Ting Hua, Yue Ning, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan

Abstract

The analysis of interactions between social media and traditional news streams is becoming increasingly relevant for a variety of applications, including: understanding the underlying factors that drive the evolution of data sources, tracking the triggers behind events, and discovering emerging trends.Researchers have explored such interactions by examining volume changes or information diffusions,however, most of them ignore the semantical and topical relationships between news and social media data.Our work is the first attempt to study how news influences social media, or inversely, based on topical knowledge.We propose a hierarchical Bayesian model that jointly models the news and social media topics and their interactions.We show that our proposed model can capture distinct topics for individual datasets as well as discover the topic influences among multiple datasets.By applying our model to large sets of news and tweets, we demonstrate its significant improvement over baseline methods and explore its power in the discovery of interesting patterns for real world cases.

People

nvc-4

Ting Hua


yue ning-updated

Yue Ning


ctlu-updated

Chang-Tien Lu


Feng-updated

Feng Chen


Ramakrishnan-updated

Naren Ramakrishnan


Publication Details

Date of publication:
February 12, 2016
Conference:
The AAAI conference on Artificial Intelligence