COVID-19's Impact on the Box office: Machine Learning and Difference-in-Difference

Yufan Zheng; Qiaoling Zhen; Minghao Tan; Haoran Hu; Choujun Zhan

doi:10.1109/ISKE54062.2021.9755401

COVID-19's Impact on the Box office: Machine Learning and Difference-in-Difference

Yufan Zheng
, Qiaoling Zhen
, Minghao Tan
, Haoran Hu
, Choujun Zhan

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

Abstract

As COVID-19 continues to spread around the world, and non-pharmacological interventions (NPIs) continue to be strengthened, the impact of COVID-19 on the film industry has not yet been clearly quantified. In this study, the Difference-in-Difference model is used to quantify the impact of the COVID-19 pandemic on the box office. Results indicate that the COVID-19 pandemic has a significant negative effect on the daily global box office. Additionally, based on a research dataset containing information on movies and COVID-19, ten machine learning methods were used to build a prediction model of the cumulative global box office. The experimental results showed that Extremely Randomized Trees had the best predictive performance, and it was found that COVID-19 features helped improve the predictive performance of several models. © 2021 IEEE.

Original language	English
Title of host publication	2021 IEEE International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2021)
Publisher	IEEE
Pages	458-463
Number of pages	6
ISBN (Electronic)	978-1-6654-0553-9, 978-1-6654-0552-2
ISBN (Print)	978-1-6654-0554-6
DOIs	https://doi.org/10.1109/ISKE54062.2021.9755401
Publication status	Published - Nov 2021
Externally published	Yes
Event	16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2021) - Chengdu, China Duration: 26 Nov 2021 → 28 Nov 2021

Publication series

Name	IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE

Conference

Conference	16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2021)
Abbreviated title	ISKE2021
Place	China
City	Chengdu
Period	26/11/21 → 28/11/21

Funding

This work was supported by Science and Technology Program of Guangzhou, China (201904010224), and Natural Science Foundation of Guangdong Province, China (2020A1515010761).

Research Keywords

COVID-19
Differencein-Difference
Machine learning
Movie

Access Output

10.1109/ISKE54062.2021.9755401

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Cite this

Zheng, Y., Zhen, Q., Tan, M., Hu, H., & Zhan, C. (2021). COVID-19's Impact on the Box office: Machine Learning and Difference-in-Difference. In 2021 IEEE International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2021) (pp. 458-463). (IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE). IEEE. https://doi.org/10.1109/ISKE54062.2021.9755401

@inproceedings{dde8c57835b7432ba163f31671c9259e,

title = "COVID-19's Impact on the Box office: Machine Learning and Difference-in-Difference",

abstract = "As COVID-19 continues to spread around the world, and non-pharmacological interventions (NPIs) continue to be strengthened, the impact of COVID-19 on the film industry has not yet been clearly quantified. In this study, the Difference-in-Difference model is used to quantify the impact of the COVID-19 pandemic on the box office. Results indicate that the COVID-19 pandemic has a significant negative effect on the daily global box office. Additionally, based on a research dataset containing information on movies and COVID-19, ten machine learning methods were used to build a prediction model of the cumulative global box office. The experimental results showed that Extremely Randomized Trees had the best predictive performance, and it was found that COVID-19 features helped improve the predictive performance of several models. {\textcopyright} 2021 IEEE.",

keywords = "COVID-19, Differencein-Difference, Machine learning, Movie",

author = "Yufan Zheng and Qiaoling Zhen and Minghao Tan and Haoran Hu and Choujun Zhan",

year = "2021",

month = nov,

doi = "10.1109/ISKE54062.2021.9755401",

language = "English",

isbn = "978-1-6654-0554-6",

series = "IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE",

publisher = "IEEE",

pages = "458--463",

booktitle = "2021 IEEE International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2021)",

address = "United States",

note = "16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2021), ISKE2021 ; Conference date: 26-11-2021 Through 28-11-2021",

}

Zheng, Y, Zhen, Q, Tan, M, Hu, H & Zhan, C 2021, COVID-19's Impact on the Box office: Machine Learning and Difference-in-Difference. in 2021 IEEE International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2021). IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE, IEEE, pp. 458-463, 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2021), Chengdu, China, 26/11/21. https://doi.org/10.1109/ISKE54062.2021.9755401

COVID-19's Impact on the Box office: Machine Learning and Difference-in-Difference. / Zheng, Yufan; Zhen, Qiaoling; Tan, Minghao et al.
2021 IEEE International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2021). IEEE, 2021. p. 458-463 (IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - COVID-19's Impact on the Box office

T2 - 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2021)

AU - Zheng, Yufan

AU - Zhen, Qiaoling

AU - Tan, Minghao

AU - Hu, Haoran

AU - Zhan, Choujun

PY - 2021/11

Y1 - 2021/11

N2 - As COVID-19 continues to spread around the world, and non-pharmacological interventions (NPIs) continue to be strengthened, the impact of COVID-19 on the film industry has not yet been clearly quantified. In this study, the Difference-in-Difference model is used to quantify the impact of the COVID-19 pandemic on the box office. Results indicate that the COVID-19 pandemic has a significant negative effect on the daily global box office. Additionally, based on a research dataset containing information on movies and COVID-19, ten machine learning methods were used to build a prediction model of the cumulative global box office. The experimental results showed that Extremely Randomized Trees had the best predictive performance, and it was found that COVID-19 features helped improve the predictive performance of several models. © 2021 IEEE.

AB - As COVID-19 continues to spread around the world, and non-pharmacological interventions (NPIs) continue to be strengthened, the impact of COVID-19 on the film industry has not yet been clearly quantified. In this study, the Difference-in-Difference model is used to quantify the impact of the COVID-19 pandemic on the box office. Results indicate that the COVID-19 pandemic has a significant negative effect on the daily global box office. Additionally, based on a research dataset containing information on movies and COVID-19, ten machine learning methods were used to build a prediction model of the cumulative global box office. The experimental results showed that Extremely Randomized Trees had the best predictive performance, and it was found that COVID-19 features helped improve the predictive performance of several models. © 2021 IEEE.

KW - COVID-19

KW - Differencein-Difference

KW - Machine learning

KW - Movie

UR - https://www.scopus.com/pages/publications/85129421325

UR - https://www.scopus.com/pages/publications/85129421325#tab=impact

U2 - 10.1109/ISKE54062.2021.9755401

DO - 10.1109/ISKE54062.2021.9755401

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-6654-0554-6

T3 - IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE

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BT - 2021 IEEE International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2021)

PB - IEEE

Y2 - 26 November 2021 through 28 November 2021

ER -

COVID-19's Impact on the Box office: Machine Learning and Difference-in-Difference

Abstract

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