Predicting the Future With

Social Media

by

Sitaram Asur, Bernardo A. Huberma

2010 IEEE/WIC/ACM International Conference

Web Intelligence and Intelligent Agent Technology (WI-IAT)

Group 5

Why Social Media?

Why Social Media ?

• Social Networking has attracted everyone
• Large Content Sharing Medium
• This data source is largely untapped

Social Media Examples

Social Media has EVERYTHING

• Environment
• Politics
• Technology
• Sports
• Entertainment Industry (Movies)

Source and Spread of Content

• People create own content
• Share others content
• Either way the content spreads among their friends
• This widespread contents (opinions/ views) can be an important source of information for predictions.

How effective is social Media for future prediction ?

• Can we correlate tweet rate with the success of a movie in box office
• Based on previous movies and twitter chatter about them, can we create a reliable classifer
• Can we do better predictions by coupling it with sentiment analysis

Related Work

•     Studied sales spike based on online chatter
  •     Found outcome of carefully constructed queries can predict market trends
• Other works involving movie success prediction require metadata such as movies genre, MPAA rating, running time, release data
• Some works tried to correlate sentiments with box-office scores

Twitter

• Extremely Popular
• Microblogging Service
• Content is in the form of Tweets
• Tweet is a short message (140 characters)
• Tweet can consist of 
  •     Text
  •     Links to Images, Video and Articles

Twitter

• Retweet is a post originally made by one user that is
  forwarded by another user.
• Twitter is a potential market for viral marketing.
• Due to its huge reach, a number of businesses use Twitter to advertise products and disseminate information to stakeholders

Dataset

• 2.89 Million Tweets
• 1.2 Million Users
• 24 Different Movies
• Collected using Twitter Search API
• Contains Text, Timestamp and User Info

Dataset

• Movies are released mostly on Fridays and rarely on Wednesdays
• Average of 2 movie releases per week
• Data collected over 3 months (24 movies)

Data Consistency

• Data Consistency: 
  • ​​Movie released only on Fridays are considered
  • Movie released on large number of theatres are considered

Movie List

• Sherlock Homes
• Avatar
• Daybreakers
• Legion
• Leap Year
• Twilight: New Moon
• Spy Next Door
• When In Rome

Movie Resolution

• Movie titled "2012"
• It is hard to classify 2012 as the title of the movie or year
• So, sanity checks have been performed to remove such conflicting movies

Critical Period

• The time from the week before it is released
  • when the promotional campaigns are in full swing
• to two weeks after release 
  • when its initial popularity fades and opinions from people have been disseminated

Time-series of tweets over the critical period for different movies

Number of tweets per unique authors for different movies

 Log distribution of authors and tweets.

Linear Regression

• Linear Fitting
• Relating one known variable with a unknown variable
• R-square (Co-efficient of determination)
• p-value

Attention and Popularity

• Pre-release attention
  • Includes promos, trailers, pictures
  • Most tweets should be URL based
  • Retweets should follow the same pattern
• Post release chatter

Attention and Popularity

• Pre-release attention
•  
•  
•  
• But is there a correlation between number of tweets with URLs and movie success?

Attention and Popularity

Some Positive correlation but co-efficient of determination is low

Prediction of Box-office revenues

• Using the tweets referring to movies prior to their release, can we accurately predict the box-office revenue generated by the movie in its opening weekend?

Tweet rate and box office gross

• The correlation of the average tweet rate with the box-office gross for the 24 movies considered showed a strong positive correlation, with a correlation coeffi- cient value of 0.90

• Transylmania with 2.75 tweet per hour grossed only $263K

• Twilight and Avatar having more than 1k tweet per hour grossed142M and 72M 

Comparison with HSX

• Hollywood Stock Exchange is virtual stock market for movies

• Players can buy "shares" in movies, actors, directors etc

• Stock prices are adjusted based on the gross income

• Earlier studies have shown correlation between HSX index and movie success in Box office

Comparison with HSX

Text

Predicting HSX index

Predicting for any week

Text

Sentiment Analysis

• Sentiments to forecast the box-office values
  • Positive
  • Negative
  • Neutral
• LingPipe Sentiment Analysis Classifier

Supervised Learning

• How to find the class descriptor ?
  • Amazon Mechanical Turk (Manual Classification)
  • Thousands of workers were employed to manually classify all the tweets
  • Tweets with unanimous classification were taken to train the model

Preprocessing

• Elimination of stop-words
• Elimination of all special characters except exclamation marks which were replaced by < EX > and question marks (< QM >)
• Removal of urls and user-ids
• Replacing the movie title with < MOV >

Movie Subjectivity

• More value for sentiments after the movie release.
• Positive sentiments - recommendations by people
• To capture this subjectivity we define

Movie Subjectivity

Movie Polarity

• Movie with more positive tweets than negative is likely successful. So we define
•  
• The Blind Side (5.02 to 9.65) - 34M to 40.1M
• New Moon(6.29 to 5) - 142M to 42M

Movie Polarity

Results of Regression Experiments

Text

Conclusion

• Social media can be utilized to forecast future outcomes.
• Constructed a linear regression model for
  predict box-office revenues of movies in advance of their release.
• Analyzed the sentiments present in tweets and demonstrated their efficacy.
• This method can be extended to a large panoply of topics eg: future product rating, election outcomes.

THANK YOU

Questions?