Title :
Nonparametric Bayes Pachinko Allocation for super-event detection in Twitter
Author_Institution :
UCLA Center for Digital Humanities, Los Angeles, CA, USA
Abstract :
Event detection in social media has been an area of considerable research in the past five years. Most models used in event detection, however, assume that events will be bursty and discrete. What does not exist, as of yet, is a model for discovering overlapping events. This paper offers a model for automatically detecting long-lasting super-events that contain smaller sub-events. This model refines Diao and Jiang´s unified model for events and topics in Twitter, supplemented with the Nonparametric Pachinko Allocation Method.
Keywords :
Bayes methods; resource allocation; social networking (online); Twitter; nonparametric Bayes Pachinko allocation method; overlapping events; social media; super-event detection;
Conference_Titel :
TENCON 2014 - 2014 IEEE Region 10 Conference
Conference_Location :
Bangkok
Print_ISBN :
978-1-4799-4076-9
DOI :
10.1109/TENCON.2014.7022492