Leveraging Sentiment, Bibliometric and Trend Analysis to Evaluate and Predict Quantum Cybersecurity Research Investments
Open Access DepositedThe exponential rise in unstructured, quantum computing information over the last 20 years has amplified cybersecurity professionals’ need to collect, organize, analyze, and predict future research directions. While quantum computing has no shortage of unstructured data such as journals and conference papers, to our knowledge no efforts have focused on harnessing Natural Language Processing (NLP), more specifically Sentiment Analysis (SA) to predict emerging threats. Our research is tackling that challenge by leveraging sentiment, bibliometric and trend analysis to evaluate and predict quantum cybersecurity threat landscape. Researchers utilized Deep Learning (DL) techniques to analyze digital quantum computing article abstracts from peer-reviewed periodicals across the globe. Large-BERT deep learning models delivered impressive results for the sentiment classification of our quantum computing research abstract data. When compared to Advanced Boolean Logic Search Queries (ABLSQ) and human classification which has been traditionally considered the standard or source of truth, Large-BERT models identified cybersecurity research with sentiments with 92% accuracy. AzureMLBERT appeared to be the best performing Large-BERT model, with the highest scores for accuracy, precision, recall, and F1 scores. Of the quantum computing research abstracts with negative sentiment scores (i.e., < 0.45) and our 8 bibliometric predictors, the model accurately predicted quantum cybersecurity research investments with 72% accuracy. Moreover, the results of the empirical study showed that pertinent information can be found beyond ABLSQ’s centered around cybersecurity terminology. By analyzing quantum computing publication patterns using sentiment analysis, bibliometric predictors, historical data and trends over time, researchers gained insights into which areas are growing and where the cybersecurity research field may be headed in the future. This empirical work aims to contribute to the extant literature in quantum cybersecurity by qualitatively examining the quantum computing threat landscape to enhance researchers understanding of quantum cybersecurity attitudes and investment behavior to improve their data-informed decision capabilities.
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McCarter_gwu_0075A_16562.pdf | 2024-10-02 | Open Access |
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