Liang Zhao, Feng Chen, Jing Dai, Ting Hua, Chang-Tien Lu, Naren Ramakrishnan

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.

People

Ramakrishnan-updated

Naren Ramakrishnan


nvc-3

Liang Zhao


ctlu-updated

Chang-Tien Lu


nvc-4

Ting Hua


Feng-updated

Feng Chen


Publication Details

Date of publication:
October 28, 2014
Journal:
PLOS