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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.
INTRODUCTIONSocial media has exploded as a category of online discourse where people create content, share it, bookmark it and network at a prodigious rate. Examples include Facebook, MySpace, Digg, Twitter and JISC listservs on the academic side. Because of its ease of use, speed and reach, social media is fast changing the public discourse in society and setting trends and agendas in topics that range from the environment and politics to technology and the entertainment industry.Since social media can also be construed as a form of collective wisdom, we decided to investigate its power at predicting real-world outcomes. Surprisingly, we discovered that the chatter of a community can indeed be used to make quantitative predictions that outperform those of artificial markets. These information markets generally involve the trading of state-contingent securities, and if large enough and properly designed, they are usually more accurate than othertechniques for extracting diffuse information, such as surveys and opinions polls. Specifically, the prices in these markets have been shown to have strong correlations with observed outcome frequencies, and thus are good indicators of future outcomes , .
In the case of social media, the enormity and high variance of the information that propagates through large user communities presents an interesting opportunity for harnessing that data into a form that allows for specific predictions about particular outcomes, without having to institute market mechanisms. One can also build models to aggregate the opinions of the collective population and gain useful insights into their behavior, while predicting future trends. Moreover, gathering information on how people converse regarding particular products can be helpful when designing marketing and advertising campaigns , .This paper reports on such a study. Specifically we consider the task of predicting box-office revenues for movies using the chatter from Twitter, one of the fastest growing social networks in the Internet. Twitter 1 , a micro-blogging network, has experienced a burst of popularity in recent months leading to a huge user-base, consisting of several tens of millions of users who actively participate in the creation and propagation of content.We have focused on movies in this study for two main reasons.• The topic of movies is of considerable interest among the social media user community, characterized both by large number of users discussing movies, as well as a substantial variance in their opinions.• The real-world outcomes can be easily observed from box-office revenue for movies. Our goals in this paper are as follows. First, we assess how buzz and attention is created for different movies and how that changes over time. Movie producers spend a lot of effort and money in publicizing their movies, and have also embraced the Twitter medium for this purpose. We then focus on the mechanism of viral marketing and pre-release hype on Twitter, and the role that attention plays in forecasting real-world boxoffice performance. Our hypothesis is that movies that are well talked about will be well-watched.Next, we study how sentiments are created, how positive and negative opinions propagate and how they influence people. For a bad movie, the initial reviews might be enough to discourage others from watching it, while on the other hand, it is possible for interest to be generated by positive reviews and opinions over time. For this purpose, we perform sentiment analysis on the data, using text classifiers to distinguish positively oriented tweets from negative.Our chief conclusions are as follows:• We show that social media feeds can be effective indicators of real-world performance.• We discovered that the rate at which movie tweets are generated can be used to build a powerful model for predicting movie box-office revenue. Moreover ourpredictions are consistently better than those produced by an information market such as the Hollywood Stock Exchange, the gold standard in the industry .1http://www.twitter.com• Our analysis of the sentiment content in the tweets shows that they can improve box-office revenue predictions based on tweet rates only after the movies are released.This paper is organized as follows. Next, we survey recent related work. We then provide a short introduction to Twitter and the dataset that we collected. In Section 5, we study how attention and popularity are created and how they evolve. We then discuss our study on using tweets from Twitterfor predicting movie performance. In Section 6, we present our analysis on sentiments and their effects.
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