2010/03/29 by Jianfeng Guo, Sitaram Asur, Qiang Ji +1 · 1 voice · 1 citation
Computer Science · Economics, Econometrics and Finance · Physics and Astronomy · #Energy, Environment, Economic Growth #Market Dynamics and Volatility #Monetary Policy and Economic Impact #cs.CY #physics.soc-ph
paper · pdf · doi:10.1016/j.apenergy.2013.03.027
arxiv created 2010/03/29 · arxiv published 2010/03/29 · openalex publication_date 2013/04/03 · arxiv updated 2015/03/13 · openalex created_date 2019/07/30 · openalex updated_date 2026/07/28
In recent years, social media has become ubiquitous and important for social networking and content sharing. And yet, the content that is generated from these websites remains largely untapped. In this paper, we demonstrate how social media content can be used to predict real-world outcomes. In particular, we use the chatter from Twitter.com to forecast box-office revenues for movies. We show that a simple model built from the rate at which tweets are created about particular topics can outperform market-based predictors. We further demonstrate how sentiments extracted from Twitter can be further utilized to improve the forecasting power of social media.