Box Office Buzz: Does Social Media Data Steal the Show from Model Uncertainty When Forecasting for Hollywood? / Steven Lehrer, Tian Xie.

Format:

Book

Author/Creator:

Lehrer, Steven.

Contributor:

National Bureau of Economic Research.

Xie, Tian.

Series:

Working Paper Series (National Bureau of Economic Research) no. w22959.

NBER working paper series no. w22959

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Other Title:

Box Office Buzz

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2016.

Summary:

Substantial excitement currently exists in industry regarding the potential of using analytic tools to measure sentiment in social media messages to help predict individual reactions to a new product, including movies. However, the majority of models subsequently used for forecasting exercises do not allow for model uncertainty. Using data on the universe of Twitter messages, we use an algorithm that calculates the sentiment regarding each film prior to, and after its release date via emotional valence to understand whether these opinions affect box office opening and retail movie unit (DVD and Blu-Ray) sales. Our results contrasting eleven different empirical strategies from econometrics and penalization methods indicate that accounting for model uncertainty can lead to large gains in forecast accuracy. While penalization methods do not outperform model averaging on forecast accuracy, evidence indicates they perform just as well at the variable selection stage. Last, incorporating social media data is shown to greatly improve forecast accuracy for box-office opening and retail movie unit sales.

Notes:

Print version record

December 2016.