Abstract
The existing approaches (e.g., questionnaire survey, interview survey) to investigating the factors causing positive and negative quality of experience (QoE) in cloud gaming rely mainly on user responses to a pre-designed questionnaire generated by researchers or guided to some extent by researchers. Despite its merits, this traditional approach costs time and money and also has the problem of low ecological validity. However, user-generated content created voluntarily by users on the internet has the advantages of high ecological validity and easy data collection, and the sample size can be much larger at low loss. This study proposes an approach to predicting the quality of experience of cloud gaming (CGQoE) by using user-generated content (UGC). 14 feature words related to QoE extracted from UGC texts can, to some extent, characterize the emotional information in UGC. The result shows that this approach has some use for analyzing the factors that affect the CGQoE and predicting its QoE. It also leaves room for future studies to compare its validity and reliability to other ways of measuring the QoE of cloud gaming.
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This paper is funded by Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center.
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Li, S., Wen, T., Yan, H., Qin, X. (2023). Prediction of Quality of Experience (QoE) of Cloud-Gaming Through an Approach to Extracting the Indicators from User Generated Content (UGC). In: Fang, X. (eds) HCI in Games. HCII 2023. Lecture Notes in Computer Science, vol 14047. Springer, Cham. https://doi.org/10.1007/978-3-031-35979-8_20
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