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.