specifically, the proposed social diagnosis mechanism helps the health seeker to identify relevant threads and recommends enthusiastic experts for healthcare support. Experimental results reveal that the proposed mechanism can effectively improve healthcare knowledge sharing and realise diagnosis support from the crowd.","PeriodicalId":54796,"journal":{"name":"Journal of Information Science","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135730036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-19DOI: 10.1177/01655515231203506
Isto Huvila, Olle Sköld
Research on how archaeological fieldwork manuals, a sub-category of methods handbooks, regulate research documentation is limited. Qualitative content analysis of 25 English-language archaeological field manuals from the early 1900s to 2010s showed that they instruct how to describe the documentation work, prescribe practices and workflows, and function as often pre-coordinated descriptions of work. A manual forms a ‘working space’ that is sometimes adopted as such by following the detailed advice given in some of the texts but likely more often used as a more general point of reference. The fact that many manuals do not provide exact recipes for the fieldwork as a whole means that they function as comprehensive representations and documentation (paradata) of actual fieldwork practices only when read in parallel with field documentation.
{"title":"A fieldwork manual as a regulatory device: Instructing, prescribing and describing documentation work","authors":"Isto Huvila, Olle Sköld","doi":"10.1177/01655515231203506","DOIUrl":"https://doi.org/10.1177/01655515231203506","url":null,"abstract":"Research on how archaeological fieldwork manuals, a sub-category of methods handbooks, regulate research documentation is limited. Qualitative content analysis of 25 English-language archaeological field manuals from the early 1900s to 2010s showed that they instruct how to describe the documentation work, prescribe practices and workflows, and function as often pre-coordinated descriptions of work. A manual forms a ‘working space’ that is sometimes adopted as such by following the detailed advice given in some of the texts but likely more often used as a more general point of reference. The fact that many manuals do not provide exact recipes for the fieldwork as a whole means that they function as comprehensive representations and documentation (paradata) of actual fieldwork practices only when read in parallel with field documentation.","PeriodicalId":54796,"journal":{"name":"Journal of Information Science","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135778484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.1177/01655515231182072
Hao Jiang, Chuanzhen Li, Juanjuan Cai, Jingling Wang
Personalised news recommendation comprises two crucial components: news understanding and user modelling. Previous studies have attempted to model news understanding and user interests using various internal news information and external knowledge graphs (KG). However, they have overlooked the collaborative function of the external KG and the internal information among diverse news and user behaviours, resulting in serious news cold-start problems and poor interpretability of user interests. To address these issues, this article proposes a novel approach called Relation-Aware Approach based on Multi-view News Network for News Recommendation (MNN4Rec). Specifically, MNN4Rec first constructs a Multi-view News Network (MNN), which includes candidate news and user-clicked news, and represents their exclusive multi-view information as heterogeneous nodes. Furthermore, we develop explicit and implicit news relationships and design a special sampling algorithm to search for news co-neighbours. We then use a novel dual-channel graph attention mechanism to obtain the fine-grained news understanding representation. Moreover, we construct explainable user interests by modelling the interaction of user-clicked news through the multi-headed self-attention mechanism in both semantic and relation levels. Finally, we match candidate news understanding with user interests to generate a prediction score for recommendation. Experimental results on Microsoft’s news data set MIND demonstrate that MNN4Rec outperforms existing news-recommendation methods while also mitigating the cold-start problem and enhancing the interpretability of user interests. Our code is available at https://github.com/JiangHaoPG11/MNN4Rec_code .
{"title":"MNN4Rec: A relation-aware approach based on multi-view news network for news recommendation","authors":"Hao Jiang, Chuanzhen Li, Juanjuan Cai, Jingling Wang","doi":"10.1177/01655515231182072","DOIUrl":"https://doi.org/10.1177/01655515231182072","url":null,"abstract":"Personalised news recommendation comprises two crucial components: news understanding and user modelling. Previous studies have attempted to model news understanding and user interests using various internal news information and external knowledge graphs (KG). However, they have overlooked the collaborative function of the external KG and the internal information among diverse news and user behaviours, resulting in serious news cold-start problems and poor interpretability of user interests. To address these issues, this article proposes a novel approach called Relation-Aware Approach based on Multi-view News Network for News Recommendation (MNN4Rec). Specifically, MNN4Rec first constructs a Multi-view News Network (MNN), which includes candidate news and user-clicked news, and represents their exclusive multi-view information as heterogeneous nodes. Furthermore, we develop explicit and implicit news relationships and design a special sampling algorithm to search for news co-neighbours. We then use a novel dual-channel graph attention mechanism to obtain the fine-grained news understanding representation. Moreover, we construct explainable user interests by modelling the interaction of user-clicked news through the multi-headed self-attention mechanism in both semantic and relation levels. Finally, we match candidate news understanding with user interests to generate a prediction score for recommendation. Experimental results on Microsoft’s news data set MIND demonstrate that MNN4Rec outperforms existing news-recommendation methods while also mitigating the cold-start problem and enhancing the interpretability of user interests. Our code is available at https://github.com/JiangHaoPG11/MNN4Rec_code .","PeriodicalId":54796,"journal":{"name":"Journal of Information Science","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135884147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-30DOI: 10.1177/01655515231196391
Josceli M Tenorio, Fabrício Landi de Moraes, Ivan Torres Pisa
Successful consumer health vocabulary (CHV) models have been engineered and updated by using automatic term extraction techniques from online content. However, the relationship between terms has yet to be mapped. This study aims to describe a CHV model for the Brazilian Portuguese language that is supported by a complex network. The method was split up into three distinct stages: (1) collect and automatically extract terms from structured data sources on the web, such as Unified Medical Language System (UMLS) vocabularies and DBpedia; (2) construct a complex network; and (3) select the terms supported by clustering techniques. A model called CHV.br was developed and supported by a complex network structure which makes connections between the controlled vocabulary and consumer vocabulary and maps semantic relationships as categories, synonyms and related terms. CHV.br contains 146,956 terms, of which 31,439 are UMLS preferred terms and 83,279 are synonyms. The CHV.br is available and powered by Simple Knowledge Organization System and Resource Description Framework standards. The method used in this study showed to be valid for the selection of the candidate terms by connecting the terms from different reliable resources, in addition to expanding the number of terms and their semantic relationships. The content and structure of CHV.br could play a vital role in enhancing the development of consumer-oriented health applications.
{"title":"CHV.br: Exploratory study for the development of a consumer health vocabulary (CHV) supported by a network model for Brazilian Portuguese language","authors":"Josceli M Tenorio, Fabrício Landi de Moraes, Ivan Torres Pisa","doi":"10.1177/01655515231196391","DOIUrl":"https://doi.org/10.1177/01655515231196391","url":null,"abstract":"Successful consumer health vocabulary (CHV) models have been engineered and updated by using automatic term extraction techniques from online content. However, the relationship between terms has yet to be mapped. This study aims to describe a CHV model for the Brazilian Portuguese language that is supported by a complex network. The method was split up into three distinct stages: (1) collect and automatically extract terms from structured data sources on the web, such as Unified Medical Language System (UMLS) vocabularies and DBpedia; (2) construct a complex network; and (3) select the terms supported by clustering techniques. A model called CHV.br was developed and supported by a complex network structure which makes connections between the controlled vocabulary and consumer vocabulary and maps semantic relationships as categories, synonyms and related terms. CHV.br contains 146,956 terms, of which 31,439 are UMLS preferred terms and 83,279 are synonyms. The CHV.br is available and powered by Simple Knowledge Organization System and Resource Description Framework standards. The method used in this study showed to be valid for the selection of the candidate terms by connecting the terms from different reliable resources, in addition to expanding the number of terms and their semantic relationships. The content and structure of CHV.br could play a vital role in enhancing the development of consumer-oriented health applications.","PeriodicalId":54796,"journal":{"name":"Journal of Information Science","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136336485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Author name disambiguation (AND) is the task of resolving the ambiguity problem in bibliographic databases, where distinct real-world authors may share the same name or same author may have distinct names. The aim of AND is to split the name-ambiguous entities (articles) into the corresponding authors. Existing AND algorithms mainly focus on designing different similarity metrics between two ambiguous articles. However, most previous methods empirically select and process the features of entities, then use features to predict the similarity by data-driven models. In this article, we are motivated by natural questions: Which features are most useful for splitting name-ambiguous entities? Can they be automatically determined by an optimisation approach rather than heuristic feature engineering? Therefore, we proposed a novel end-to-end differentiable feature selection algorithm, automatically searching the optimal features for AND task (AAND). AAND optimises the discrete feature selection by differentiable Gumbel-Softmax, leading to the joint learning of feature selection policy and similarity prediction model. The experiments are conducted on a benchmark data set, S2AND, which harmonises eight different AND data sets. The results show that the performance of our proposal is superior to the advanced AND methods and feature selection algorithms. Meanwhile, deep insights into AND features are also given.
{"title":"Automatic author name disambiguation by differentiable feature selection","authors":"ZhiJian Fang, Yue Zhuo, Jinying Xu, Zhechong Tang, Zijie Jia, HuaXiong Zhang","doi":"10.1177/01655515231193859","DOIUrl":"https://doi.org/10.1177/01655515231193859","url":null,"abstract":"Author name disambiguation (AND) is the task of resolving the ambiguity problem in bibliographic databases, where distinct real-world authors may share the same name or same author may have distinct names. The aim of AND is to split the name-ambiguous entities (articles) into the corresponding authors. Existing AND algorithms mainly focus on designing different similarity metrics between two ambiguous articles. However, most previous methods empirically select and process the features of entities, then use features to predict the similarity by data-driven models. In this article, we are motivated by natural questions: Which features are most useful for splitting name-ambiguous entities? Can they be automatically determined by an optimisation approach rather than heuristic feature engineering? Therefore, we proposed a novel end-to-end differentiable feature selection algorithm, automatically searching the optimal features for AND task (AAND). AAND optimises the discrete feature selection by differentiable Gumbel-Softmax, leading to the joint learning of feature selection policy and similarity prediction model. The experiments are conducted on a benchmark data set, S2AND, which harmonises eight different AND data sets. The results show that the performance of our proposal is superior to the advanced AND methods and feature selection algorithms. Meanwhile, deep insights into AND features are also given.","PeriodicalId":54796,"journal":{"name":"Journal of Information Science","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135107090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-15DOI: 10.1177/01655515231193845
Naseema Sheriff, R Sevukan
In today’s data-driven culture, research data management (RDM) is essential for the research community. The demand for reusing research datasets is a challenging and diverse process for the scientific community. Despite this, it is essential in RDM to discover trends and themes using text mining, which is scarce. The purpose of this study is to employ text mining to discover insights from job advertisements associated with RDM profiles, which collected 810 advertisements. We found RDM-related patterns using latent Dirichlet allocation (LDA) and identified three key contexts. The first is ‘research services in libraries’, with the topics of research services, research information, research universities, collection processes and library services. The second context is ‘research data’, which includes RDM, business data, university data, research data, health research, science research, social science research, data centres, data services, statistical software, digital scholarship and digital preservation. The third context is ‘workplace environment’, and the topics are leadership, work development and scientific position. Job title normalisation reveals names such as ‘data librarian’, ‘librarian’, ‘director’, ‘data curator’, ‘data manager’, ‘research data librarian’, ‘data specialist’ and ‘data officer’ are frequently employed. Focusing on titles with a single or double occurrence is new and interesting for developing nations. Reputable institutions such as Harvard, Stanford and the Massachusetts Institute of Technology, as well as countries such as the United States, the United Kingdom, Canada and Germany, are the major participants in RDM practises and services. This discovery will assist higher education institutions, RDM stakeholders, which aid in the formulation of curriculum, and job seekers to familiarise themselves with the themes.
{"title":"Discovering research data management trends from job advertisements using a text-mining approach","authors":"Naseema Sheriff, R Sevukan","doi":"10.1177/01655515231193845","DOIUrl":"https://doi.org/10.1177/01655515231193845","url":null,"abstract":"In today’s data-driven culture, research data management (RDM) is essential for the research community. The demand for reusing research datasets is a challenging and diverse process for the scientific community. Despite this, it is essential in RDM to discover trends and themes using text mining, which is scarce. The purpose of this study is to employ text mining to discover insights from job advertisements associated with RDM profiles, which collected 810 advertisements. We found RDM-related patterns using latent Dirichlet allocation (LDA) and identified three key contexts. The first is ‘research services in libraries’, with the topics of research services, research information, research universities, collection processes and library services. The second context is ‘research data’, which includes RDM, business data, university data, research data, health research, science research, social science research, data centres, data services, statistical software, digital scholarship and digital preservation. The third context is ‘workplace environment’, and the topics are leadership, work development and scientific position. Job title normalisation reveals names such as ‘data librarian’, ‘librarian’, ‘director’, ‘data curator’, ‘data manager’, ‘research data librarian’, ‘data specialist’ and ‘data officer’ are frequently employed. Focusing on titles with a single or double occurrence is new and interesting for developing nations. Reputable institutions such as Harvard, Stanford and the Massachusetts Institute of Technology, as well as countries such as the United States, the United Kingdom, Canada and Germany, are the major participants in RDM practises and services. This discovery will assist higher education institutions, RDM stakeholders, which aid in the formulation of curriculum, and job seekers to familiarise themselves with the themes.","PeriodicalId":54796,"journal":{"name":"Journal of Information Science","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135396503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.1177/01655515231196390
Hadi Harati, Alireza Isfandyari-Moghaddam
The main purpose of this article is to present a model for information-seeking behaviour with an emphasis on unplanned and planned behaviour of users in using library resources and services. The working method was that, reviewing the literature and previous information behaviour models, such as Wilson, Ellis, Kuhlthau, and Dervin models, this article proposes a novel model of information-seeking behaviour for library users. Our model of information-seeking behaviour was developed by combining the existing models of planned information-seeking behaviour with the focus on the factors affecting unplanned rather than planned behaviour of users in accessing resources or services. Our proposed model for information-seeking behaviour of clients has two main parts. The first part planned behaviour resulting from a problem or a certain information need according to which the user seeks to find information in a planned manner. The second part deals with unplanned behaviour shaped by a hidden or uncertain information need. Finally, both types of behaviour can result in the discovery, extraction, collection and use of information.
{"title":"A model of planned and unplanned information-seeking behaviour","authors":"Hadi Harati, Alireza Isfandyari-Moghaddam","doi":"10.1177/01655515231196390","DOIUrl":"https://doi.org/10.1177/01655515231196390","url":null,"abstract":"The main purpose of this article is to present a model for information-seeking behaviour with an emphasis on unplanned and planned behaviour of users in using library resources and services. The working method was that, reviewing the literature and previous information behaviour models, such as Wilson, Ellis, Kuhlthau, and Dervin models, this article proposes a novel model of information-seeking behaviour for library users. Our model of information-seeking behaviour was developed by combining the existing models of planned information-seeking behaviour with the focus on the factors affecting unplanned rather than planned behaviour of users in accessing resources or services. Our proposed model for information-seeking behaviour of clients has two main parts. The first part planned behaviour resulting from a problem or a certain information need according to which the user seeks to find information in a planned manner. The second part deals with unplanned behaviour shaped by a hidden or uncertain information need. Finally, both types of behaviour can result in the discovery, extraction, collection and use of information.","PeriodicalId":54796,"journal":{"name":"Journal of Information Science","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135981108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.1177/01655515231196392
Paulina Sajna-Kosobucka, Radoslaw Sajna-Kunowsky
The Internet has become a very important tool of modern political communication. Political parties use it for a variety of purposes, including relationships with its supporters and potential voters. Therefore, the website of any political party is an important channel of communication. Such a website should be user-friendly to various audiences. The purpose of the article is to assess the quality of information architecture (IA) on the websites of the two major American political parties: the Republican Party and the Democratic Party. Triangulation of methods was used in the research: a qualitative-heuristic assessment of the IA on the mentioned websites, expert assessment and a comparative analysis. Within the examined websites, there is a noticeable lack of some important components of the IA. The website of the Republican Party is slightly better in terms of quality than the website of Democratic Party by 14.09%. The results of the conducted research may have an impact not only on the assessment of the technological advancement of parties but also on the image and public perception, and therefore also the effectiveness in reaching voters of a given party. This approach should contribute to the development of websites research in order to improve the quality of user experience and information processes.
{"title":"Information architecture on the websites of major American political parties: Qualitative-heuristic assessment and comparative analysis","authors":"Paulina Sajna-Kosobucka, Radoslaw Sajna-Kunowsky","doi":"10.1177/01655515231196392","DOIUrl":"https://doi.org/10.1177/01655515231196392","url":null,"abstract":"The Internet has become a very important tool of modern political communication. Political parties use it for a variety of purposes, including relationships with its supporters and potential voters. Therefore, the website of any political party is an important channel of communication. Such a website should be user-friendly to various audiences. The purpose of the article is to assess the quality of information architecture (IA) on the websites of the two major American political parties: the Republican Party and the Democratic Party. Triangulation of methods was used in the research: a qualitative-heuristic assessment of the IA on the mentioned websites, expert assessment and a comparative analysis. Within the examined websites, there is a noticeable lack of some important components of the IA. The website of the Republican Party is slightly better in terms of quality than the website of Democratic Party by 14.09%. The results of the conducted research may have an impact not only on the assessment of the technological advancement of parties but also on the image and public perception, and therefore also the effectiveness in reaching voters of a given party. This approach should contribute to the development of websites research in order to improve the quality of user experience and information processes.","PeriodicalId":54796,"journal":{"name":"Journal of Information Science","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135980884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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