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 in:IEEE access 2023, Vol.11, p.18374-18384
Main Authors: Curiac, Christian-Daniel, Micea, Mihai V.
Format: Article
Language:English
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