Undercovering research trends: Network analysis of keywords in scholarly articles

Duvvuru, Arjun; Kamarthi, Sagar; Sultornsanee, Sivarit

doi:10.1109/jcsse.2012.6261963

Cited by 23 publications

(20 citation statements)

References 14 publications

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“…A similar study for the field of Marketing Science which relies on the usage of keyword counts can be found at [81]. A. Duvvuru et al [82] conducted a keyword based study for the European Journal of Operational Research. They suggested a network analysis technique for the keywords in scholarly articles.…”

Section: ) Frequency Analysismentioning

confidence: 99%

Authorship and Content Analysis of Engineering Education Research: A Case Study

Nawaz

2016

Int. J. Eng. Ped.

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Abstract-In this paper, Engineering Education as a discipline has been analyzed by taking IEEE Transactions on Education (IEEE T Educ) as a case; for examining the various trends that have been emerging over time. Based on various criteria of authorship and citation, an effort is made to highlight the main contributors or top authors of this engineering education community. It was found that authorship trends have been shifting more towards collaboration. It was also found that the authorship community is growing, both in terms of publications and publishing authors. Study of citation patterns during the last decade, reveals a high citation count per article, which indicates a high readership of this journal. Later, the study of authorship and citation patterns shed light on the trend that multi-author articles are cited more often than single-author articles. This study was compared with earlier studies in the field of Engineering Education Research (EER) using keyword analysis and temporal evolution and distribution of keywords. Additionally key-phrase and topic modeling was performed to identify leading and evolving research areas within the EER., Analysis of word co-occurrence was performed to discover the main context in which the keywords have been used. Lastly, topic modeling techniques were applied for probabilistic distributions of IEEE topics and the results were in line with earlier studies.

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Section: ) Frequency Analysismentioning

confidence: 99%

Authorship and Content Analysis of Engineering Education Research: A Case Study

Nawaz

2016

Int. J. Eng. Ped.

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“…Duvvuru et al [3,17] analysed networks of co-occurring keywords in scholarly articles and monitored the evolution in time of the link weights for detecting research trends and emerging research areas. However, as pointed out by previous works [18], keywords tend to be noisy and do not always represent research topics.…”

Section: Related Workmentioning

confidence: 99%

“…There are already several approaches capable of detecting novel topics and research trends [2][3][4], which rely on statistical techniques to analyse the impact of either labels or distributions of words associated to topics. However, all these approaches are able to recognise topics only in the two aforementioned phases; that is, when they are already established and associated with a substantial number of publications and when the communities of researchers have already reached a consensus for a label.…”

Section: Introductionmentioning

confidence: 99%

Detection of Embryonic Research Topics by Analysing Semantic Topic Networks

Salatino

Motta

2016

Semantics, Analytics, Visualization. Enhancing Scholarly Data

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Being aware of new research topics is an important asset for anybody involved in the research environment, including researchers, academic publishers and institutional funding bodies. In recent years, the amount of scholarly data available on the web has increased steadily, allowing the development of several approaches for detecting emerging research topics and assessing their trends. However, current methods focus on the detection of topics which are already associated with a label or a substantial number of documents. In this paper, we address instead the issue of detecting embryonic topics, which do not possess these characteristics yet. We suggest that it is possible to forecast the emergence of novel research topics even at such early stage and demonstrate that the emergence of a new topic can be anticipated by analysing the dynamics of pre-existing topics. We present an approach to evaluate such dynamics and an experiment on a sample of 3 million research papers, which confirms our hypothesis. In particular, we found that the pace of collaboration in sub-graphs of topics that will give rise to novel topics is significantly higher than the one in the control group.

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“…Traditional methods, such as the manual exploration of publications in significant conferences and journals, are no longer viable. This has led to the emergence of several approaches capable of detecting novel topics and research trends (Bolelli, Ertekin & Giles, 2009;Duvvuru, Kamarthi & Sultornsanee, 2012;He et al, 2009;Wu, Venkatramanan & Chiu, 2016). However, all of these approaches focus on topics that are already associated with a number of publications and consistently referred to by a community of researchers.…”

Section: Introductionmentioning

confidence: 99%

How are topics born? Understanding the research dynamics preceding the emergence of new areas

Salatino

Osborne

Motta

2017

PeerJ Computer Science

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The ability to promptly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. While the literature describes several approaches which aim to identify the emergence of new research topics early in their lifecycle, these rely on the assumption that the topic in question is already associated with a number of publications and consistently referred to by a community of researchers. Hence, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. In this paper, we begin to address this challenge by performing a study of the dynamics preceding the creation of new topics. This study indicates that the emergence of a new topic is anticipated by a significant increase in the pace of collaboration between relevant research areas, which can be seen as the 'parents' of the new topic. These initial findings (i) confirm our hypothesis that it is possible in principle to detect the emergence of a new topic at the embryonic stage, (ii) provide new empirical evidence supporting relevant theories in Philosophy of Science, and also (iii) suggest that new topics tend to emerge in an environment in which weakly interconnected research areas begin to cross-fertilise.

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Undercovering research trends: Network analysis of keywords in scholarly articles

Cited by 23 publications

References 14 publications

Authorship and Content Analysis of Engineering Education Research: A Case Study

Authorship and Content Analysis of Engineering Education Research: A Case Study

Detection of Embryonic Research Topics by Analysing Semantic Topic Networks

How are topics born? Understanding the research dynamics preceding the emergence of new areas

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