1. Do Professional Critics Diverge from Public Opinion? Evidence from Twitter Yu-Hsi Liu Suffolk University ACEI 2014

2. Motives What is a “good” movie? A high-grossing movie? A movie of high expert opinion? Or a movie with positive word-of-mouth? According to Bourdieu’s (1979) perspective, experts with more social capital display “good taste” while the public display “no taste” or “bad taste”. To test this hypothesis, the empirical literature has investigated the relationship between expert opinion and popular appeal. However, popular appeal doesn’t equal ordinary evaluation. Social network websites provide us a channel to measure ordinary evaluation directly. 2

3. Questions Does expert judgment diverge from ordinary evaluation? How does the gap between expert judgment and ordinary evaluation change over time? Will a positive public evaluation lead to a strong demand? Does ordinary consumers learn from critics? 3

4. Econometric Challenges Is movie criticism an influencer? Or just a predictor which reflect public opinion in advance? Reinstein and Snyder (2005) pointed out that high gross could be a response to high movie quality. It may not be a result of good expert review. The spurious correlation between high market performance and good expert review needs to be cleared. We need to control for movie quality. But movie quality is often unobservable and difficult to measure. 4

5. Econometric Implement A fixed effects model can control the unobservable quality for each movie. But it will omit all the time-constant explanatory factors. First-differencing eliminates the movie-specific factors such as movie quality. Twitter allows me to measure ordinary evaluation on a weekly basis. The weekly gap between expert review and ordinary evaluation can eliminate the unobservable movie quality on an individual movie. 5

6. Hypotheses If ordinary evaluation does not differ from the professional judgment, the professional judgment, Metascore, should have explanatory power on tweet valence. If professional judgment has an influence effect on consumers, Metascore to have more explanatory power on tweet valence during the early weeks than in the later weeks If tweet valence shows consistency with Metascore in the beginning but diverges from Metascore later (saying the gap decreases over time), professional judgment has NO influence effect but prediction effect.

7. Measurements Ordinary evaluation is measured by tweet valence, which is defined as the ratio of the positive tweets to total tweet on a movie. Professional judgment on a movie is measured by Metascore, a movie-variant, time-invariant variable collected from Metacritic.com. Metascore is a weighted average assigning different weights critics from publications and media. The gap between Metascore and valence. The gap is defined as Gap = | (valence*100-Metascore) | 7

8. Data Collection The data comes from the daily counts of tweets on individual movies from May 16 2011 to Aug 19 2011. Link Twitter data with weekend North-American box-office revenue and Metascore. The Twitter data on movies is collected daily by twittercritics.com. Including the daily number of total tweets (by movie), the daily number of positive tweets (by movie), and the daily number of negative tweets (by movie). 57 movies from May 2011 to Aug 2011 in the U.S. theaters. Unbalanced Panel Data: the data set is on movie j ranging from week 1 to week 27. 8

9. The Dynamics of Weekend Gross Box Office Revenue 9

10. Tweets Volume and Weekend Revenue: Bad Teacher (2011) 10

11. The Dynamics of Tweet Valence

12. Data Information

13. Data Information (continued) 285 movies were widely released in U.S. theaters from May 19, 2011 to Aug 19, 2011. 57 movies have tweets data in the same time period. 13 AverageMaxMin 57 Movies in the Twitter Dataset Total Box Office Revenue$90,813,260$366,007,900$1,183,354 Production Cost$73,890,385$260,000,000$1,500,000 * 6 movies out of 57 have their production cost data missing. AverageMaxMin 286 Movies on theater in the same period Total Box Office Revenue$22,043,286$366,007,900$11 Production Cost$49,917,031$260,000,000$135,000 * 189 movies out of 286 have their production cost data missing.

14. All 286 Movies on May 2011 to Aug 2011 57 Movies in the data set

15. The Covariance Matrix MetascoreTweet Valence Gap (without the absolute value) Production Budget(million) Metascore 1 Tweet Valence 0.3951 Gap (without the absolute value) -0.69210.38971 Production Budget (million) -0.165-0.3384-0.10051

16. Econometric Model Public Appeal as a Dependent Variable (1)ln(Rev jt ) = X j β 1 + β 2 ln VOLUME j,t-1 + β 3 VALENCE j,t-1 + β 4 (t*Z j ) + β 5 τ t + η jt (2) ln d jt = ln(Rev jt ) - ln(Rev j,t-1 ) = δ 0 + δ 1 (lnVOLUME j,t-1 - lnVOLUME j,t-2 ) + δ 2 (VALENCE j,t-1 - VALENCE j,t-2 ) + δ 3 Z j + δ 4 T jt +ε jt Ordinary Evaluation as a Dependent Variable (3)VALENCE jt = λ 0 + X j λ 1 + λ 2 Metascore j + λ 3 lnVOLUME jt + λ 4 θ tj + υ jt (4)Gap jt = γ 0 + X j γ 1 + γ 2 Metascore j + γ 3 ln VOLUME jt + γ 4 θ jt + ρ jt,

17. Findings and Implications Metascore is significantly and positively correlated with box- office revenue. Tweet valence is significant to public appeal, both with and without Metascore in the regression. Metascore loses its significance while adding tweet valence into the regression. It could be due to co-linearty or the substitution between Metascore and tweet valence. Either one confirms that professional judgment is consistent with ordinary evaluation.

19. Findings and Implications Metascore is highly significantly correlated with tweet valence, which confirms that professional judgment is consistent with ordinary evaluation. Production budget, which is a proxy of advertisement expenditure, is not significantly correlated with tweet valence. The gap between Metascore and tweet valence is not increasing over time. It suggests that ordinary evaluation converges to professional critics as time gradually, which implies that there is no influence effect.

21. Discussion and Conclusion My results confirm that there are positive correlations between professional judgment, ordinary evaluation and public appeal. The gap between ordinary evaluation and professional judgment decreases over time. Ordinary evaluation is influenced by advertisement in the beginning, but later consumers revise their expectation and converge to experts’ opinion. My evidence suggests that ordinary consumers have good taste. There is little distinction between public opinion and expert judgment, but the influence effect seems to be weak.