Whose and What Chatter Matters? the Effect of Tweets on Movie Sales

Whose and What Chatter Matters? The Eﬀect of Tweets on Movie Sales

Huaxia Rui (joint work with Yizao Liu, and Andrew Whinston)

Simon School of Business University of Rochester

October 6, 2012

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Outline

1 Whose and What Chatter Matters? Motivations Data Model Results TwitterSensor

2 / 25 Whose and What Chatter Matters? The Eﬀect of Tweets on Movie Sales Whose and What Chatter Matters?

Word-of-Mouth (WOM) Research

Word-of-mouth is often considered to be the most credible information source to consumers for the purchase of a new product or new service.

Oﬄine period ( before 2003)

Online period ( since 2004)

Big Data period ( present )

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The Eﬀect of WOM on Product Sales

. Awareness eﬀect vs. Persuasive eﬀect . ..

Awareness eﬀect: the function of spreading basic information about the product among the population.

Persuasive eﬀect: the function of altering people’s preferences toward the product and thus inﬂuencing their purchase decisions......

4 / 25 Whose and What Chatter Matters? The Eﬀect of Tweets on Movie Sales Whose and What Chatter Matters? Motivations Motivation 1

. What Chatter Matters: the Good, the Bad, or the Eager? . ..

“back at work and recovering from #avatar - fantastic movie!”

“I’m just not excited about the new Alice In Wonderland :/ Tim Burton seems to be running out ideas a bit”

“DAMN IT!!! Didn’t make it...Sold out tickets for Avatar!!!” . .. . .

5 / 25 Whose and What Chatter Matters? The Eﬀect of Tweets on Movie Sales Whose and What Chatter Matters? Motivations Motivation 2

. Whose Chatter Matters? . ..

. .. . . “Today a single customer complaint from someone with influence can have more impact on your company’s reputation than your best marketing.” – Jason Duty, head of Dell’s global social outreach service. 1 1 Source: Customer must be king in the web world, Financial Times. 01/25/2012 6 / 25 Whose and What Chatter Matters? The Effect of Tweets on Movie Sales Whose and What Chatter Matters? Motivations Motivation 2 . The Million Followers Fallacy? . .. “The number of Twitter followers (or reach) is usually meaningless.”a “Indegree alone reveals very little about the influence of a user.”b Per Christakis’ anecdotal evidence, Twitter follower/Facebook friend counts are misleading.c Recently, Evan Williams hinted that a simple measure of followers “doesn’t capture your distribution” and follower counts may soon become the second most important number to users.

aAvnit, A. (2009), Berinato, S. (2010) bCha, Haddadi, Benevenuto, and Gummadi (2010) c . Garber, M. (2010) 7 / 25 .. . . Whose and What Chatter Matters? The Eﬀect of Tweets on Movie Sales Whose and What Chatter Matters? Data Why Twitter WOM data?

Twitter is a more natural environment to study the awareness eﬀect of WOM (push vs. pull).

More social network information is available from Twitter.

A new category of WOM: intention WOM.

Volume: 4 million tweets about 63 movies. 12,136 posts used in Liu (2006). 95,867 posts used in Duan, Gu, and Whinston (2008).

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Daily box oﬃce revenue data from BoxOﬃceMojo.com

Tweets from twitter.com collected through Twitter Application Programming Interface (API).

Each tweet: content, time, number of followers. Pre-processing: advertising tweets, irrelevant tweets. Tweet classiﬁcation: intention tweets, positive tweets, negative tweets, neutral tweets.

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10 / 25 Whose and What Chatter Matters? The Effect of Tweets on Movie Sales Whose and What Chatter Matters? Data Intention Classifier . Pattern Matching . .. (plan|need) (to|2) (watch|see|c|catch)( the)* movie (sold|sell) out|no ticket saw|watched|went just really last ...... SVM . .. ∑ Decision function: f (x) = i αi K(xi , x) + b RBF Kernel: K(x, x0) = exp(−γ||x − x0)||2) . .. . 11. / 25 Whose and What Chatter Matters? The Effect of Tweets on Movie Sales Whose and What Chatter Matters? Data Sentiment Classifier . Naive Bayesian Approach . .. C ∗ = argmax P(C |D) Ci i

j=n P(D|C )P(C ) ∏ P(C |D) = i i , P(D|C ) = P(t |C ) i P(D) i j i j=1

Nij + α P(tj |Ci ) = Ni + 2α α: smoothing factor

Nij : number of tweets in class i containing word j.

Ni : number of tweets in class i...... 12 / 25 Whose and What Chatter Matters? The Eﬀect of Tweets on Movie Sales Whose and What Chatter Matters? Data Variables

Gross Revenues Movie gross box oﬃce revenues from Friday to next Thursday Ad Advertising expenditure in a week Tweets Total number of tweets mentioning the name of the movie i in a week (i.e., from this Friday to next Thursday) Type-1 tweets Total number of tweets with followers less than 400 (small audiences) from Friday to next Thursday Type-2 tweets Total number of tweets with followers more than 400 (large audiences) from Friday to next Thursday T2Ratio Ratio of Type-2 tweets in a week IntRatio (%) Ratio of intention tweets in a week PosRatio (%) Ratio of tweets with positive sentiment in a week NegRatio (%) Ratio of tweets with negative sentiment in a week

13 / 25 Whose and What Chatter Matters? The Eﬀect of Tweets on Movie Sales Whose and What Chatter Matters? Model Dynamic Panel Data Model

0 yit = αyi,t−1 + β xi,t−1 + ηi + νit (1)

Revenueit = αRevenuei,t−1 + β0Adi,t−1 + β1Tweetsi,t−1

+ β2T 2Ratioi,t−1 + β3IntRatioi,t−1

+ β4PosRatioi,t−1 + β5NegRatioi,t−1

+ ηi + νit (2)

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0 (yit −yi,t−1) = α(yi,t−1 −yi,t−2)+(xi,t−1 −xi,t−2) β +(νit −νi,t−1)

0 y¯it = αy¯i,t−1 + β x¯i,t−1 +ν ¯it (3) where

y¯it = yit − yi,t−1

x¯i,t−1 = xi,t−1 − xi,t−2

ν¯it = νit − νi,t−1.

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0 0 To estimate δ = (α, β ) , we use yi1, ··· , yi,t−2, xi1, ··· , xi,t−2 as instruments for movie i, period t.

    y¯i,2 x¯i,2 y¯i,3     ¯  . .  ¯  .  Xi = . . , Yi = . , y¯i,T −1 x¯i,T −1 y¯i,T

  yi,1 xi,1 0 0 0 0 ... 0 ... 0 0 ... 0    0 0 yi,1 yi,2 xi,1 x1,2 ... 0 ... 0 0 ... 0  Z =   . i  ......  ...... 0 0 0 0 0 0 ... yi,1 ... yi,T −2 xi,1 ... xi,T −2

16 / 25 Whose and What Chatter Matters? The Eﬀect of Tweets on Movie Sales Whose and What Chatter Matters? Model Estimation

The GMM estimator minimizes the criterion [ ]0 [ ] ∑N ∑N 0 ¯ − ¯ 0 ¯ − ¯ J = Zi (Yi Xi δ) W Zi (Yi Xi δ) (4) i=1 i=1

where W is the weighting matrix and δ = (α, β0)0 is the coeﬃcient vector. Hence, we have the following estimator:

0 0 −1 0 0 δGMM = (X¯ ZWZ X¯ ) X¯ ZWZ Y¯ , (5)

17 / 25 Whose and What Chatter Matters? The Eﬀect of Tweets on Movie Sales Whose and What Chatter Matters? Results Estimation Results

Variable Estimate SD Tweets 5.35∗∗∗ 0.36 T2Ratio 75, 653.54∗∗∗ 18,229.72 IntRatio 154, 698.00∗∗∗ 38,300.25 PosRatio 116, 681∗ 61,798.56 NegRatio −136, 926.9∗ 70445.52 Lag Revenue 0.30∗∗∗ 0.01 Ad 155.1425 203.7851 No. Weekly Observations: 433

Tweets Total number of tweets mentioning movie i in a week T2Ratio Ratio of type 2 tweets in a week IntRatio (%) Ratio of intention tweets in a week PosRatio (%) Ratio of tweets with positive sentiment in a week NegRatio (%) Ratio of tweets with negative sentiment in a week 18 / 25 Whose and What Chatter Matters? The Eﬀect of Tweets on Movie Sales Whose and What Chatter Matters? Results Managerial Implications

Firms interested in the online WOM about their products should actively monitor or even seek WOM messages produced by people with large indegree in the social network.

Companies may carefully monitor people’s intention toward certain products on Twitter and incorporate that information to better forecast future sales.

The dual eﬀect of intention tweets revealed in our study suggests the possibility of targeted advertising on Twitter.

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Individually, each tweet might be inconsequential and“boring”; Collectively, the Twitterverse might reveal interesting patterns.

20 / 25 Figure 1: http://www.twittersensor.com Whose and What Chatter Matters? The Eﬀect of Tweets on Movie Sales Whose and What Chatter Matters? TwitterSensor TwitterSensor

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22 / 25 Figure 2: http://www.twittersensor.com Whose and What Chatter Matters? The Eﬀect of Tweets on Movie Sales Whose and What Chatter Matters? TwitterSensor TwitterSensor

23 / 25 Figure 3: http://www.twittersensor.com Whose and What Chatter Matters? The Eﬀect of Tweets on Movie Sales Whose and What Chatter Matters? TwitterSensor References

Avnit, A. 2009. The Million Followers Fallacy, Internet Draft, Pravda Media. http: // pravdam. com/ 2009/ 08/ 20/ the-million-followers-fallacy-guest-post-by-adi-avnit/ . Berinato, S. 2010. On Twitter, Followers Don’t Equal Influence. http://blogs.hbr.org/research/2010/05/influence-and-twitter.html Cha, M., H. Haddadi, F. Benevenuto, and K.P.Gummadi. 2010. Measuring User Influence in Twitter: The Million Follower Fallacy. Proc. International AAAI Conference on Weblogs and Social Media. Chevalier, J., and D. Mayzlin. 2006. The Effect of Word of Mouth on Sales: Online Book Reviews. Journal of Marketing Research, 43(3), 345-354. Chintagunta, P. K., S. Gopinath, and S. Venkataraman. The Effect of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets. Marketing Science, forthcoming Dhar, V., and E. A. Chang. 2009. Does Chatter Matter? The Impact of User-Generated Content on Music Sales. Journal of Interactive Marketing, 23(4), 300-307. Duan, W., B. Gu, and A. B. Whinston. 2008. Do Online Reviews Matter? An Investigation of Panel Data. Decision Support Systems, 45(4), 1007-1016.

24 / 25 Whose and What Chatter Matters? The Eﬀect of Tweets on Movie Sales Whose and What Chatter Matters? TwitterSensor References

Garber, M. 2010. Nicholas Christakis on the networked nature of Twitter. http://www.niemanlab.org/2010/12/ nicholas-christakis-on-the-networked-nature-of-twitter/ Godes, D., and D. Mayzlin. 2004. Using Online Conversations to Study Word-of-Mouth Communication. Marketing Science, 23(4), 545-560. Granovetter, M. 1973. The Strength of Weak Ties. The American Journal of Sociology, 78, 1360-1380. Olivera, F., P. Goodman, S. Tan. 2008. Contribution Behaviors in Distributed Environments. MIS Quarterly, 32, 23-42. Liu, Y. 2006. Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue. Journal of Marketing, 70, 74-89. Onishi, H., and P. Manchanda. 2010. Marketing Activity, Blogging and Sales. Working paper. Sonnier, G. P., L. McAlister, and O. J. Rutz. 2011. A Dynamic Model of the Effect of Online Communications on Firm Sales. Marketing Science, 30(4), 702-716. Trusov, M., R. E. Bucklin, and K. Pauwels. 2009. Effects of Word-of-Mouth Versus Traditional Marketing: Findings from an Internet Social Networking Site. Journal of Marketing, 73, 90-102.

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