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

Abstract

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.INDEX TERMS LDA topic modeling, multivariate Mann-Kendall test, natural language processing, paper metadata, research theme, research trend.

Automating Research Problem Framing and Exploration through Knowledge Extraction from Bibliometric Data

“…As a result, the scientific output is categorized depending on the purpose of its use into either fine-grained themes (e.g., Multi-Factor Authentication or Generative Adversarial Networks) or coarse-grained research areas (e.g., Information Security or Machine Learning) [10]. Specific Natural Language Processing (NLP) techniques are used to support bibliographic data acquisition and preprocessing, key term extraction, key term frequency analysis, topic modeling, and text classification [19][20][21].…”

A hybrid approach for generative process model with topic modelling towards efficient and dynamic document clustering

“…Their research focused on processing of data with preservation of privacy. Curiac and Micea [36] investigated on social media data to identify hot topics on information security. They proposed an LDA based methodology to achieve this.…”