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 challenging task for Natural Language Processing (NLP). While existing NLP methods generally characterize the topics using unique key terms, we take step further more accurately modeling as finite sets of terms. The proposed approach involves two stages: identifying paper metadata LDA topic modeling; and, evaluation theme trends employing version Mann-Kendall test that is able to cope with multivariate time series term occurrences. results obtained applying this general methodology Information Security confirm its viability.