Jian Cui (Indiana University Bloomington)
Twitter has been recognized as a highly valuable source for security practitioners, offering timely updates on breaking events and threat analyses. Current methods for automating event detection on Twitter rely on standard text embedding techniques to cluster tweets. However, these methods are not effective as standard text embeddings are not specifically designed for clustering security-related tweets. To tackle this, our paper introduces a novel method for creating custom embeddings that improve the accuracy and comprehensiveness of security event detection on Twitter. This method integrates patterns of security-related entity sharing between tweets into the embedding process, resulting in higher-quality embeddings that significantly enhance precision and coverage in identifying security events.