# Analyzing the Perceived Severity of Cybersecurity Threats Reported on Social Media

arXiv: https://arxiv.org/abs/1902.10680 · HTML: https://mlsanity.com/paper/1902.10680/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

## Built on by (most cited first, 6 of 6)

- [Generative AI for Requirements Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2409.06741/) (2024)
- [CVSS-BERT: Explainable Natural Language Processing to Determine the Severity of a Computer Security Vulnerability from its Description](https://mlsanity.com/paper/2111.08510/) (2021)
- [Measuring, Modeling, and Helping People Account for Privacy Risks in Online Self-Disclosures with AI](https://mlsanity.com/paper/2412.15047/) (2024)
- [Ignore Me But Don't Replace Me: Utilizing Non-Linguistic Elements for Pretraining on the Cybersecurity Domain](https://mlsanity.com/paper/2403.10576/) (2024)
- [A Framework for Unsupervised Classificiation and Data Mining of Tweets about Cyber Vulnerabilities](https://mlsanity.com/paper/2104.11695/) (2021)
- [A Survey of Social Cybersecurity: Techniques for Attack Detection, Evaluations, Challenges, and Future Prospects](https://mlsanity.com/paper/2504.04311/) (2025)

## Abstract (arXiv)

Breaking cybersecurity events are shared across a range of websites, including security blogs (FireEye, Kaspersky, etc.), in addition to social media platforms such as Facebook and Twitter. In this paper, we investigate methods to analyze the severity of cybersecurity threats based on the language that is used to describe them online. A corpus of 6,000 tweets describing software vulnerabilities is annotated with authors' opinions toward their severity. We show that our corpus supports the development of automatic classifiers with high precision for this task. Furthermore, we demonstrate the value of analyzing users' opinions about the severity of threats reported online as an early indicator of important software vulnerabilities. We present a simple, yet effective method for linking software vulnerabilities reported in tweets to Common Vulnerabilities and Exposures (CVEs) in the National Vulnerability Database (NVD). Using our predicted severity scores, we show that it is possible to achieve a Precision@50 of 0.86 when forecasting high severity vulnerabilities, significantly outperforming a baseline that is based on tweet volume. Finally we show how reports of severe vulnerabilities online are predictive of real-world exploits.