Disaster Detection Using Text Anomaly Detection in SNS
Published in Korea Next Generation Computing Conference (KINGPC), 2022
This paper presents a novel approach for disaster detection in social media using text anomaly detection techniques. Our method can identify disaster-related events from SNS posts in real-time, enabling early warning systems and rapid response coordination.
The proposed framework leverages text anomaly detection to identify unusual patterns in social media content that may indicate disaster events. By analyzing the linguistic and semantic features of social media posts, our system can detect emerging disasters and provide early warnings to emergency response teams. Experimental results demonstrate the effectiveness of our approach in identifying various types of disasters from social media streams.
Recommended citation: Byounghan Lee, Kyung-Ah Sohn. (2022). "Disaster Detection Using Text Anomaly Detection in SNS." Korea Next Generation Computing Conference (KINGPC), South Korea.