Unsupervised Spatial Event Detection in Targeted Domains with Applications to Civil Unrest Modeling
Abstract
Twitter has become a popular data source as a surrogate for monitoring and detecting events. Targeted domains such as crime, election, and social unrest require the creation of algorithms capable of detecting events pertinent to these domains. Due to the unstructured language, short-length messages, dynamics, and heterogeneity typical of Twitter data streams, it is technically difficult and labor-intensive to develop and maintain supervised learning systems. We present a novel unsupervised approach for detecting spatial events in targeted domains and illustrate this approach using one specific domain, viz. civil unrest modeling. Given a targeted domain, we propose a dynamic query expansion algorithm to iteratively expand domain-related terms, and generate a tweet homogeneous graph. An anomaly identification method is utilized to detect spatial events over this graph by jointly maximizing local modularity and spatial scan statistics. Extensive experiments conducted in 10 Latin American countries demonstrate the effectiveness of the proposed approach.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 28, 2014
- Accession Number
- AD1067206
Entities
People
- Chang-tien Lu
- Feng Chen
- Jing Dai
- Liang Zhao
- Naren Ramakrishnan
- Ting Hua
Organizations
- Department of Computer Science, University of Oxford