Making large-scale support vector machine learning practical
Advances in kernel methods
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
The dynamics of viral marketing
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Proceedings of the first workshop on Online social networks
Twitter power: Tweets as electronic word of mouth
Journal of the American Society for Information Science and Technology
Characterizing debate performance via aggregated twitter sentiment
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Expert Systems with Applications: An International Journal
Pre-release box-office success prediction for motion pictures
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Data from Online Social Networks (OSNs) are providing analysts with an unprecedented access to public opinion on elections, news, movies, etc. However, caution must be taken to determine whether and how much of the opinion extracted from OSN user data is indeed reflective of the opinion of the larger online population. In this work we study this issue in the context of movie reviews on Twitter and compare the opinion of Twitter users with that of IMDb and Rotten Tomatoes. We introduce metrics to quantify how Twitter users can be characteristically different from general users, both in their rating and their relative preference for Oscar-nominated and non-nominated movies. We also investigate whether such data can truly predict a movie's box-office success.