Liang Zhao, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan

Abstract

Twitter has become a popular social sensor. It is socially significant to surveil the tweet content under crucial themes such as "disease" and "civil unrest". However, this creates two challenges: 1) how to characterize the theme pattern, given Twitter's heterogeneity, dynamics, and unstructured language; and 2) how to model the theme consistently across multiple Twitter functions such as hashtags, replying, and friendships. In this paper, we propose a dynamic query expansion (DQE) model for theme tracking in Twitter. Specifically, DQE characterizes the theme consistency among heterogeneous entities (e.g., terms, tweets, and users) through semantic and social relationships, including co-occurrence, replying, authorship, and friendship. The proposed new optimization algorithm estimates the weight of each relationship by minimizing the Kullback-Leibler divergence. To demonstrate the effectiveness and scalability of DQE, we conducted extensive experiments to track the theme "civil unrest" across 8 Latin American countries.

People

Ramakrishnan-updated

Naren Ramakrishnan


Feng-updated

Feng Chen


nvc-3

Liang Zhao


ctlu-updated

Chang-Tien Lu


Publication Details

Date of publication:
December 28, 2015
Conference:
IEEE International Conference on Big Data