Time-based Gap Analysis of Cybersecurity Trends in Academic and Digital Media
Bibliographic record
Abstract
This study analyzes cybersecurity trends and proposes a conceptual framework to identify cybersecurity topics of social interest and emerging topics that need to be addressed by researchers in the field. The insights drawn from this framework allow for a more proactive approach to identifying cybersecurity patterns and emerging threats that will ultimately improve the collective cybersecurity posture of the modern society. To achieve this, cybersecurity-oriented content in both media and academic corpora, disseminated between 2008 and 2018, were morphologically analyzed via text mining. A total of 3,556 academic papers obtained from the top-10 highly reputable cybersecurity academic conferences, and 4,163 news articles collected from the New York Times were processed. The LDA topic modeling followed optimal perplexity and coherence scores resulted in 12 trendy topics. Next, the time-based gap between these trendy topics was analyzed to measure the correlation between media and trendy academic topics. Both convergences and divergences between the two cybersecurity corpora were identified, suggesting a strong time-based correlation between these resources. This framework demonstrates the effective use of automated techniques to provide insights about cybersecurity topics of social interest and emerging trends and informs the direction of future academic research in this field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".