Predicting the Future With Social Media
Abstract
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
References
Jure Leskovec, Lada A. Adamic and Bernardo A. Huberman. The dynamics of viral marketing. In Proceedings of the 7th ACM Conference on Electronic Commerce, 2006.
Bernardo A. Huberman, Daniel M. Romero, and Fang Wu. Social networks that matter: Twitter under the microscope. First Monday, 14(1), Jan 2009.
B. Jansen, M. Zhang, K. Sobel, and A. Chowdury. Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 2009.
D. M. Pennock, S. Lawrence, C. L. Giles, and F. A˚. Nielsen. The real power of artificial markets. Science, 291(5506):987–988, Jan 2001.
Kay-Yut Chen, Leslie R. Fine and Bernardo A. Huberman. Predicting the Future. Information Systems Frontiers, 5(1):47–61, 2003.
W. Zhang and S. Skiena. Improving movie gross prediction through news analysis. In Web Intelligence, pages 301304, 2009.
Akshay Java, Xiaodan Song, Tim Finin and Belle Tseng. Why we twit-ter: understanding microblogging usage and communities. Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pages 56–65, 2007.
Ramesh Sharda and Dursun Delen. Predicting box-office su ccess of motion pictures with neural networks. Expert Systems with Applications, vol 30, pp 243–254, 2006.
Daniel Gruhl, R. Guha, Ravi Kumar, Jasmine Novak and Andrew Tomkins. The predictive power of online chatter. SIGKDD Conference on Knowledge Discovery and Data Mining, 2005.
Mahesh Joshi, Dipanjan Das, Kevin Gimpel and Noah A. Smith. Movie Reviews and Revenues: An Experiment in Text Regression NAACL-HLT, 2010.
Rion Snow, Brendan O'Connor, Daniel Jurafsky and Andrew Y. Ng. Cheap and Fast - But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks. Proceedings of EMNLP, 2008.
Fang Wu, Dennis Wilkinson and Bernardo A. Huberman. Feeback Loops of Attention in Peer Production. Proceedings of SocialCom-09: The 2009 International Conference on Social Computing, 2009.
Bo Pang and Lillian Lee. Opinion Mining and Sentiment Analysis Foundations and Trends in Information Retrieval, 2(1-2), pp. 1135, 2008.
Namrata Godbole, Manjunath Srinivasaiah and Steven Skiena. Large-Scale Sentiment Analysis for News and Blogs. Proc. Int. Conf. Weblogs and Social Media (ICWSM), 2007.
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