Predicting the Future With Social Media
Sitaram Asur
Social Computing Lab
HP Labs
Palo Alto, California
Email: sitaram.asur@hp.com
Bernardo A. Huberman
Social Computing Lab
HP Labs
Palo Alto, California
Email: bernardo.huberman@hp.com
Abstract—In recent years, social media has become ubiquitous
and important for social networking and content sharing. And
yet, the content that is generated from these websites remains
largely untapped. In this paper, we demonstrate how social media
content can be used to predict real-world outcomes. In particular,
we use the chatter from Twitter.com to forecast box-office
revenues for movies. We show that a simple model built from
the rate at which tweets are created about particular topics can
outperform market-based predictors. We further demonstrate
how sentiments extracted from Twitter can be further utilized to
improve the forecasting power of social media.
I. INTRODUCTION
Social media has exploded as a category of online discourse
where people create content, share it, bookmark it and network
at a prodigious rate. Examples include Facebook, MySpace,
Digg, Twitter and JISC listservs on the academic side. Because
of its ease of use, speed and reach, social media is fast
changing the public discourse in society and setting trends
and agendas in topics that range from the environment and
politics to technology and the entertainment industry.
Since social media can also be construed as a form of
collective wisdom, we decided to investigate its power at
predicting real-world outcomes. Surprisingly, we discovered
that the chatter of a community can indeed be used to make
quantitative predictions that outperform those of artificial
markets. These information markets generally involve the
trading of state-contingent securities, and if large enough and
properly designed, they are usually more accurate than other
techniques for extracting diffuse information, such as surveys
and opinions polls. Specifically, the prices in these markets
have been shown to have strong correlations with observed
outcome frequencies, and thus are good indicators of future
outcomes [4], [5].
In the case of social media, the enormity and high vari-
ance of the information that propagates through large user
communities presents an interesting opportunity for harnessing
that data into a form that allows for specific predictions
about particular outcomes, without having to institute market
mechanisms. One can also build models to aggregate the
opinions of the collective population and gain useful insights
into their behavior, while predicting future trends. Moreover,
gathering information on how people converse regarding par-
ticular products can be helpful when designing marketing and
advertising campaigns [1], [3].
This paper reports on such a study. Specifically we consider
the task of predicting box-office revenues for movies using
the chatter from Twitter, one of the fastest growing social
networks in the Internet. Twitter 1, a micro-blogging network,
has experienced a burst of popularity in recent months leading
to a huge user-base, consisting of several tens of millions of
users who actively participate in the creation and propagation
of content.
We have focused on movies in this study for two main
reasons.
• The topic of movies is of considerable interest among
the social media user community, characterized both by
large number of users discussing movies, as well as a
substantial variance in their opinions.
• The real-world outcomes can be easily observed from
box-office revenue for movies.
Our goals in this paper are as follows. First, we assess how
buzz and attention is created for different movies and how that
changes over time. Movie producers spend a lot of effort and
money in publicizing their movies, and have also embraced
the Twitter medium for this purpose. We then focus on the
mechanism of viral marketing and pre-release hype on Twitter,
and the role that attention plays in forecasting real-world box-
office performance. Our hypothesis is that movies that are well
talked about will be well-watched.
Next, we study how sentiments are created, how positive and
negative opinions propagate and how they influence people.
For a bad movie, the initial reviews might be enough to
discourage others from watching it, while on the other hand, it
is possible for interest to be generated by positive reviews and
opinions over time. For this purpose, we perform sentiment
analysis on the data, using text classifiers to distinguish
positively oriented tweets from negative.
Our chief conclusions are as follows:
• We show that social media feeds can be effective indica-
tors of real-world performance.
• We discovered that the rate at which movie tweets
are generated can be used to build a powerful model
for predicting movie box-office revenue. Moreover our
predictions are consistently better than those produced
by an information market such as the Hollywood Stock
Exchange, the gold standard in the industry [4].
1http://www.twitter.com
• Our analysis of the sentiment content in the tweets shows
that they can improve box-office revenue predictions
based on tweet rates only after the movies are released.
This paper is organized as follows. Next, we survey recent
related work. We then provide a short introduction to Twitter
and the dataset that we collected. In Section 5, we study how
attention and popularity are created and how they evolve.
We then discuss our study on using tweets from Twitter
for predicting movie performance. In Section 6, we present
our analysis on sentiments and their effects. We conclude
in Section 7. We describe our prediction model in a general
context in the Appendix.
II. RELATED WORK
Although Twitter has been very popular as a web service,
there has not been considerable published research on it.
Huberman and others [2] studied the social interactions on
Twitter to reveal that the driving process for usage is a sparse
hidden network underlying the friends and followers, while
most of the links represent meaningless interactions. Java et
al [7] investigated community structure and isolated different
types of user intentions on Twitter. Jansen and others [3]
have examined Twitter as a mechanism for word-of-mouth
advertising, and considered particular brands and products
while examining the structure of the postings and the change in
sentiments. However the authors do not perform any analysis
on the predictive aspect of Twitter.
There has been some prior work on analyzing the correlation
between blog and review mentions and performance. Gruhl
and others [9] showed how to generate automated queries
for mining blogs in order to predict spikes in book sales.
And while there has been research on predicting movie
sales, almost all of them have used meta-data information
on the movies themselves to perform the forecasting, such
as the movies genre, MPAA rating, running time, release
date, the number of screens on which the movie debuted,
and the presence of particular actors or actresses in the cast.
Joshi and others [10] use linear regression from text and
metadata features to predict earnings for movies. Mishne and
Glance [15] correlate sentiments in blog posts with movie
box-office scores. The correlations they observed for positive
sentiments are fairly low and not sufficient to use for predictive
purposes. Sharda and Delen [8] have treated the prediction
problem as a classification problem and used neural networks
to classify movies into categories ranging from ’flop’ to
’blockbuster’. Apart from the fact that they are predicting
ranges over actual numbers, the best accuracy that their model
can achieve is fairly low. Zhang and Skiena [6] have used
a news aggregation model along with IMDB data to predict
movie box-office numbers. We have shown how our model
can generate better results when compared to their method.
III. TWITTER
Launched on July 13, 2006, Twitter 2 is an extremely
popular online microblogging service. It has a very large user
2http://www.twitter.com
base, consisting of several millions of users (23M unique users
in Jan 3). It can be considered a directed social network, where
each user has a set of subscribers known as followers. Each
user submits periodic status updates, known as tweets, that
consist of short messages of maximum size 140 characters.
These updates typically consist of personal information about
the users, news or links to content such as images, video
and articles. The posts made by a user are displayed on the
user’s profile page, as well as shown to his/her followers. It is
also possible to send a direct message to another user. Such
messages are preceded by @userid indicating the intended
destination.
A retweet is a post originally made by one user that is
forwarded by another user. These retweets are a popular means
of propagating interesting posts and links through the Twitter
community.
Twitter has attracted lots of attention from corporations
for the immense potential it provides for viral marketing.
Due to its huge reach, Twitter is increasingly used by news
organizations to filter news updates through the community.
A number of businesses and organizations are using Twitter
or similar micro-blogging services to advertise products and
disseminate information to stakeholders.
IV. DATASET CHARACTERISTICS
The dataset that we used was obtained by crawling hourly
feed data from Twitter.com. To ensure that we obtained all
tweets referring to a movie, we used keywords present in the
movie title as search arguments. We extracted tweets over
frequent intervals using the Twitter Search Api 4, thereby
ensuring we had the timestamp, author and tweet text for
our analysis. We extracted 2.89 million tweets referring to 24
different movies released over a period of three months.
Movies are typically released on Fridays, with the exception
of a few which are released on Wednesday. Since an average of
2 new movies are released each week, we collected data over
a time period of 3 months from November to February to have
sufficient data to measure predictive behavior. For consistency,
we only considered the movies released on a Friday and only
those in wide release. For movies that were initially in limited
release, we began collecting data from the time it became
wide. For each movie, we define the critical period as 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.
Some details on the movies chosen and their release dates
are provided in Table 1. Note that, some movies that were
released during the period considered were not used in this
study, simply because it was difficult to correctly identify
tweets that were relevant to those movies. For instance,
for the movie 2012, it was impractical to segregate tweets
talking about the movie, from those referring to the year. We
3http://blog.compete.com/2010/02/24/compete-ranks-top-sites-for-january-
2010/
4http://search.twitter.com/api/
Movie Release Date
Armored 2009-12-04
Avatar 2009-12-18
The Blind Side 2009-11-20
The Book of Eli 2010-01-15
Daybreakers 2010-01-08
Dear John 2010-02-05
Did You Hear About The Morgans 2009-12-18
Edge Of Darkness 2010-01-29
Extraordinary Measures 2010-01-22
From Paris With Love 2010-02-05
The Imaginarium of Dr Parnassus 2010-01-08
Invictus 2009-12-11
Leap Year 2010-01-08
Legion 2010-01-22
Twilight : New Moon 2009-11-20
Pirate Radio 2009-11-13
Princess And The Frog 2009-12-11
Sherlock Holmes 2009-12-25
Spy Next Door 2010-01-15
The Crazies 2010-02-26
Tooth Fairy 2010-01-22
Transylmania 2009-12-04
When In Rome 2010-01-29
Youth In Revolt 2010-01-08
TABLE I
NAMES AND RELEASE DATES FOR THE MOVIES WE CONSIDERED IN OUR
ANALYSIS.
have taken care to ensure that the data we have used was
disambiguated and clean by choosing appropriate keywords
and performing sanity checks.
2 4 6 8 10 12 14 16 18 20
500
1000
1500
2000
2500
3000
3500
4000
4500
release weekend weekend 2
Fig. 1. Time-series of tweets over the critical period for different movies.
The total data over the critical period for the 24 movies
we considered includes 2.89 million tweets from 1.2 million
users.
Fig 1 shows the timeseries trend in the number of tweets
for movies over the critical period. We can observe that the
busiest time for a movie is around the time it is released,
following which the chatter invariably fades. The box-office
revenue follows a similar trend with the opening weekend
generally providing the most revenue for a movie.
Fig 2 shows how the number of tweets per unique author
changes over time. We find that this ratio remains fairly
consistent with a value between 1 and 1.5 across the critical
2 4 6 8 10 12 14 16 18 20
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2
Days
T
w
ee
ts
p
er
a
ut
ho
rs
Release weekend
Fig. 2. Number of tweets per unique authors for different movies
0 1 2 3 4 5 6 7 8
0
2
4
6
8
10
12
14
log(tweets)
lo
g(
fre
qu
en
cy
)


Fig. 3. Log distribution of authors and tweets.
period. Fig 3 displays the distribution of tweets by different
authors over the critical period. The X-axis shows the number
of tweets in the log scale, while the Y-axis represents the
corresponding frequency of authors in the log scale. We can
observe that it is close to a Zipfian distribution, with a few
authors generating a large number of tweets. This is consistent
with observed behavior from other networks [12]. Next, we
examine the distribution of authors over different movies. Fig 4
shows the distribution of authors and the number of movies
they comment on. Once again we find a power-law curve, with
a majority of the authors talking about only a few movies.
V. ATTENTION AND POPULARITY
We are interested in studying how attention and popularity
are generated for movies on Twitter, and the effects of this
attention on the real-world performance of the movies consid-
ered.
A. Pre-release Attention:
Prior to the release of a movie, media companies and and
producers generate promotional information in the form of
trailer videos, news, blogs and photos. We expect the tweets
for movies before the time of their release to consist primarily
of such promotional campaigns, geared to promote word-of-
mouth cascades. On Twitter, this can be characterized by
2 4 6 8 10 12 14 16 18 20 22 24
0
1
2
3
4
5
6
7
8
9
10
x 105
Number of Movies
A
ut
ho
rs
Fig. 4. Distribution of total authors and the movies they comment on.
Features Week 0 Week 1 Week 2
url 39.5 25.5 22.5
retweet 12.1 12.1 11.66
TABLE II
URL AND RETWEET PERCENTAGES FOR CRITICAL WEEK
tweets referring to particular urls (photos, trailers and other
promotional material) as well as retweets, which involve users
forwarding tweet posts to everyone in their friend-list. Both
these forms of tweets are important to disseminate information
regarding movies being released.
First, we examine the distribution of such tweets for dif-
ferent movies, following which we examine their correlation
with the performance of the movies.
2 4 6 8 10 12 14 16 18 20 22 24
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Movies
Tw
ee
ts
w
ith
u
rls
(p
er
ce
nt
ag
e)


Week 0
Week 1
Week 2
Fig. 5. Percentages of urls in tweets for different movies.
Table 2 shows the percentages of urls and retweets in the
Features Correlation R2
url 0.64 0.39
retweet 0.5 0.20
TABLE III
CORRELATION AND R2 VALUES FOR URLS AND RETWEETS BEFORE
RELEASE.
Features Adjusted R2 p-value
Avg Tweet-rate 0.80 3.65e-09
Tweet-rate timeseries 0.93 5.279e-09
Tweet-rate timeseries + thcnt 0.973 9.14e-12
HSX timeseries + thcnt 0.965 1.030e-10
TABLE IV
COEFFICIENT OF DETERMINATION (R2) VALUES USING DIFFERENT
PREDICTORS FOR MOVIE BOX-OFFICE REVENUE FOR THE FIRST WEEKEND.
tweets over the critical period for movies. We can observe that
there is a greater percentage of tweets containing urls in the
week prior to release than afterwards. This is consistent with
our expectation. In the case of retweets, we find the values to
be similar across the 3 weeks considered. In all, we found the
retweets to be a significant minority of the tweets on movies.
One reason for this could be that people tend to describe their
own expectations and experiences, which are not necessarily
propaganda.
We want to determine whether movies that have greater
publicity, in terms of linked urls on Twitter, perform better in
the box office. When we examined the correlation between the
urls and retweets with the box-office performance, we found
the correlation to be moderately positive, as shown in Table
3. However, the adjusted R2 value is quite low in both cases,
indicating that these features are not very predictive of the
relative performance of movies. This result is quite surprising
since we would expect promotional material to contribute
significantly to a movie’s box-office income.
B. Prediction of first weekend Box-office revenues
Next, we investigate the power of social media in predicting
real-world outcomes. Our goal is to observe if the knowledge
that can be extracted from the tweets can lead to reasonably
accurate prediction of future outcomes in the real world.
The problem that we wish to tackle can be framed as
follows. 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?
0 2 4 6 8 10 12 14 16
x 107
0
5
10
15
x 107
Predicted Box−office Revenue
A
ct
ua
l r
ev
en
ue


Tweet−rate
HSX
Fig. 6. Predicted vs Actual box office scores using tweet-rate and HSX
predictors
To use a quantifiable measure on the tweets, we define the
tweet-rate, as the number of tweets referring to a particular
movie per hour.
Tweet− rate(mov) = |tweets(mov)||Time (in hours)| (1)
Our initial analysis of 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. This suggests a strong linear relationship
among the variables considered. Accordingly, we constructed
a linear regression model using least squares of the average
of all tweets for the 24 movies considered over the week
prior to their release. We obtained an adjusted R2 value
of 0.80 with a p-value of 3.65e − 09 ∗ ∗∗, where the ’***’
shows significance at 0.001, indicating a very strong predictive
relationship. Notice that this performance was achieved using
only one variable (the average tweet rate). To evaluate our
predictions, we employed real box-office revenue information,
extracted from the Box Office Mojo website 5.
The movie Transylmania that opened on Dec 4th had
easily the lowest tweet-rates of all movies considered. For
the week prior to its release, it received on an average 2.75
tweets per hour. As a result of this lack of attention, the
movie captured the record for the lowest-grossing opening for
a movie playing at over 1,000 sites, making only $263,941
in its opening weekend, and was subsequently pulled from
theaters at the end of the second week. On the other end
of the spectrum, two movies that made big splashes in their
opening weekends, Twilight:New Moon (making 142M) and
Avatar(making 77M) had, for their pre-release week, averages
of 1365.8 and 1212.8 tweets per hour respectively. This once
again illustrates the importance of attention in social media.
Next, we performed a linear regression of the time series
values of the tweet-rate for the 7 days before the release.
We used 7 variables each corresponding to the tweet-rate
for a particular day. An additional variable we used was the
number of theaters the movies were released in, thcnt. The
results of the regression experiments are shown in Table 4.
Note that, in all cases, we are using only data available prior
to the release to predict box-office for the opening weekend.
Comparison with HSX:
To compare with our tweet-based model, we used the Hol-
lywood Stock Exchange index. The fact that artificial online
markets such as the Foresight Exchange and the Hollywood
Stock Exchange are good indicators of future outcomes has
been shown previously [4], [5]. The prices in these markets
have been shown to have strong correlations with observed
outcome frequencies. In the case of movies, the Hollywood
Stock Exchange (http://www.hsx.com/), is a popular play-
money market, where the prices for movie stocks can ac-
curately predict real box office results. Hence, to compare
with our tweet-rate predictor, we considered regression on
5http://boxofficemojo.com
Predictor AMAPE Score
Regnobudget+nReg1w 3.82 96.81
Avg Tweet-rate + thcnt 1.22 98.77
Tweet-rate Timeseries + thcnt 0.56 99.43
TABLE V
AMAPE AND SCORE VALUE COMPARISON WITH EARLIER WORK.
the movie stock prices from the Hollywood Stock Exchange,
which can be considered the gold standard [4].
From the results in Table 4, it can be seen that our
regression model built from social media provides an
accurate prediction of movie performances at the box
office. Furthermore, the model built using the tweet rate
timeseries outperforms the HSX-based model. The graph
outlining the predicted and actual values of this model is also
shown in Fig 6, outlining the utility of harvesting social media.
Comparison with News-based Prediction:
In earlier work, Zhang and others [6] have developed a
news-based model for predicting movie revenue. The best-
performing method in the aforementioned work is the com-
bined model obtained by using predictors from IMDB and
news. The corresponding R2 value for this combined model
is 0.788, which is far lower than the ones obtained by
our predictors. We computed the AMAPE (Adjusted Mean
Absolute Percentage/Relative Error) measure, that the authors
use, for our data. The comparative values are shown in Table
5. We can observe that our values are far better than the ones
reported in the earlier work. Note however, that since historical
information on tweets are not available, we were able to use
data on only the movies we have collected, while the authors
in the earlier paper have used a larger database of movies for
their analysis.
C. Predicting HSX prices
Given that social media can accurately predict box office
results, we also tested their efficacy at forecasting the stock
prices of the HSX index. At the end of the first weekend,
the Hollywood stock exchange adjusts the price for a movie
stock to reflect the actual box office gross. If the movie does
not perform well, the price goes down and vice versa. We
conducted an experiment to see if we could predict the price
of the HSX movie stock at the end of the opening weekend
for the movies we have considered. We used the historical
HSX prices as well as the tweet-rates, individually, for the
week prior to the release as predictive variables. The response
variable was the adjusted price of the stock. We also used
the theater count as a predictor in both cases, as before. The
results are summarized in Table 6. As is apparent, the tweet-
rate proves to be significantly better at predicting the actual
values than the historical HSX prices. This again illustrates
the power of the buzz from social media.
Predictor Adjusted R2 p− value
HSX timeseries + thcnt 0.95 4.495e-10
Tweet-rate timeseries + thnt 0.97 2.379e-11
TABLE VI
PREDICTION OF HSX END OF OPENING WEEKEND PRICE.
Predictor Adjusted R2 p− value
Avg Tweet-rate 0.79 8.39e-09
Avg Tweet-rate + thcnt 0.83 7.93e-09
Avg Tweet-rate + PNratio 0.92 4.31e-12
Tweet-rate timeseries 0.84 4.18e-06
Tweet-rate timeseries + thcnt 0.863 3.64e-06
Tweet-rate timeseries + PNratio 0.94 1.84e-08
TABLE VIII
PREDICTION OF SECOND WEEKEND BOX-OFFICE GROSS
Weekend Adjusted R2
Jan 15-17 0.92
Jan 22-24 0.97
Jan 29-31 0.92
Feb 05-07 0.95
TABLE VII
COEFFICIENT OF DETERMINATION (R2) VALUES USING TWEET-RATE
TIMESERIES FOR DIFFERENT WEEKENDS
D. Predicting revenues for all movies for a given weekend
Until now, we have considered the problem of predicting
opening weekend revenue for movies. Given the success of
the regression model, we now attempt to predict revenue for
all movies over a particular weekend. The Hollywood Stock
Exchange de-lists movie stocks after 4 weeks of release, which
means that there is no timeseries available for movies after
4 weeks. In the case of tweets, people continue to discuss
movies long after they are released. Hence, we attempt to use
the timeseries of tweet-rate, over 7 days before the weekend,
to predict the box-office revenue for that particular weekend.
Table 7 shows the results for 3 weekends in January and
1 in February. Note, that there were movies that were two
months old in consideration for this experiment. Apart from
the time series, we used two additional variables - the theater
count and the number of weeks the movie has been released.
We used the coefficient of determination (adjusted R2) to
evaluate the regression models. From Table 7, we find that
the tweets continue to be good predictors even in this case,
with an adjusted R2 consistently greater than 0.90. The results
have shown that the buzz from social media can be accurate
indicators of future outcomes. The fact that a simple linear
regression model considering only the rate of tweets on movies
can perform better than artificial money markets, illustrates the
power of social media.
VI. SENTIMENT ANALYSIS
Next, we would like to investigate the importance of sen-
timents in predicting future outcomes. We have seen how
efficient the attention can be in predicting opening weekend
box-office values for movies. Hence we consider the problem
of utilizing the sentiments prevalent in the discussion for
forecasting.
Sentiment analysis is a well-studied problem in linguistics
and machine learning, with different classifiers and language
models employed in earlier work [13], [14]. It is common
to express this as a classification problem where a given
text needs to be labeled as Positive, Negative or Neutral.
Here, we constructed a sentiment analysis classifier using the
LingPipe linguistic analysis package 6 which provides a set
of open-source java libraries for natural language processing
tasks. We used the DynamicLMClassifier which is a language
model classifier that accepts training events of categorized
character sequences. Training is based on a multivariate es-
timator for the category distribution and dynamic language
models for the per-category character sequence estimators.
To obtain labeled training data for the classifier, we utilized
workers from the Amazon Mechanical Turk 7. It has been
shown that manual labeling from Amazon Turk can correlate
well with experts [11]. We used thousands of workers to assign
sentiments for a large random sample of tweets, ensuring that
each tweet was labeled by three different people. We used
only samples for which the vote was unanimous as training
data. The samples were initially preprocessed in the following
ways:
• 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 >
We used the pre-processed samples to train the classifier using
an n-gram model. We chose n to be 8 in our experiments.
The classifier was trained to predict three classes - Positive,
Negative and Neutral. When we tested on the training-set with
6http://www.alias-i.com/lingpipe
7https://www.mturk.com/
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Movie Subjectivity
Week 0 Week 1 Week 2
Fig. 7. Movie Subjectivity values
0
2
4
6
8
10
12
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Movie Polarity
Week 0 Week 1 Week 2
Fig. 8. Movie Polarity values
cross-validation, we obtained an accuracy of 98%. We then
used the trained classifier to predict the sentiments for all the
tweets in the critical period for all the movies considered.
A. Subjectivity
Our expectation is that there would be more value for
sentiments after the movie has released, than before. We
expect tweets prior to the release to be mostly anticipatory
and stronger positive/negative tweets to be disseminated later
following the release. Positive sentiments following the release
can be considered as recommendations by people who have
seen the movie, and are likely to influence others from
watching the same movie. To capture the subjectivity, we
defined a measure as follows.
Subjectivity =
|Positive and Negative Tweets|
|Neutral Tweets| (2)
When we computed the subjectivity values for all the movies,
we observed that our hypothesis was true. There were more
sentiments discovered in tweets for the weeks after release,
than in the pre-release week. Fig 7 shows the ratio of subjec-
tive to objective tweets for all the movies over the three weeks.
We can observe that for most of the movies, the subjectivity
increases after release.
Variable p− value
(Intercept) 0.542
Avg Tweet-rate 2.05e-11 (***)
PNRatio 9.43e-06 (***)
TABLE IX
REGRESSION USING THE AVERAGE TWEET-RATE AND THE POLARITY
(PNRATIO). THE SIGNIFICANCE LEVEL (*:0.05, **: 0.01, ***: 0.001) IS
ALSO SHOWN.
B. Polarity
To quantify the sentiments for a movie, we measured the
ratio of positive to negative tweets. A movie that has far more
positive than negative tweets is likely to be successful.
PNratio =
|Tweets with Positive Sentiment|
|Tweets with Negative Sentiment| (3)
Fig 8 shows the polarity values for the movies considered
in the critical period. We find that there are more positive
sentiments than negative in the tweets for almost all the
movies. The movie with the enormous increase in positive
sentiment after release is The Blind Side (5.02 to 9.65). The
movie had a lukewarm opening weekend sales (34M) but then
boomed in the next week (40.1M), owing largely to positive
sentiment. The movie New Moon had the opposite effect. It
released in the same weekend as Blind Side and had a great
first weekend but its polarity reduced (6.29 to 5), as did its
box-office revenue (142M to 42M) in the following week.
Considering that the polarity measure captured some vari-
ance in the revenues, we examine the utility of the sentiments
in predicting box-office sales. In this case, we considered
the second weekend revenue, since we have seen subjectivity
increasing after release. We use linear regression on the
revenue as before, using the tweet-rate and the PNratio as an
additional variable. The results of our regression experiments
are shown in Table 8. We find that the sentiments do provide
improvements, although they are not as important as the rate
of tweets themselves. The tweet-rate has close to the same
predictive power in the second week as the first. Adding the
sentiments, as an additional variable, to the regression equation
improved the prediction to 0.92 while used with the average
tweet-rate, and 0.94 with the tweet-rate timeseries. Table 9
shows the regression p-values using the average tweet rate
and the sentiments. We can observe that the coefficients are
highly significant in both cases.
VII. CONCLUSION
In this article, we have shown how social media can be
utilized to forecast future outcomes. Specifically, using the
rate of chatter from almost 3 million tweets from the popular
site Twitter, we constructed a linear regression model for
predicting box-office revenues of movies in advance of their
release. We then showed that the results outperformed in
accuracy those of the Hollywood Stock Exchange and that
there is a strong correlation between the amount of attention
a given topic has (in this case a forthcoming movie) and
its ranking in the future. We also analyzed the sentiments
present in tweets and demonstrated their efficacy at improving
predictions after a movie has released.
While in this study we focused on the problem of predicting
box office revenues of movies for the sake of having a clear
metric of comparison with other methods, this method can be
extended to a large panoply of topics, ranging from the future
rating of products to agenda setting and election outcomes. At
a deeper level, this work shows how social media expresses a
collective wisdom which, when properly tapped, can yield an
extremely powerful and accurate indicator of future outcomes.
VIII. APPENDIX: GENERAL PREDICTION MODEL FOR
SOCIAL MEDIA
Although we focused on movie revenue prediction in this
paper, the method that we advocate can be extended to other
products of consumer interest.
We can generalize our model for predicting the revenue
of a product using social media as follows. We begin with
data collected regarding the product over time, in the form
of reviews, user comments and blogs. Collecting the data
over time is important as it can measure the rate of chatter
effectively. The data can then be used to fit a linear regression
model using least squares. The parameters of the model
include:
• A : rate of attention seeking
• P : polarity of sentiments and reviews
• D : distribution parameter
Let y denote the revenue to be predicted and  the error. The
linear regression model can be expressed as :
y = βa ∗A+ βp ∗ P + βd ∗D +  (4)
where the β values correspond to the regression coefficients.
The attention parameter captures the buzz around the product
in social media. In this article, we showed how the rate of
tweets on Twitter can capture attention on movies accurately.
We found this coefficient to be the most significant in our
experiments. The polarity parameter relates to the opinions
and views that are disseminated in social media. We observed
that this gains importance after the movie has been released
and adds to the accuracy of the predictions. In the case of
movies, the distribution parameter is the number of theaters a
particular movie is released in. In the case of other products,
it can reflect their availability in the market.
IX. ACKNOWLEDGEMENT
This material is based upon work supported by the National
Science Foundation under Grant # 0937060 to the Computing
Research Association for the CIFellows Project.
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