Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data

Mestyán, Márton; Yasseri, Taha; Kertész, János

doi:10.1371/journal.pone.0071226

Cited by 246 publications

(148 citation statements)

References 43 publications

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“…However, the best-known automated methods for films pertain to economic impact, such as the opening weekend or total box office gross. More recently, researchers and film industry professionals have evaluated films using electronic measures, such as Twitter mentions (8) and frequency of Wikipedia edits (9), but these may also be better indicators of impact or popularity. For an automated, objective measure that pertains to a film's influence, we turn to scientific works for an appropriate analog.…”

Section: Significancementioning

confidence: 99%

Cross-evaluation of metrics to estimate the significance of creative works

Wasserman

Zeng

Amaral

2015

Proc. Natl. Acad. Sci. U.S.A.

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In a world overflowing with creative works, it is useful to be able to filter out the unimportant works so that the significant ones can be identified and thereby absorbed. An automated method could provide an objective approach for evaluating the significance of works on a universal scale. However, there have been few attempts at creating such a measure, and there are few “ground truths” for validating the effectiveness of potential metrics for significance. For movies, the US Library of Congress’s National Film Registry (NFR) contains American films that are “culturally, historically, or aesthetically significant” as chosen through a careful evaluation and deliberation process. By analyzing a network of citations between 15,425 United States-produced films procured from the Internet Movie Database (IMDb), we obtain several automated metrics for significance. The best of these metrics is able to indicate a film’s presence in the NFR at least as well or better than metrics based on aggregated expert opinions or large population surveys. Importantly, automated metrics can easily be applied to older films for which no other rating may be available. Our results may have implications for the evaluation of other creative works such as scientific research.

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Section: Significancementioning

confidence: 99%

Cross-evaluation of metrics to estimate the significance of creative works

Wasserman

Zeng

Amaral

2015

Proc. Natl. Acad. Sci. U.S.A.

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“…Social applications include evaluating toponym importance in order to make type size decisions for maps [21], measuring the flow of concepts across the world [22], and estimating the popularity of politicians and political parties [23]. Finally, economic applications include attempts to forecast movie ticket sales [24] and stock prices [25]. The latter two applications are of particular interest because they include a forecasting component, as the present work does.…”

Section: Author Summarymentioning

confidence: 95%

Global Disease Monitoring and Forecasting with Wikipedia

Generous

Fairchild²,

Deshpande³

et al. 2016

OJPHI

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Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with r 2 up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without retraining is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

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“…R Yao and J Chen 15 introduced sentiment analysis and machine learning methods to study the relationship between the online reviews for a movie and its box-office revenue performance. M Mestyan et al 16 proved that the popularity of a movie could be predicted much before its release based on data extracted from the entry to the movie in Wikipedia, the online encyclopedia. S Moon et al 17 applied machine learning techniques and linear modeling to develop a model for predicting the near-weekend ticket sales and the ideal number of screens using web-based external factors, such as online reviews, star ratings, and search volume.…”

Section: Related Workmentioning

confidence: 99%

An inside look into the complexity of box-office revenue prediction in China

Xiao

Chen³

et al. 2017

International Journal of Distributed Sensor Networks

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In this article, we discuss various elements contributing to exerting influence on box office in China, which are divided into internal and external factors. Since these factors could merely be quantified by online data sources partially or inaccurately, we propose that relativity analysis is more reasonable than precise revenue prediction. Trailer is selected as the combination of movie content and online behavior prior to releasing. Indexes from seven mainstream video websites are retrieved by the designed big data system which is integrated with the Internet of things technology. Correlation coefficients of different time periods are calculated. We apply multiple linear regression with stepwise method in modeling and prove that watching counts of 1 week before releasing on Youku is the barometer of market performance, especially the first week revenues. We also manifest the power of influential users through constructing Sina Weibo acquisition and analysis system.

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Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data

Cited by 246 publications

References 43 publications

Cross-evaluation of metrics to estimate the significance of creative works

Cross-evaluation of metrics to estimate the significance of creative works

Global Disease Monitoring and Forecasting with Wikipedia

An inside look into the complexity of box-office revenue prediction in China

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