Ting Hua, Wei Wang, Chang-Tien Lu, Naren Ramakrishnan


Storyline detection aims to connect seemly irrelevant single documents into meaningful chains, which provides opportunities for understanding how events evolve over time and what triggers such evolutions. Most previous work generated the storylines through unsupervised methods that can hardly reveal underlying factors driving the evolution process. This paper introduces a Bayesian model to generate storylines from massive documents and infer the corresponding hidden relations and topics. In addition, our model is the first attempt that utilizes Twitter data as human input to ``supervise'' the generation of storylines. Through extensive experiments, we demonstrate our proposed model can achieve significant improvement over baseline methods and can be used to discover interesting patterns for real world cases.


Naren Ramakrishnan

Chang-Tien Lu

Ting Hua

Wei Wang

Publication Details

Date of publication:
October 24, 2016
ACM International Conference on Information Managment