@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",
}