Identifying Hot Information Security Topics Using LDA and Multivariate Mann-Kendall Test

Discovering promising research themes in a scientific domain by evaluating semantic information extracted from bibliometric databases represents a challenging task for Natural Language Processing (NLP). While existing NLP methods generally characterize the research topics using unique key terms, we...

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Bibliographic Details
Published inIEEE access Vol. 11; pp. 18374 - 18384
Main Authors Curiac, Christian-Daniel, Micea, Mihai V.
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Bibliometrics
Data mining
Domains
Information security
LDA topic modeling
Metadata
Modelling
Multivariate analysis
multivariate Mann–Kendall test
Natural language processing
paper metadata
research theme
research trend
Security
Time series analysis
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Summary:Discovering promising research themes in a scientific domain by evaluating semantic information extracted from bibliometric databases represents a challenging task for Natural Language Processing (NLP). While existing NLP methods generally characterize the research topics using unique key terms, we take a step further by more accurately modeling the research themes as finite sets of key terms. The proposed approach involves two stages: identifying the research themes from paper metadata using LDA topic modeling; and, evaluation of research theme trends by employing a version of the Mann-Kendall test that is able to cope with multivariate time series of term occurrences. The results obtained by applying this general methodology to Information Security domain confirm its viability.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3247588