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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.03994 |
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| _version_ | 1866911908275159040 |
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| author | Singh, Angelo O'Hagan, Joseph |
| author_facet | Singh, Angelo O'Hagan, Joseph |
| contents | Users of social virtual reality (VR) platforms often use user reviews to document incidents of witnessed and/or experienced user harassment. However, at present, research has yet to be explore utilising this data as a monitoring mechanism to identify emergent issues within social VR communities. Such a system would be of much benefit to developers and researchers as it would enable the automatic identification of emergent issues as they occur, provide a means of longitudinally analysing harassment, and reduce the reliance on alternative, high cost, monitoring methodologies, e.g. observation or interview studies. To contribute towards the development of such a system, we collected approximately 40,000 Rec Room user reviews from the Steam storefront. We then analysed our dataset's sentiment, word/term frequencies, and conducted a topic modelling analysis of the negative reviews detected in our dataset. We report our approach was capable of longitudinally monitoring changes in review sentiment and identifying high level themes related to types of harassment known to occur in social VR platforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03994 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Topic Modelling of User Reviews as a Monitoring Mechanism for Emergent Issues Within Social VR Communities Singh, Angelo O'Hagan, Joseph Human-Computer Interaction Users of social virtual reality (VR) platforms often use user reviews to document incidents of witnessed and/or experienced user harassment. However, at present, research has yet to be explore utilising this data as a monitoring mechanism to identify emergent issues within social VR communities. Such a system would be of much benefit to developers and researchers as it would enable the automatic identification of emergent issues as they occur, provide a means of longitudinally analysing harassment, and reduce the reliance on alternative, high cost, monitoring methodologies, e.g. observation or interview studies. To contribute towards the development of such a system, we collected approximately 40,000 Rec Room user reviews from the Steam storefront. We then analysed our dataset's sentiment, word/term frequencies, and conducted a topic modelling analysis of the negative reviews detected in our dataset. We report our approach was capable of longitudinally monitoring changes in review sentiment and identifying high level themes related to types of harassment known to occur in social VR platforms. |
| title | Exploring Topic Modelling of User Reviews as a Monitoring Mechanism for Emergent Issues Within Social VR Communities |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2406.03994 |