Title :
Toward Predicting Collective Behavior via Social Dimension Extraction
Author :
Tang, Lei ; Liu, Huan
Author_Institution :
Arizona State Univ., Tempe, AZ, USA
Abstract :
The SocioDim framework demonstrates promising results toward predicting collective behavior. However, many challenges require further research. For example, networks in social media are continually evolving, with new members joining a network and new connections established between existing members each day. This dynamic nature of networks entails efficient update of the model for collective behavior prediction. It is also intriguing to consider temporal fluctuation into the problem of collective behavior prediction.
Keywords :
behavioural sciences computing; social networking (online); SocioDim framework; collective behavior; social media; Advertising; Data mining; Facebook; Large-scale systems; MySpace; Social network services; Supervised learning; Twitter; Videos; YouTube; behavior prediction; collective behavior; edge-centric clustering; intelligent systems; node-centric clustering; social dimensions; social media;
Journal_Title :
Intelligent Systems, IEEE
DOI :
10.1109/MIS.2010.36