Domain-Independent Detection of Emergency Situations Based on Social Activity Related to Geolocations.

In general, existing methods for automatically detecting emergency situations using Twitter rely on features based on domain-specific keywords found in messages. This type of keyword-based methods usually require training on domain-specific labeled data, using multiple languages, and for different types of events (e.g., earthquakes, floods, wildfires, etc.). In addition to being costly, these approaches may fail to detect previously unexpected situations, such as uncommon catastrophes or terrorist attacks. However, collective mentions of certain keywords are not the only type of self-organizing phenomena that may arise in social media when a real-world extreme situation occurs. Just as nearby physical sensors become activated when stimulated, localized citizen sensors (i.e., users) will also react in a similar manner. To leverage this information, we propose to use self-organized activity related to geolocations to identify emergency situations. We propose to detect such events by tracking the frequencies, and probability distributions of the interarrival time of the messages related to specific locations. Using an off-the-shelf classifier that is independent of domain-specific features, we study and describe emergency situations based solely on location-based features in messages. Our findings indicate that anomalies in location-related social media user activity indeed provide information for automatically detecting emergency situations independent of their domain.