Cybersickness remains a persistent challenge in immersive game design, yet most empirical research relies on laboratory-based trials with limited ecological validity. In contrast, this study analyzes over 1.3 million user-generated reviews from the Steam platform to examine how players experience, mitigate, and discuss cybersickness in first-person virtual reality (VR) games. Using large-scale computational text analysis, the study integrates topic modeling, lexicon-based sentiment analysis (VADER), and Random Forest classification to identify sickness-related narratives, self-reported mitigation strategies, and emotional framing in player reviews. The results show that cybersickness is more frequent in negative reviews; however, it also appears in a non-negligible subset of positive reviews. Reviews mentioning cybersickness tend to be longer and are associated with shorter playtimes at the time of review. Furthermore, surface-level textual and behavioral features, mainlyy word count and sentiment polarity reliably distinguish sickness-related reviews from other forms of player dissatisfaction. The analysis also reveals commonly reported mitigation strategies, including session control, seated play, and locomotion adjustments, while showing considerable inter-individual variability in their perceived effectiveness. Based on these results, the study derives design recommendations emphasizing player-centered comfort options and customizable locomotion systems. Overall, the results demonstrate the value of large-scale computational text analysis for capturing real-world VR experiences and informing inclusive, comfort-aware game design beyond laboratory settings.
Guzsvinecz et al. (Mon,) studied this question.