DocumentCode
1467193
Title
Toward Predicting Collective Behavior via Social Dimension Extraction
Author
Tang, Lei ; Liu, Huan
Author_Institution
Arizona State Univ., Tempe, AZ, USA
Volume
25
Issue
4
fYear
2010
Firstpage
19
Lastpage
25
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;
fLanguage
English
Journal_Title
Intelligent Systems, IEEE
Publisher
ieee
ISSN
1541-1672
Type
jour
DOI
10.1109/MIS.2010.36
Filename
5445065
Link To Document