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The effect of players' ability to resist monsters on immersion and fear in survival horror games
- 1 the University of New South Wales
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Abstract
Horror survival games, as a crucial segment of the video game industry, are highly regrded for their ability to evoke excitement, suspense and the challenge of overcoming obstacles and fears through wit and skill. This study delves into the impact of players' ability to confront monsters in horror survival games on their level of immersion and fear, and how these two psychological states mutually enhance the gaming experience. This research applies Python for natural language processing analyses, including sentiment analysis, keyword frequency analysis, Latent Dirichlet Allocation (LDA) topic modeling, and K-Means clustering to uncover the main trends in player feedback, which is based on 5,000 user comments scraped from the Steam platform on two famous horror games, "Resident Evil 7" and "Outlast 2." Additionally, data on players' personal experiences of fear and immersion in the games are collected through surveys. The results show that the sensation of fear and successfully confronting monsters significantly enhances players' immersion of being completely engaged in the game, highlighting the importance of balancing challenge and player's ability to make choices in game design. This study provides valuable insights into understanding the psychological dynamics of players in horror survival games, guiding game designers and developers on how to create more engaging and satisfying gaming experiences that meet players' requirements.
Keywords
Horror Survival Games, Resist, Player Immersion, Fear Experience
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Cite this article
Yan,Q. (2024). The effect of players' ability to resist monsters on immersion and fear in survival horror games. Applied and Computational Engineering,77,138-143.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Software Engineering and Machine Learning
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