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A Multifaceted Approach to Social Multimedia-Based Prediction of Elections
You, Quanzeng; Cao, Liangliang; Cong Y(丛杨); Zhang, Xianchao; Luo JB(罗杰波)
Department机器人学研究室
Source PublicationIEEE TRANSACTIONS ON MULTIMEDIA
ISSN1520-9210
2015
Volume17Issue:12Pages:2271-2280
Indexed BySCI ; EI
EI Accession number20161102101849
WOS IDWOS:000365315500014
Contribution Rank3
KeywordData Mining Election Prediction Social Multimedia Vector Auto-regression
AbstractCompared with real-world polling, election prediction based on social media can be far more timely and cost-effective due to the immediate availability of fast evolving Web contents. However, information from social media may suffer from noise and sampling bias that are caused by various factors and thus pose one of biggest challenges in social media-based data analytics. This paper presents a new model, named competitive vector auto regression (CVAR), to build a reliable forecasting system for the US presidential elections and US House race. Our CVAR model is designed to analyze the correlation between image-centric social multimedia and real-world phenomena. By introducing the competition mechanism, CVAR compares the popularity among multiple competing candidates. More importantly, CVAR is able to combine visual information with textual information from rich and multifaceted social multimedia, which helps extract reliable signals and mitigate sampling bias. As a result, our proposed system can 1) accurately predict the election outcome, 2) infer the sentiment of the candidate photos shared in the social media communities, and 3) account for the sentiment of viewer comments towards the candidates on the related images. The experiments on the 2012 US presidential election at both national and state levels, as well as the 2014 US House race, have demonstrated the power and promise of the proposed approach.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS KeywordPRESIDENTIAL-ELECTION ; FUNDAMENTALS
WOS Research AreaComputer Science ; Telecommunications
Citation statistics
Cited Times:10[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/17437
Collection机器人学研究室
Corresponding AuthorYou, Quanzeng
Affiliation1.Department of Computer Science, University of Rochester, Rochester, NY, United States
2.Electrical Engineering and Computer Sciences Department, Columbia University, New York, NY, United States
3.Yahoo Labs, New York, NY, United States
4.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
5.School of Software, Dalian University of Technology, Dalian, China
Recommended Citation
GB/T 7714
You, Quanzeng,Cao, Liangliang,Cong Y,et al. A Multifaceted Approach to Social Multimedia-Based Prediction of Elections[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2015,17(12):2271-2280.
APA You, Quanzeng,Cao, Liangliang,Cong Y,Zhang, Xianchao,&Luo JB.(2015).A Multifaceted Approach to Social Multimedia-Based Prediction of Elections.IEEE TRANSACTIONS ON MULTIMEDIA,17(12),2271-2280.
MLA You, Quanzeng,et al."A Multifaceted Approach to Social Multimedia-Based Prediction of Elections".IEEE TRANSACTIONS ON MULTIMEDIA 17.12(2015):2271-2280.
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