The Impact of COVID-19 on Online Games: Machine Learning and Difference-in-Difference

Shuangyan Wu; Haoran Hu; Yufan Zheng; Qiaoling Zhen; Shuntao Zhang; Choujun Zhan

doi:10.1007/978-981-19-4549-6_35

The Impact of COVID-19 on Online Games: Machine Learning and Difference-in-Difference

Shuangyan Wu, Haoran Hu, Yufan Zheng, Qiaoling Zhen, Shuntao Zhang, Choujun Zhan^*

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

By intervening in people’s behavior, governments in several nations have established a variety of strategies to slow down the spread of COVID-19 pandemic. At the same time, it has a different impact on everyone. Data from the Steam platform online games between January 2018 and February 2021 was used for this project’s analysis. Through the difference-in-difference model in Synthetic Control Methods to quantify and analyze, crucial positive effect on Steam’s online players during COVID-19 and the increase of the number of online players and the released games of the platform in 2020 had been found. The machine learning prediction model was created using the daily totals of the online gaming players of the most popular games on the site. The Ridge regression, whose R squared reached 0.805, had been demonstrated by the experimental results that it got the best performance. Simultaneously, this work found the features of the COVID-19 pandemic and the features of the human mobility, which helps to build a great majority of the predictive models. © Springer Nature Singapore Pte Ltd. 2022

Original language	English
Title of host publication	Computer Supported Cooperative Work and Social Computing
Subtitle of host publication	16th CCF Conference, ChineseCSCW 2021: Revised Selected Papers
Editors	Yuqing Sun, Tun Lu, Buqing Cao, Hongfei Fan, Dongning Liu, Bowen Du, Liping Gao
Publisher	Springer Singapore
Pages	458-470
Number of pages	13
Volume	Part II
ISBN (Electronic)	9789811945496
ISBN (Print)	9789811945489
DOIs	https://doi.org/10.1007/978-981-19-4549-6_35
Publication status	Published - 2022
Externally published	Yes
Event	16th CCF Conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2021): Human-Centered Collaborative Intelligence - Xiangtan, China Duration: 26 Nov 2021 → 28 Nov 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1492
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	16th CCF Conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2021)
Place	China
City	Xiangtan
Period	26/11/21 → 28/11/21

Research Keywords

COVID-19
Difference-in-difference
Machine learning
Online game
Steam platform

Access Output

10.1007/978-981-19-4549-6_35

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Cite this

Wu, S., Hu, H., Zheng, Y., Zhen, Q., Zhang, S., & Zhan, C. (2022). The Impact of COVID-19 on Online Games: Machine Learning and Difference-in-Difference. In Y. Sun, T. Lu, B. Cao, H. Fan, D. Liu, B. Du, & L. Gao (Eds.), Computer Supported Cooperative Work and Social Computing: 16th CCF Conference, ChineseCSCW 2021: Revised Selected Papers (Vol. Part II, pp. 458-470). (Communications in Computer and Information Science; Vol. 1492). Springer Singapore. https://doi.org/10.1007/978-981-19-4549-6_35

Wu, Shuangyan ; Hu, Haoran ; Zheng, Yufan et al. / The Impact of COVID-19 on Online Games : Machine Learning and Difference-in-Difference. Computer Supported Cooperative Work and Social Computing: 16th CCF Conference, ChineseCSCW 2021: Revised Selected Papers. editor / Yuqing Sun ; Tun Lu ; Buqing Cao ; Hongfei Fan ; Dongning Liu ; Bowen Du ; Liping Gao. Vol. Part II Springer Singapore, 2022. pp. 458-470 (Communications in Computer and Information Science).

@inproceedings{ef3257445ddb4cb58316fb412b3344e9,

title = "The Impact of COVID-19 on Online Games: Machine Learning and Difference-in-Difference",

abstract = "By intervening in people{\textquoteright}s behavior, governments in several nations have established a variety of strategies to slow down the spread of COVID-19 pandemic. At the same time, it has a different impact on everyone. Data from the Steam platform online games between January 2018 and February 2021 was used for this project{\textquoteright}s analysis. Through the difference-in-difference model in Synthetic Control Methods to quantify and analyze, crucial positive effect on Steam{\textquoteright}s online players during COVID-19 and the increase of the number of online players and the released games of the platform in 2020 had been found. The machine learning prediction model was created using the daily totals of the online gaming players of the most popular games on the site. The Ridge regression, whose R squared reached 0.805, had been demonstrated by the experimental results that it got the best performance. Simultaneously, this work found the features of the COVID-19 pandemic and the features of the human mobility, which helps to build a great majority of the predictive models. {\textcopyright} Springer Nature Singapore Pte Ltd. 2022",

keywords = "COVID-19, Difference-in-difference, Machine learning, Online game, Steam platform",

author = "Shuangyan Wu and Haoran Hu and Yufan Zheng and Qiaoling Zhen and Shuntao Zhang and Choujun Zhan",

year = "2022",

doi = "10.1007/978-981-19-4549-6_35",

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booktitle = "Computer Supported Cooperative Work and Social Computing",

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note = "16th CCF Conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2021) : Human-Centered Collaborative Intelligence ; Conference date: 26-11-2021 Through 28-11-2021",

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Wu, S, Hu, H, Zheng, Y, Zhen, Q, Zhang, S & Zhan, C 2022, The Impact of COVID-19 on Online Games: Machine Learning and Difference-in-Difference. in Y Sun, T Lu, B Cao, H Fan, D Liu, B Du & L Gao (eds), Computer Supported Cooperative Work and Social Computing: 16th CCF Conference, ChineseCSCW 2021: Revised Selected Papers. vol. Part II, Communications in Computer and Information Science, vol. 1492, Springer Singapore, pp. 458-470, 16th CCF Conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2021), Xiangtan, China, 26/11/21. https://doi.org/10.1007/978-981-19-4549-6_35

The Impact of COVID-19 on Online Games: Machine Learning and Difference-in-Difference. / Wu, Shuangyan; Hu, Haoran; Zheng, Yufan et al.
Computer Supported Cooperative Work and Social Computing: 16th CCF Conference, ChineseCSCW 2021: Revised Selected Papers. ed. / Yuqing Sun; Tun Lu; Buqing Cao; Hongfei Fan; Dongning Liu; Bowen Du; Liping Gao. Vol. Part II Springer Singapore, 2022. p. 458-470 (Communications in Computer and Information Science; Vol. 1492).

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

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T2 - 16th CCF Conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2021)

AU - Wu, Shuangyan

AU - Hu, Haoran

AU - Zheng, Yufan

AU - Zhen, Qiaoling

AU - Zhang, Shuntao

AU - Zhan, Choujun

PY - 2022

Y1 - 2022

N2 - By intervening in people’s behavior, governments in several nations have established a variety of strategies to slow down the spread of COVID-19 pandemic. At the same time, it has a different impact on everyone. Data from the Steam platform online games between January 2018 and February 2021 was used for this project’s analysis. Through the difference-in-difference model in Synthetic Control Methods to quantify and analyze, crucial positive effect on Steam’s online players during COVID-19 and the increase of the number of online players and the released games of the platform in 2020 had been found. The machine learning prediction model was created using the daily totals of the online gaming players of the most popular games on the site. The Ridge regression, whose R squared reached 0.805, had been demonstrated by the experimental results that it got the best performance. Simultaneously, this work found the features of the COVID-19 pandemic and the features of the human mobility, which helps to build a great majority of the predictive models. © Springer Nature Singapore Pte Ltd. 2022

AB - By intervening in people’s behavior, governments in several nations have established a variety of strategies to slow down the spread of COVID-19 pandemic. At the same time, it has a different impact on everyone. Data from the Steam platform online games between January 2018 and February 2021 was used for this project’s analysis. Through the difference-in-difference model in Synthetic Control Methods to quantify and analyze, crucial positive effect on Steam’s online players during COVID-19 and the increase of the number of online players and the released games of the platform in 2020 had been found. The machine learning prediction model was created using the daily totals of the online gaming players of the most popular games on the site. The Ridge regression, whose R squared reached 0.805, had been demonstrated by the experimental results that it got the best performance. Simultaneously, this work found the features of the COVID-19 pandemic and the features of the human mobility, which helps to build a great majority of the predictive models. © Springer Nature Singapore Pte Ltd. 2022

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KW - Difference-in-difference

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KW - Online game

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PB - Springer Singapore

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Wu S, Hu H, Zheng Y, Zhen Q, Zhang S, Zhan C. The Impact of COVID-19 on Online Games: Machine Learning and Difference-in-Difference. In Sun Y, Lu T, Cao B, Fan H, Liu D, Du B, Gao L, editors, Computer Supported Cooperative Work and Social Computing: 16th CCF Conference, ChineseCSCW 2021: Revised Selected Papers. Vol. Part II. Springer Singapore. 2022. p. 458-470. (Communications in Computer and Information Science). Epub 2022 Jul 22. doi: 10.1007/978-981-19-4549-6_35