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论文类型： 期刊论文

发表时间： 2015-12-01

发表刊物： IEEE TRANSACTIONS ON MULTIMEDIA

收录刊物： SCIE、EI、Scopus

卷号： 17

期号： 12

页面范围： 2271-2280

ISSN号： 1520-9210

关键字： Data mining; election; prediction; social multimedia; vector auto-regression

摘要： 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.