Measuring the interdisciplinarity of Information and Library Science interactions using citation analysis and semantic analysis

Huang, Lu; Cai, Yijie; Zhao, En-Guang; Zhang, Shengting; Shu, Yongqian; Fan, Jianqing

doi:10.1007/s11192-022-04401-x

Cited by 10 publications

(4 citation statements)

References 62 publications

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“…While numerous studies have explored citation network analysis across various contemporary scientific disciplines over the past decades [ 23 , 35 , 36 ], its application in the humanities and historical corpora has been relatively limited [ 37 – 39 ]. Additionally, those studies have a predominant focus on several key areas: (i) Comparisons of the humanities to other disciplines in terms of growth timeframes, internationality, and interconnectivity [ 37 ]; (ii) exploration of the potentially problematic role of bibliometrics in performance evaluation within the humanities [ 40 , 41 ]; (iii) mapping the cognitive and social structure of the humanities, such as understanding the relationships among various disciplines and sub-disciplines in institutional frameworks and personal relations [ 19 , 42 , 43 ].…”

Section: Related Workmentioning

confidence: 99%

Citation network analysis for viewpoint plurality assessment of historical corpora: The case of the medieval rabbinic literature

Ben-Gigi,

Zhitomirsky-Geffet,

Katzoff

et al. 2024

PLoS ONE

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Citation networks enable analysis of author groups, defining in-group dynamics, and mapping out inter-group relationships. While intellectual diversity and inclusiveness is one of the important principles of modern scholarship, it is intriguing to explore the extent to which these principles apply to historical communities of leaders and intellectuals. This paper introduces a novel methodological framework aimed at assessing the degree of viewpoint plurality and diversity of historical scholarship communities, through an in-depth analysis of the citations used in their literature, which has become possible due to the recently developed advanced computational analysis techniques. To achieve this goal, we have devised a set of new network-based indicators grounded in standard network metrics. These indicators can be applied at both the individual author and community levels. The developed methodology was applied to a citation network automatically constructed from a corpus of Rabbinic Halachic literature spanning the 10th to 15th centuries. This corpus includes over 5,000 citations from hundreds of books authored by approximately 140 Rabbinic scholars from six diverse geographic communities. We found that most of the authors and communities cite many more external resources from other communities than their own reflecting a willingness to engage with a diverse range of viewpoints. A more in-depth analysis based on the novel proportional diversity measures unveils more intriguing insights. Contrary to expectations, communities with the greatest number of external citations, such as Spain and Ashkenaz, surprisingly exhibit lower levels of viewpoint plurality compared to others, such as Italy and North Africa, elucidating a key finding of the study.

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Section: Related Workmentioning

confidence: 99%

Citation network analysis for viewpoint plurality assessment of historical corpora: The case of the medieval rabbinic literature

Ben-Gigi,

Zhitomirsky-Geffet,

Katzoff

et al. 2024

PLoS ONE

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“…For instance, it is not very effective in handling dynamic short texts and can be slow in training on large datasets [22]. Furthermore, it struggles to identify relationships between topics [23]. The model's generalization ability, accuracy, and interpretability of results are challenged [4].…”

Section: A Topic Model Selectionmentioning

confidence: 99%

Advancements of Artificial Intelligence Techniques in the Realm About Library and Information Subject—A Case Survey of Latent Dirichlet Allocation Method

Pan,

Xue

2023

IEEE Access

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To investigate the advancements of artificial intelligence techniques in the realm of library and information subject, we have chosen the Latent Dirichlet Allocation method as a case study to explore its current study status and implementations. Traditional theme mining analyses utilize methods such as word frequency statistics, co-occurrence analysis, community detection, and citation analysis to capture external quantitative features of words or documents. In contrast, the Latent Dirichlet Allocation theme modelling method employs a three-layer Bayesian structure of document-topic-word to describe the themes of documents and the semantic relationships among words, enabling a better exploration of latent semantic information in text. This method plays a pivotal role in fine-grained knowledge extraction and analysis. We systematically review more than a decade of relevant literature in the realm about library and information subject. Through content analysis, we construct an analytical architecture for the implementation of the Latent Dirichlet Allocation method. This architecture, viewed from the perspective of the implementation process of Latent Dirichlet Allocation, comprehensively summarizes the core stages and technical challenges, including text pre-processing, model construction (i.e., theme model selection and optimal theme number determination), and model solving. Additionally, we provide a comprehensive overview of the current study status of the Latent Dirichlet Allocation method across various implementation domains, such as theme exploration, knowledge organization, academic evaluation, sentiment analysis, and recommendation study. Our findings indicate that the Latent Dirichlet Allocation method has formed a mature analytical process in the realm of library and information subject, with ongoing growth in study interest.

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“…To assess the interdisciplinarity of the working groups' collaborations, we analysed the literature on which the group drew to produce their published articles, the 'inspiration' measure of [9] and used by several authors in whole or in part as a measure of interdisciplinarity [33,[70][71][72]. Specifically, we examined the disciplinarity diversity of the cited journals [76,73].…”

Section: Collation Of Articles Used and Producedmentioning

confidence: 99%

Interdisciplinary collaboration from diverse science teams can produce significant outcomes

Specht

Crowston

2022

PLoS ONE

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Scientific teams are increasingly diverse in discipline, international scope and demographics. Diversity has been found to be a driver of innovation but also can be a source of interpersonal friction. Drawing on a mixed-method study of 22 scientific working groups, this paper presents evidence that team diversity has a positive impact on scientific output (i.e., the number of journal papers and citations) through the mediation of the interdisciplinarity of the collaborative process, as evidenced by publishing in and citing more diverse sources. Ironically these factors also seem to be related to lower team member satisfaction and perceived effectiveness, countered by the gender balance of the team. Qualitative data suggests additional factors that facilitate collaboration, such as trust and leadership. Our findings have implications for team design and management, as team diversity seems beneficial, but the process of integration can be difficult and needs management to lead to a productive and innovative process.

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Measuring the interdisciplinarity of Information and Library Science interactions using citation analysis and semantic analysis

Cited by 10 publications

References 62 publications

Citation network analysis for viewpoint plurality assessment of historical corpora: The case of the medieval rabbinic literature

Citation network analysis for viewpoint plurality assessment of historical corpora: The case of the medieval rabbinic literature

Advancements of Artificial Intelligence Techniques in the Realm About Library and Information Subject—A Case Survey of Latent Dirichlet Allocation Method

Interdisciplinary collaboration from diverse science teams can produce significant outcomes

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