DocumentCode :
3607856
Title :
A Multifaceted Approach to Social Multimedia-Based Prediction of Elections
Author :
Quanzeng You ; Liangliang Cao ; Yang Cong ; Xianchao Zhang ; Jiebo Luo
Author_Institution :
Dept. of Comput. Sci., Univ. of Rochester, Rochester, NY, USA
Volume :
17
Issue :
12
fYear :
2015
Firstpage :
2271
Lastpage :
2280
Abstract :
Compared 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.
Keywords :
Internet; data analysis; forecasting theory; multimedia computing; regression analysis; social networking (online); social sciences computing; vectors; CVAR; US House race; US presidential elections; Web contents; competitive vector auto regression; correlation analysis; data analytics; election outcome prediction; election prediction; forecasting system; image-centric social multimedia; multifaceted approach; multifaceted social multimedia; real-world phenomena; sampling bias; social media communities; Autoregressive processes; Multimedia communication; Predictive models; Reliability; Sentiment analysis; Social network services; Twitter; Voting; Data mining; election; prediction; social multimedia; vector auto-regression;
fLanguage :
English
Journal_Title :
Multimedia, IEEE Transactions on
Publisher :
ieee
ISSN :
1520-9210
Type :
jour
DOI :
10.1109/TMM.2015.2487863
Filename :
7293668
Link To Document :
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