Predicting the Future With Social Media

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

Dataset Characteristics

Dataset Characteristics

Dataset Characteristics

Dataset Characteristics

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

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• Comparison with Hollywood Stock Exchange

Attention and Popularity• Prediction revenues for a given weekend– Using Tweet-rate timeseries + thcnt

Sentiment Analysis• Tweets are classified into Positive, Negative or Neu-

tral

Sentiment Analysis

Sentiment Analysis

Attention and Popularity• Prediction revenues for a second weekend

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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