ARSA: a sentiment-aware model for predicting sales performance using blogs
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Modeling and predicting group activity over time in online social media
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Scalable learning of collective behavior based on sparse social dimensions
Proceedings of the 18th ACM conference on Information and knowledge management
Using a model of social dynamics to predict popularity of news
Proceedings of the 19th international conference on World wide web
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
Predicting popular messages in Twitter
Proceedings of the 20th international conference companion on World wide web
Don't turn social media into another 'Literary Digest' poll
Communications of the ACM
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Tweets and Votes: A Study of the 2011 Singapore General Election
HICSS '12 Proceedings of the 2012 45th Hawaii International Conference on System Sciences
Identifying and following expert investors in stock microblogs
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Credibility ranking of tweets during high impact events
Proceedings of the 1st Workshop on Privacy and Security in Online Social Media
Using proximity to predict activity in social networks
Proceedings of the 21st international conference companion on World Wide Web
A predictive model for the temporal dynamics of information diffusion in online social networks
Proceedings of the 21st international conference companion on World Wide Web
Predicting IMDB movie ratings using social media
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Predicting collective sentiment dynamics from time-series social media
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
Correlating S&P 500 stocks with Twitter data
Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research
Topic evolution prediction of user generated contents considering enterprise generated contents
Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research
Predicting aggregate social activities using continuous-time stochastic process
Proceedings of the 21st ACM international conference on Information and knowledge management
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As the importance and popularity of online social media has become more obvious, there are more researches aiming at making use of information from them. One important topic of this is predicting the future with social media. This paper focuses on predicting box offices using microblog. Compared with previous work which makes use of the count of related microblogs simply, the information from social media has been utilized more deeply in this paper. Two sets of features have been extracted: count based features and content based features. For the former, the information in the aspect of users, which decrease the influence of garbage microblogs, has been exploited. For content based features, a new box office oriented semantic classification method has been provided to make the features more relative with box offices. Meanwhile, more complex machine learning models such as SVM and neutral network have been applied to the prediction method. Our prediction model is more accurate and reliable. With our prediction method, the data in Tencent microblog has been utilized to predict box offices of certain movies in China. With the results, the strength of our method and predictive power of online social media can be completely demonstrated.