Toward a Real-Time Service for Landslide Detection: Augmented Explicit Semantic Analysis and Clustering Composition Approaches

The use of Social Media for event detection, such as detection of natural disasters, has gained a booming interest from research community as Social Media has become an immensely important source of real-time information. However, it poses a number of challenges with respect to high volume, noisy information and lack of geo-tagged data. Extraction of high quality information (e.g., Accurate locations of events) while maintaining good performance (e.g., Low latency) are the major problems. In this paper, we propose two approaches for tackling these issues: an augmented Explicit Semantic Analysis approach for rapid classification and a composition of clustering algorithms for location estimation. Our experiments demonstrate over 98% in precision, recall and F-measure when classifying Social Media data while producing a 20% improvement in location estimation due to clustering composition approach. We implement these approaches as part of the landslide detection service LITMUS, which is live and openly accessible for continued evaluation and use.