Visualizing the intellectual structure of information science (2006–2015): Introducing author keyword coupling analysis

Yang, Siluo; Rui-zhen, Han; Wolfram, Dietmar; Zhao, Yuehua

doi:10.1016/j.joi.2015.12.003

Cited by 70 publications

(27 citation statements)

References 39 publications

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“…The investigations of AK are numerous and present highly varied approaches. However, most mainly count terms and use co-occurrence networks (Aizawa & Kageura, 2003;Dotsika & Watkins, 2017;Névéol, Do gan, & Lu, 2010;Radhakrishnan, Erbis, Isaacs, & Kamarthi, 2017;Su & Lee, 2010;Uddin & Khan, 2016;Yang, Han, Wolfram, & Zhao, 2016). Only a few works focus on the dynamics of keywords over time (Gil-Leiva & Alonso-Arroyo, 2007;Han, Gui, & Xu, 2014;Lee, 2016;Liu, Tian, Kong, Lee, & Xia, 2019;Mela, Roos, & Deng, 2013).…”

Section: Introductionmentioning

confidence: 99%

Survival analysis of author keywords: An application to the library and information sciences area

Peset

Garzón-Farinós

Moreno

et al. 2019

Asso for Info Science & Tech

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Our purpose is to adapt a statistical method for the analysis of discrete numerical series to the keywords appearing in scientific articles of a given area. As an example, we apply our methodological approach to the study of the keywords in the Library and Information Sciences (LIS) area. Our objective is to detect the new author keywords that appear in a fixed knowledge area in the period of 1 year in order to quantify the probabilities of survival for 10 years as a function of the impact of the journals where they appeared. Many of the new keywords appearing in the LIS field are ephemeral. Actually, more than half are never used again. In general, the terms most commonly used in the LIS area come from other areas. The average survival time of these keywords is approximately 3 years, being slightly higher in the case of words that were published in journals classified in the second quartile of the area. We believe that measuring the appearance and disappearance of terms will allow understanding some relevant aspects of the evolution of a discipline, providing in this way a new bibliometric approach.

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Section: Introductionmentioning

confidence: 99%

Survival analysis of author keywords: An application to the library and information sciences area

Peset

Garzón-Farinós

Moreno

et al. 2019

Asso for Info Science & Tech

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“…AKC analysis was introduced by Liu et al [ 38 ] and was formally proposed by Liu and Zhang [ 39 ]. This method was re-introduced and compared with CA and ABC analyses by Qiu and Wang [ 40 ], Liu and Wang [ 41 ], Song and Wu [ 42 ], and Yang et al [ 43 ]. The AKC analysis is supposed to expand the keyword co-occurrence relationship at the author level; it can also be used to establish author relationships through the keyword coupling strength of authors’ oeuvres.…”

Section: Methodsmentioning

confidence: 99%

Mining author relationship in scholarly networks based on tripartite citation analysis

Wang¹,

Wang²,

Yang³

2017

PLoS ONE

Self Cite

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Following scholars in Scientometrics as examples, we develop five author relationship networks, namely, co-authorship, author co-citation (AC), author bibliographic coupling (ABC), author direct citation (ADC), and author keyword coupling (AKC). The time frame of data sets is divided into two periods: before 2011 (i.e., T1) and after 2011 (i.e., T2). Through quadratic assignment procedure analysis, we found that some authors have ABC or AC relationships (i.e., potential communication relationship, PCR) but do not have actual collaborations or direct citations (i.e., actual communication relationship, ACR) among them. In addition, we noticed that PCR and AKC are highly correlated and that the old PCR and the new ACR are correlated and consistent. Such facts indicate that PCR tends to produce academic exchanges based on similar themes, and ABC bears more advantages in predicting potential relations. Based on tripartite citation analysis, including AC, ABC, and ADC, we also present an author-relation mining process. Such process can be used to detect deep and potential author relationships. We analyze the prediction capacity by comparing between the T1 and T2 periods, which demonstrate that relation mining can be complementary in identifying authors based on similar themes and discovering more potential collaborations and academic communities.

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“…Third, particularly for the automatic approach, four types of techniques have been used: statistical, linguistic, machine-learning-based, and hybrid approaches [12,24,25]. While the statistical approaches do not require any learning mechanism and use statistical information of terms, e.g., tf, idf, and tfidf [26,27], the linguistic approaches use linguistic features of terms, e.g., parsing, sentiment analysis, and semantics [28,29]. Machine-learning-based approaches use key terms that are extracted from the collected textual data by means of a training process and apply them to a machine learning model to find key terms in new textual data [12,28].…”

Section: Key Term Identification In the Previous Text Mining Applicatmentioning

confidence: 99%

“…Yang, Han, Wolfram and Zhao [28] introduced the author keyword coupling analysis (AKCA) method to visualize the field of information science (2006-2015).…”

Section: Patent Documentsmentioning

confidence: 99%

SocialTERM-Extractor: Identifying and Predicting Social-Problem-Specific Key Noun Terms from a Large Number of Online News Articles Using Text Mining and Machine Learning Techniques

Suh

2019

Sustainability

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In the digital age, the abundant unstructured data on the Internet, particularly online news articles, provide opportunities for identifying social problems and understanding social systems for sustainability. However, the previous works have not paid attention to the social-problem-specific perspectives of such big data, and it is currently unclear how information technologies can use the big data to identify and manage the ongoing social problems. In this context, this paper introduces and focuses on social-problem-specific key noun terms, namely SocialTERMs, which can be used not only to search the Internet for social-problem-related data, but also to monitor the ongoing and future events of social problems. Moreover, to alleviate time-consuming human efforts in identifying the SocialTERMs, this paper designs and examines the SocialTERM-Extractor, which is an automatic approach for identifying the key noun terms of social-problem-related topics, namely SPRTs, in a large number of online news articles and predicting the SocialTERMs among the identified key noun terms. This paper has its novelty as the first trial to identify and predict the SocialTERMs from a large number of online news articles, and it contributes to literature by proposing three types of text-mining-based features, namely temporal weight, sentiment, and complex network structural features, and by comparing the performances of such features with various machine learning techniques including deep learning. Particularly, when applied to a large number of online news articles that had been published in South Korea over a 12-month period and mostly written in Korean, the experimental results showed that Boosting Decision Tree gave the best performances with the full feature sets. They showed that the SocialTERMs can be predicted with high performances by the proposed SocialTERM-Extractor. Eventually, this paper can be beneficial for individuals or organizations who want to explore and use social-problem-related data in a systematical manner for understanding and managing social problems even though they are unfamiliar with ongoing social problems.

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Visualizing the intellectual structure of information science (2006–2015): Introducing author keyword coupling analysis

Cited by 70 publications

References 39 publications

Survival analysis of author keywords: An application to the library and information sciences area

Survival analysis of author keywords: An application to the library and information sciences area

Mining author relationship in scholarly networks based on tripartite citation analysis

SocialTERM-Extractor: Identifying and Predicting Social-Problem-Specific Key Noun Terms from a Large Number of Online News Articles Using Text Mining and Machine Learning Techniques

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