Exploring Topic Modelling of User Reviews as a Monitoring Mechanism for Emergent Issues Within Social VR Communities

TL;DR

This paper investigates using topic modeling on user reviews from social VR platforms to automatically monitor emergent harassment issues, enabling real-time insights and reducing reliance on costly traditional methods.

Contribution

It introduces a novel approach applying topic modeling to social VR reviews to detect and analyze emergent harassment issues over time.

Findings

Sentiment analysis showed longitudinal changes in user reviews.

Topic modeling identified key themes related to harassment.

The approach can monitor emergent issues effectively.

Abstract

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.