全球智库信息集成服务系统(G2TT): Box Office Buzz: Does Social Media Data Steal the Show from Model Uncertainty When Forecasting for Hollywood?

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/w22959
来源ID	Working Paper 22959
	Box Office Buzz: Does Social Media Data Steal the Show from Model Uncertainty When Forecasting for Hollywood?
	Steven Lehrer; Tian Xie
发表日期	2016-12-26
出版年	2016
语种	英语
摘要	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.
主题	Econometrics ; Estimation Methods ; Other ; Accounting, Marketing, and Personnel
URL	https://www.nber.org/papers/w22959
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/580633
推荐引用方式 GB/T 7714	Steven Lehrer,Tian Xie. Box Office Buzz: Does Social Media Data Steal the Show from Model Uncertainty When Forecasting for Hollywood?. 2016.

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文件名称/大小	资源类型	版本类型	开放类型	使用许可
w22959.pdf（373KB）	智库出版物		限制开放	CC BY-NC-SA	浏览