Introduction• Goal– How buzz and attention is created for different movies and
how that changes over time
– How sentiments are created, how that propagates and how they influence people
• Hypothesis– Well-talked movies will be well-watched
Prior Work• Using meta-data information of movie
– Genre– MPAA rating– Running time– Release date– Number of screen– Actor– Director– Etc
• W. Zhang and S. Skiena. “Improving movie gross prediction through news analysis.” In Web Intelligence, 2009– Used a news aggregation model along with IMDB data
Dataset Characteristics• 2.89 million tweets / 24 different movies / 3 months
• Critical Period
re-lease
1 week
2 week
Attention and Popularity• Prior to the release of a movie
– Expect the tweets to consist promotional campaign– Tweets and retweets referring to particular urls(photo, trailers, …)
Attention and Popularity• Correlation between urls and retweets with the box-office rev-
enues
• Tweet-rate
Attention and Popularity• Correlation between avg tweet-rate and BO revenues = 0.9
• Strong linear relationship => linear regression model
• Prediction of first weekend Box-office revenues
Attention and Popularity• Prediction revenues for a given weekend– Using Tweet-rate timeseries + thcnt
Conclusion• Social media feeds can be effective indicators of real-
world performance
• Tweet-rates can be used to build a powerful model for predicting movie box-office revenue
• Sentiment in tweets can improve box-office revenue prediction after the movies are released
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