Using Online Conversations to Study Word-of-Mouth Communication
Marketing Science
Journal of Management Information Systems
Movie forecast Guru: A Web-based DSS for Hollywood managers
Decision Support Systems
Do online reviews matter? - An empirical investigation of panel data
Decision Support Systems
Continental Airlines Continues to Soar with Business Intelligence
Information Systems Management
Predicting box-office success of motion pictures with neural networks
Expert Systems with Applications: An International Journal
A Lexicon-Enhanced Method for Sentiment Classification: An Experiment on Online Product Reviews
IEEE Intelligent Systems
Predicting the Future with Social Media
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Proceedings of the 20th international conference on World wide web
Who says what to whom on twitter
Proceedings of the 20th international conference on World wide web
A Dynamic Model of the Effect of Online Communications on Firm Sales
Marketing Science
Fast, Scalable, and Context-Sensitive Detection of Trending Topics in Microblog Post Streams
ACM Transactions on Management Information Systems (TMIS)
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The rise of social media has fundamentally changed the way information is produced, disseminated, and consumed in the digital age, which has profound economic and business effects. Among many different types of social media, social broadcasting networks such as Twitter in the U.S. and “Weibo” in China are particularly interesting from a business perspective. In the case of Twitter, the huge amounts of real-time data with extremely rich text, along with valuable structural information, makes Twitter a great platform to build Business Intelligence (BI) systems. We propose a framework of social-broadcasting-based BI systems that utilizes real-time information extracted from these data with text mining techniques. To demonstrate this framework, we designed and implemented a Twitter-based BI system that forecasts movie box office revenues during the opening weekend and forecasts daily revenue after 4 weeks. We found that incorporating information from Twitter could reduce the Mean Absolute Percentage Error (MAPE) by 44% for the opening weekend and by 36% for total revenue. For daily revenue forecasting, including Twitter information into a baseline model could reduce forecasting errors by 17.5% on average. On the basis of these results, we conclude that social-broadcasting-based BI systems have great potential and should be explored by both researchers and practitioners.