Abstract
The demand for video games increased in large scale during the COVID-19 pandemic as people had to stay at home. In this study we investigate the changes in player population of games during the pandemic using our dataset of 1963 games on Steam to generate insights that would be valuable for the game industry to understand the demand in such crisis. We conduct an empirical analysis to analyse changes in player population size and weekly patterns. Also, we investigate the use of machine learning classification models to predict the games that become popular during the pandemic using information about games as features. Our results indicate a 33% of increase of population during the pandemic and diminishing of weekly player population patterns. Also, we identify that the Random Forest model performs better than other classification models in predicting popular games, however, with only a 63% accuracy and tags assigned to games are the most important feature for prediction generation. Our tag analysis reveals Multiplayer, Adventure, Racing and Boardgames are popular during the pandemic.
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Wannigamage, D., Barlow, M., Lakshika, E., Kasmarik, K. (2020). Analysis and Prediction of Player Population Changes in Digital Games During the COVID-19 Pandemic. In: Gallagher, M., Moustafa, N., Lakshika, E. (eds) AI 2020: Advances in Artificial Intelligence. AI 2020. Lecture Notes in Computer Science(), vol 12576. Springer, Cham. https://doi.org/10.1007/978-3-030-64984-5_36
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DOI: https://doi.org/10.1007/978-3-030-64984-5_36
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