A Study from the VAST Box Office Challenge

Social media presents a promising, albeit challenging, source of data for business intelligence. Customers voluntarily discuss products and companies, giving a real-time pulse of brand sentiment and adoption. Unfortunately, such data is noisy and unstructured, making it difficult to easily extract real-time intelligence. So, using such data can be time-consuming and cost prohibitive for businesses. One promising direction is to apply visual analytics (VA). Recently, the VA community has begun focusing on extracting knowledge from unstructured social media data. Studies have ranged from geotemporal anomaly detection to topic extraction to customer sentiment analysis. The development of tools for such analyses now lets users explore this rich information source and mine it for business intelligence. One key area for business intelligence is revenue prediction. In particular, owing to the abundance of social media discussions on movies, movie revenue prediction has drawn much attention from both the movie industry and academia. Prediction methods have employed movie metadata, social media data, and Google search volumes (for some examples, see the “Related Work” sidebar). Such methods have demonstrated the benefits of extracting business intelligence from social media for predicting movie revenue. However, they’ve relied solely on automated extraction and knowledge prediction. We’ve developed a VA toolkit for predicting opening-weekend revenue and viewer-rating scores of upcoming movies. It consists of a Webdeployable series of linked visualization views that combine data mining with statistical techniques. To demonstrate our toolkit’s effectiveness, we report on the results of the 2013 Visual Analytics Science and Technology (VAST) Box Office Challenge (www.boxofficevast.org/vast-welcome. html). These results also let us explore the hypothesis that VA can help users develop better movie revenue predictions, compared to a purely statistical solution. Such a VA approach for social media analysis and forecasting is directly applicable to a wide range of business intelligence problems. Understanding how information spreads, as well as the underlying sentiment of the messages being spread, can give analysts critical insight into the general pulse of their brand or product. Developing a set of quick-look visualization tools for an overview of such social media data and linking these tools to models that business analysts generate for deploying new products, advertising campaigns, and sales forecasts can be crucial. Our toolkit can also be used to explore other business-related social media data—for example, to see how well an ad campaign did and the pattern of information spreading. Some exploration can help adjust business decisions.