Trendi: Tracking stories in news and microblogs via emerging, evolving and fading topics

Xuchao Zhang, Liang Zhao, Zhiqian Chen, Arnold Boediardjo, Jing Dai

Abstract

n today's era of information overload, people are struggling to detect the evolution of hot topics from massive news media and microblogs such as Twitter. Reports from mainstream news agencies and discussions from microblogs could complement each other to form a complete picture of major events. Existing work has generally focused on a single source, seldom attempting to combine multiple sources to track the evolution of topics: emerging, evolving and fading phrases as this would require a considerably more sophisticated model. This paper proposes a novel story discovery model that integrates evolutionary topics in news and Twitter data sources using an incremental algorithm by 1) discovering complementary information from news and microblogs that provides a more complete view of major events; 2) modeling emerging, evolving and fading topics and features throughout ongoing events; and 3) creating a scalable algorithm that is capable of handling massive data from news and social media. The parameters of the new model are optimized using a novel algorithm based on the alternative direction method of multipliers (ADMM). Extensive experimental evaluations on multiple datasets from different domains demonstrate the effectiveness and efficiency of our proposed approach.

Xuchao Zhang, Liang Zhao, Zhiqian Chen, Arnold P. Boedihardjo, Jing Dai, Chang-Tien Lu: Trendi: Tracking stories in news and microblogs via emerging, evolving and fading topics. IEEE BigData 2017: 1590-1599