# A Framework for Unsupervised Classificiation and Data Mining of Tweets about Cyber Vulnerabilities

arXiv: https://arxiv.org/abs/2104.11695 · HTML: https://mlsanity.com/paper/2104.11695/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 23 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Processing Tweets for Cybersecurity Threat Awareness](https://mlsanity.com/paper/1904.02072/) (2019)
- [Analyzing the Perceived Severity of Cybersecurity Threats Reported on Social Media](https://mlsanity.com/paper/1902.10680/) (2019)
- [Cyberthreat Detection from Twitter using Deep Neural Networks](https://mlsanity.com/paper/1904.01127/) (2019)
- [Deep Learning Approach for Enhanced Cyber Threat Indicators in Twitter Stream](https://mlsanity.com/paper/2004.00503/) (2020)

## Built on by (most cited first, 1 of 1)

- [Elevating Intrusion Detection and Security Fortification in Intelligent Networks through Cutting-Edge Machine Learning Paradigms](https://mlsanity.com/paper/2512.19037/) (2025)

## Abstract (arXiv)

Many cyber network defense tools rely on the National Vulnerability Database (NVD) to provide timely information on known vulnerabilities that exist within systems on a given network. However, recent studies have indicated that the NVD is not always up to date, with known vulnerabilities being discussed publicly on social media platforms, like Twitter and Reddit, months before they are published to the NVD. To that end, we present a framework for unsupervised classification to filter tweets for relevance to cyber security. We consider and evaluate two unsupervised machine learning techniques for inclusion in our framework, and show that zero-shot classification using a Bidirectional and Auto-Regressive Transformers (BART) model outperforms the other technique with 83.52% accuracy and a F1 score of 83.88, allowing for accurate filtering of tweets without human intervention or labelled data for training. Additionally, we discuss different insights that can be derived from these cyber-relevant tweets, such as trending topics of tweets and the counts of Twitter mentions for Common Vulnerabilities and Exposures (CVEs), that can be used in an alert or report to augment current NVD-based risk assessment tools.