Predicting the Future With Social Media

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Predicting the Future With Social Media
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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

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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

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Dataset Characteristics

• 2.89 million tweets / 24 different movies / 3
  months
• Critical Period

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Dataset Characteristics
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Dataset Characteristics
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Dataset Characteristics
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Dataset Characteristics
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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, )

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Attention and Popularity
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Attention and Popularity

• Correlation between urls and retweets with the
  box-office revenues
• Tweet-rate

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Attention and Popularity

• Correlation between avg tweet-rate and BO
  revenues 0.9
• Strong linear relationship gt linear regression
  model
• Prediction of first weekend Box-office revenues

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Attention and Popularity

• Comparison with Hollywood Stock Exchange

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Attention and Popularity

• Prediction revenues for a given weekend
• Using Tweet-rate timeseries thcnt

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Sentiment Analysis

• Tweets are classified into Positive, Negative or
  Neutral

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Sentiment Analysis
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Sentiment Analysis
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Attention and Popularity

• Prediction revenues for a second weekend

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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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