“People are Way too Obsessed with Rank”: Trust System in Social Virtual Reality

Over the past few years, social VR has been gaining traction quickly. Social VR commonly refers to 3 dimensional, immersive digital environments where people can interact, communicate, and socialize with each other through head-mounted devices (McVeigh-Schultz et al. 2018). Popular VR platforms at the time of writing include VRChat⁵, Horizon Worlds⁶, RecRoom⁷ and others. In these virtual worlds, users are typically virtually represented by avatars. They embody and control those avatars via body tracking technologies (Freeman et al. 2022a). Social VR provides life-like interaction. In particular, VR-mediated social interaction affords verbal and non-verbal interaction in real-time (Li et al. 2019; Wang et al. 2019; De Simone et al. 2019) and supports communication features like animated or facial tracking-based facial expressions (Kolesnichenko et al. 2019). In addition, a strong sense of body ownership induced by technological characteristics over virtual user representations makes social VR seem to extend the social and experiential qualities of traditional shared virtual spaces (Kilteni et al. 2012; Maselli and Slater 2013). Previous studies on social VR illustrate people engaging in different social activities afforded by those platforms (Barreda-Ángeles and Hartmann 2022; Sykownik et al. 2021; Maloney and Freeman 2020), to satisfy diverse social needs, such as socializing with friends, meeting strangers worldwide, creating and exploring different worlds, and gathering for social events (Barreda-Ángeles and Hartmann 2022; Sykownik et al. 2021; Maloney and Freeman 2020). Previous studies have also focused on studying specific aspects of user behaviors and experiences in social VR. Piitulainen et al. (2022) investigated social dancing in VRChat and found that social factors are the main reasons people enjoy dancing in social VR. Sykownik et al. (2022) studied selfdisclosure and found that disclosure on social VR is similar to offline communication. The relationship with others moderates self-disclosure but is also impacted by the contextual factors that social VR applications afford. Furthermore, research has tried to understand the design strategies of social VR platforms (McVeigh-Schultz et al. 2018; Jonas et al. 2019). For example, McVeigh-Schultz et al. (2019) conducted interviews with industry experts to understand the design choices of these social VR platforms to promote prosocial behaviors and introduced a preliminary design framework that helps to form prosocial interactions in social VR. Few previous studies in social VR cover TS (Saffo et al. 2020; Zheng et al. 2023), but little focus on the system itself rather than mentioning it to give a wider picture of the VRChat platform.

Harassment is a prevalent problem in social VR (Blackwell et al. 2019; Shriram and Schwartz 2017; Freeman et al. 2022b). Research shows that two out of seven women and 21 out of 99 men reported experiencing harassment, and 42% of the users said they witnessed someone else being harassed (Shriram and Schwartz 2017). The non-profit Center for Countering Digital Hate’s research shows how frequently misconduct appears on VRChat. The researchers discovered 100 potential violations of Meta’s VR guidelines, including sexual harassment and abuse, after watching user behavior for 11 h and 30 min.⁸ Social VR applications afford a high potential for toxic behavior. It has been noted that the distinctive qualities of VR technology and the affordances of multi-modal communications, including both verbal and nonverbal interactions, such as voice, gesture, proxemics, gaze, and facial expression, not only support new forms of immersive experience but also potentially increase the risk of online harassment beyond text or voice-based harassment (Blackwell et al. 2019). The experience of being harassed in VR is intensified (Slater et al. 2009) and more acute than in other computer-mediated social spaces due to embodiment and presence. Using synchronous audio in virtual reality, unlike other social media platforms, where insults are typically provided asynchronously and primarily via text, makes harassment more severe. The synchronousness of VR also causes social VR applications to mainly facilitate interactions between strangers (Blackwell et al. 2019), causing more conflicts. Additionally, toxic game culture impacts VR (Freeman et al. 2022a), and gaming is a major significant case for virtual reality technology (Blackwell et al. 2019). Furthermore, due to social VR’s emphasis on supporting open virtual worlds, simulating social contexts (such as multi-user events), and drawing in a diverse user base, harassment may be felt in social VR as being even more immersive and damaging than in traditional VR, which focuses primarily on single-player games or applications (Freeman et al. 2022b). The subjective and individualized nature of users’ conceptions of harassment in social VR makes content moderation in social VR challenging and reporting toxic behaviors or avoiding them difficult (Blackwell et al. 2019).

In sum, social VR can afford immersive and natural social interaction similar to the real world. Social VR platforms have a prevalence of toxicity. The multiple reasons for that include the affordances of VR technologies, the demographic of users, and the impact of gaming culture. Furthermore, there is little research on how algorithm-based TS is appropriated.

Online harassment can have severe and adverse effects on the well-being of those targeted (Uttarapong et al. 2021; Schulenberg et al. 2023) and those dealing with it (Dosono and Semaan 2019; Steiger et al. 2021). At the moment, online platforms combat harm mainly through content moderation. Content moderation is the “governance mechanisms that structure participation in a community to facilitate cooperation and prevent abuse” (Grimmelmann 2015). According to this definition, content moderation has two focal points. A thread of research focuses on how to prevent abuse with punitive moderation strategies deployed by humans, algorithms, or both, such as reporting and flagging (Kou and Gui 2021), account suspension (Chandrasekharan et al. 2017; Cai et al. 2021), and content removal (Srinivasan et al. 2019). However, recently, scholars have also pointed out the potential drawbacks of this approach, as it could lead to user discontent and dropout from the platform with disappointment and frustration (Kou 2021) and question the legitimacy of the moderation process (Pan et al. 2022). To supplement the punitive sanction mechanism, researchers suggest that the moderation system should also consider the restorative approach to managing communities and transferring violators to positive members (Schoenebeck et al. 2021; Xiao et al. 2023), such as explaining the moderation decision to improve perceived justice and fairness (Jhaver et al. 2019), educating and communicating with violators to improve users’ trust in the moderation system (Cai et al. 2021), training the mental model to understand the intentions and personalities of violators (Cai and Wohn 2021), and improving algorithm transparency (Ma and Kou 2023). However, the approach also has shortcomings, including being very labor intensive and requiring training for moderators (Xiao et al. 2022).

Another research thread focuses on facilitating cooperation, which means that the moderation system should include components to organize how community members positively engage with each other (Kim et al. 2021; Seering et al. 2019). This approach would promote prosocial behaviors by setting a good example for the rest of the community members to mimic normative behaviors (Cai et al. 2021; Seering et al. 2017). Other mechanisms include the reward system (e.g., Karma point on Reddit) and the badge system to show the support and loyalty of community members (e.g., Twitch badge system) (Cai and Wohn 2023).

In line with research about facilitating cooperation, we focus on the TS in social VR, a reputation system aimed at promoting prosocial behaviors as a strategy to foster communities and mitigate antisocial behaviors. Appropriate behavior will typically be encouraged over the long run by the incentive of a positive reputation and the disincentive of a bad one (Hendrikx et al. 2015). Reputation systems facilitate the collection, aggregation, and distribution of data about entities, which in turn can be used to characterize and predict the future behavior of entities (Despotovic and Aberer 2004; Resnick and Zeckhauser n.d; Ruohomaa et al. 2007); they tend to assign users points or ranks, working as social corrective and reinforcing prosocial behaviors. The reputation system has been used to combat toxicity in online spaces, such as games (League of Legends Wiki. n.d; Vidal 2023). One example is the Honor System in League of Legends (League of Legends Wiki. n.d), which allows players to ‘honor’ others on their team for deserving behaviors (e.g., good teamwork). Honorable players advance to honor levels, while those who misbehave are demoted. However, new platforms bring new affordances and challenges to the moderation system (Cai et al. 2023; Jiang et al. 2019). VRChat is a special 3D platform designed for social purposes and different from other online spaces with respect to toxic and harassing behaviors (Sabri et al. 2023), such as violating of personal space considered a form of harassment (Blackwell et al. 2019). The TS, displaying individual users’ trust ranks in a highly immersive and lifelike environment to mitigate toxic behaviors, requires a deep understanding of how such design might affect users’ behaviors and moderation practices.

The common mechanisms in social VR platforms for users to protect themselves from toxic behaviors include Safety Bubbles, Reporting, Blocking, Muting, Vote-kicking, and Escaping (Zheng et al. 2023). The TS is only used in VRChat, as shown in Table 1. Trust is often used interchangeably with the word “reputation” to describe a system that evaluates and shows the reliability of its users (Jøsang et al. 2007). This type of system is also often called the “trust and reputation system” (Jøsang 2006). Trust is a directional relationship that involves the trustor and trustee. One must assume the trustor is a thinking entity that can evaluate and make decisions based on past experiences and received information. The trustee can be a person, organization, physical entity, or even a concept (in the TS in social VR, the trustee is a user). Essentially, trust is the subjective probability that an entity (A) expects another entity (B) to perform an action on which its welfare depends. Trust can be considered the trustor’s evaluation of the trustee’s reliability (e.g., expressed as probability) in the context of dependence on the trustee (Jøsang 2006).

TS on VRChat is a system that evaluates the trustworthiness (i.e., reliability) of users based on their behaviors, then ranks them accordingly⁹. Normal ranks include Visitor, New User, User, Known User, and Trusted User, referring to Fig. 1. Newcomers start at the Visitor level and can progress to higher ranks by actively engaging in the VRChat. The progression through these ranks is determined by an algorithm. The VRChat official does not explain how the algorithm works, except to state that the trust ranks are linked with how much time a user has spent on VRChat, how much content they have contributed, friends they have made, and other factors, as well as things that do not raise trust rank: standing or idling (away from keyboard), uploading a large amount of low-effort content, and mass-friending large numbers of users. As the VRChat official puts it, “You gain these ranks simply by playing VRChat – as you explore worlds, make friends, and create content, you will gain more trust, which determines our Trust Rank.”

In addition to these standard ranks, the TS also includes special ranks like “Nuisance” and “VRChat Team.” The Nuisance rank is assigned as a form of penalty for users who engage in misconduct and typically results in being blocked by default. This means they cannot be seen or heard unless other users choose to unblock and unmute them manually. The VRChat Team rank is reserved exclusively for members of the VRChat team and is identifiable by a special tag. Each trust rank is associated with a specific color displayed on the user’s nameplate. This visual indicator helps users quickly identify the trust level of others in their vicinity. For example, Known Users might have orange nameplates, while Trusted Users have purple ones. Because of that, many use colors to describe user groups, such as orange as an alternative name for the Known User. The nameplates are a vital aspect of the user interface, providing immediate context about who one interacts with. With increasing trust ranks come greater privileges. For instance, users ranked Visitor and below are restricted from uploading content, while those above this rank are granted this ability. Trust and Known Users have the unique option to present themselves as ordinary Users, influencing how the system and other participants interact with them.

A set of safety settings is often used in combination with TS, which allows users to configure how users in each rank are treated regarding their visibility. As shown by Table 2, including voice, avatar, avatar audio, custom animations, shaders, particles, and lights, users can enable or disable other users’ features based on their ranks. Users can configure each rank with a unique setting, referring to Fig. 1. For example, if one disables all the features of users in Visitor Rank, then with the setting, all Visitor users will be invisible to the user. If the voice setting is activated, then one can hear the voice of users in these ranks, but not other things. By default, all safety settings are disabled for trusted users, meaning their avatar effects are fully visible unless manually adjusted by other users.

It also allows for hiding or showing specific users. For example, if a user encounters toxic users with ranks that the user does not set restrictions on, the user can choose to block those specific toxic users. The safety system will disable all the features of the users’ avatars.

To answer our research questions, we utilized the discussion data from the “r/VRChat” subreddit, the largest online community for VRChat users, with more than 148 thousand users at the time of writing. Researchers frequently browse the subreddit as they are interested in the social VR platform. The online discussions contain the naturally and directly shared experiences of VRChat users. They are different from conducting interviews or surveys, which heavily rely on self-reports and require participants to recount, which may be limited by recall bias (Gorin and Stone 2001) and social desirability bias (Fisher 1993). Thus, we deemed collecting and analyzing discussion data appropriate as the first endeavor.

We used Reddit’s API, which allows us to fetch data by relevant keywords. The API enables us to collect all threads containing keywords in either content, user comments, or titles. Our initial set of keywords was carefully chosen based on a thorough review of VRChat’s official documentation¹⁰ on the TS, combined with the first author’s extensive experience on the platform, spanning over three years, including “known user, visitor, nuisance, trust user, trust system.” To enhance the scope and depth of our dataset, we further analyzed the first hundred threads from our initial data collection. This detailed review was crucial in identifying additional keywords frequently used by users in their discussions about the TS. It was observed that users often used terms like “rank system” interchangeably with the TS and “trust ranks” as a general term for different levels within the system. Recognizing the significance of these terms in the context of user discussions, we incorporated “trust rank” and “rank system” as complementary additional keywords in our data collection process. This expansion of keywords, based on actual user language and discussion patterns, ensured that our data collection was not only thorough but also aligned with the users’ perspectives and terminologies. It provided us with a more nuanced and authentic dataset, reflecting the honest conversations and opinions of VRChat users regarding the TS.

We scraped all the relevant published data in r/VRChat, about 11596 posts in total (including threads and comments). The dates of the posts ranged from 2018 to 2022. The data scraped included the title, body, URL of each thread, and the total number and content of the associated comments, as well as their metadata like timestamp and upvotes. After the data scraping, we embarked on refining our dataset. Our primary focus was on eliminating irrelevant data that did not pertain to the TS. For example, while many posts mentioned keywords, not all of them were relevant to discussions about the TS; for example, “The loud music from the participants’ avatars became a nuisance, disrupting the event I had planned.” Such posts were identified and removed from our dataset. Additionally, we encountered replicated data due to the keyword-based approach of our data retrieval. This occurred because some threads touched upon multiple keywords, leading to their multiple inclusions in our dataset. To address these challenges, we thoroughly reviewed all 647 threads. Our goal was to identify and eliminate posts that, despite including keywords, did not discuss the TS and duplicated threads and associated comments. Through this rigorous filtering process, we refined our dataset to 6,028 posts, of which 362 were threads and the remainder were comments. This final dataset was considered representative and relevant for our analysis, focusing on user interactions and opinions regarding the TS in VRChat.

We aimed to gain a deep understanding with nuanced details about users’ perception of the TS in VRChat. We employed a qualitative analysis followed by previous work with a similar dataset (Ma and Kou 2021). We adopted the inductive thematic analysis method from (Braun and Clarke 2006) to code the collected data. Two coders met regularly throughout the entire analysis phase. We first immersed ourselves in reading the data to familiarize ourselves with the whole dataset. After the familiarization, two researchers separately returned to the dataset to generate initial codes for words, sentences, or paragraphs for the same hundred posts, including comments. Then, the coders met to discuss the initial list of codes. We went over and compared our codes. We identified differences where one coder might have a code not identified by another. In such cases, we would discuss whether the code was sufficiently distinctive in its semantics or whether it could be grouped into other codes. Through this process, we formed a consensus among the codes. Cohen’s Kappa (κ) was used to measure inter-rater reliability (IRR) (McDonald et al. 2019; Viera et al. 2005). IRR on the transcript was κ = 0.80 (SD = 0.20), with codes ranging from strong to nearly perfect agreement. Then, we split the rest of the data to code. We employed iterative coding, initially generating codes in a data-driven manner. We coded segments of text based on their inherent meaning. For example, a user comment such as “The ranks damage first impressions, which kills the desire to interact.” was coded as ‘damage motivation to interact’. Throughout the analysis, we constantly moved between the codes and the associated data, collaboratively synthesizing these codes to develop higher-level themes. For instance, specific codes like ‘damage motivation to interact’, ‘blocking based on ranks helps segregation’, and ‘muting based on ranks helps segregation,’ were collectively grouped under a broader theme, ‘inhibit interactions among users from different ranks’. When a new code appeared, we compared it with our existing codes to either integrate it into previous themes or create a new theme for it. Our iterative process culminated in the formation of overarching themes that encapsulated the complex user perceptions of the TS in VRChat.

Overall, standard ethical guidelines are followed (Franzke et al. 2020; GDPR.Eu. n.d.) to collect user data and perform analysis. We consulted with our institution’s ethics committee on potential ethical concerns. The data are openly accessible and have a public viewing expectation. Additionally, they do not involve sensitive information. The gathering and processing are regarded as no more than minimal risk to individuals. Therefore, we do not need to request ethics approval for our study. But, we are aware that the HCI community has several reservations regarding the use of publicly accessible data (Adams 2022). Fiesler suggests that these concerns should be framed within “the broader context of the benefits of scientific discovery” (Fiesler 2019). The implications are that researchers should be thoughtful and sensitive in their interactions with the data and in their purposes for using it. In this research, we utilized multiple approaches to protect the people involved in our study. We rephrased our quotes to reduce the researchability for tracking back to the original posts. We eliminated all information that could be used to identify an individual. The researchers in charge are the only ones who have access to the data, which have been safely saved on our password-protected devices. Researchers in the team also had meetings to explore the possible negative effects and potential positive outcomes of this research in addition to the aforementioned measurements. We reasoned that: 1) The research provides an understanding of the TS used in the most popular social VR platform, VRChat, exploring how users perceive it and weighing its pros and cons. The outcomes can provide implications for similar systems to better cope with harassment. 2) The research can act as a vehicle to understand harassment in social VR platforms.

A fundamental function of TS is to evaluate and present a user’s trustworthiness. A portion of the VRChat community places their trust in the TS, valuing its logicbased approach to evaluating user trustworthiness. Ideally, the longer users spend on the platform, the higher their rank becomes. However, if they tend to engage in toxic behavior towards other users, they are more likely to be reported, blocked, or votekicked, which many users believe can negatively impact trust ranks (VRChat does not confirm the influence of these actions on user ranks.). Thus, toxic users cannot upgrade their ranks, so users with high ranks tend not to be obnoxious. A user’s comment illustrates the view,

“The trust is based on how long you’ve been using the platform without being obnoxious to others. You are either new or you’re not... I believe that if someone has played for a long time without becoming a nuisance. People will trust them.”

The quote shows that people who tend to be toxic are less likely to have high ranks. They spend more time on the platform while upgrading their trust rank, indicating that they conduct a limited amount of harmful behaviors.

Many users regarded the TS acts as a form of deterrence. In order to get high ranks, users need to put time and effort into the platform. This investment creates a disincentive for high-level users to engage in harassment or toxic behavior, as once they are being massively blocked, vote-kicked, or reported, resulting in losing trust level or even a ban on the account. As demonstrated by a user,

“Upgrading to a higher rank is difficult and requires a lot of time and effort from users. So I think high-level users seldom do behaviors that may result in harmful consequences to their ranks, such as behaviors that may lead to reports. But low-level users don’t have this concern.”

According to the user’s comment, higher-ranked individuals are typically more mindful of their actions as they have invested significant time and effort in attaining their rank. They are less likely to do things that lead to negative consequences in their ranks. Conversely, those who tend to exhibit toxic behavior are most likely with lower ranks, such as the Visitors.

Many users believe in the system because of its logic, but its inappropriate implementation results in distrust from other users. The algorithm’s opacity is a contributing factor. Users are left in the dark about how their ‘trustworthiness’ is calculated, aside from the system’s reliance on metrics such as time spent on VRChat, number of friends, and contributions to the platform. Many users assumed the ranks could only demonstrate how a user perform in the above factors but could not tell whether a user is trustworthy or whether they are more or less likely to carry out toxic behavior. A user’s comment encapsulates a common sentiment,

“The Trust System has never been a reliable approach for evaluating g people. Since it solely indicates user experience and their contribution to the game asset.”

Another reason is due to the precarity of the algorithm. Many users observed that individuals with better performance in each factor, such as more time, more contributions, more friends, and fewer violations of the community code, received a lower trust rank than others. For example,

“This rating system is so erratic... I am still a new user with 65 hours, half of my friends with 40 are known users or higher, and I have four avatars posted.”

Such experiences highlight the perceived inconsistencies in the ranking process. Users who believe they perform better in key metrics still lag in rank compared to others, leading to questions about the system’s fairness and accuracy.

Additionally, the user community has observed that high rank does not necessarily equate to high moral standards or non-toxic behavior. For example,

“Don’t trust people based on their ranks. I once reasoned that I might loosen my security setting when around “known” and “trusted” users. This was foolish on my side and ignorant. Morality is not ranked in the ranking system. It’s just a way to gauge how much time someone has invested in the game. People who have played the game longer are more likely to be aware of its safety flaws. Some utilize it as knowledge to protect themselves. But I’ve had known and trusted users crash me.”

The above quote is from a user who wrote to warn others that Known and Trusted users are also toxic and should set Safety Settings against them. Their deep understanding of the system’s intricacies could be used for protection or, conversely, to exploit less informed users.

As mentioned in the previous section, the trust ranks are perceived to be able to demonstrate how users perform in the factors the TS uses to calculate their ranks, such as time spent. In practice, users utilize this characteristic of trust ranks to interpret other users’ information. For example, they regard the ranks as an indication of a user’s familiarity and experience with the platform, which can help in finding suitable friends. The following observation reflects a perception among users,

“The users with higher ranks are more likely to be familiar with or comfortable with VRChat culture and not find things so strange, which is the only thing the ranks tell me.”

As stated by the quote, the user thought the primary information that a trust rank can provide is a user's familiarity with the platform. Trust ranks become a shorthand for differentiating users familiar and comfortable with the VRChat culture from those newcomers who are easily surprised by things on VRChat.

Additionally, the rank can also serve as a valuable tool to discern users’ interest on the platform, distinguishing between those who are deeply committed and those who are more transient in nature. Users who attain higher ranks typically demonstrate prolonged and active involvement, signaling a robust interest in the platform. Consequently, many individuals utilize these ranks as a means to connect with others who share a similar level of investment and enthusiasm, preferring to interact with those who are not just briefly exploring out of curiosity, as noted by a user,

“And when it comes to elitism, I do it... with their rank. Because that informs me of the level of users’ interest in the social game. I don’t like to build relationships with people I know that they will become bored after a few weeks and then I won’t be able to see them. That’s why I want to make sure the folks I know show symptoms of enjoying the game.”

The use of trust ranks as a barometer for users’ commitment and longevity on the platform is evident in this user’s statement. High-ranking users are often seen as more engaged and less likely to abandon the platform on a whim, making them preferable companions for those seeking long-term interactions.

While intended to assess and display user trustworthiness, the system’s categorization process leads to unintended social dynamics. Trust ranks classify users into different groups, which provides the basis for stereotype formation. In addition, these ranks can hinder interaction among users with varying ranks.

The TS, designed to foster a safe and respectful online environment, paradoxically gives rise to new forms of discrimination and conflict, particularly affecting users with lower ranks. This unintended consequence stems from the hierarchical nature of the system, where different levels of trust create divisions and tensions within the community.

The implementation of trust ranks inadvertently shifts the environment towards competitiveness. High-rank users have more privileges, and ranks are dynamic and changeable, leading to a competitive atmosphere. A user illustrated the view,

“There’s literally no reason to display ”rank” ingame and the safety features should be split up in Users and Friends. VRChat is a social platform, not a competitive esport game.”

This sentiment expressed by a user underscores the discomfort with the visible hierarchy created by the trust ranks. The public display of ranks transforms what is meant to be a social, interactive space into a field of competition where users are constantly aware of their status relative to others. It is echoed by other users, for example,

“People are way too obsessed with rank these days. Just play the game, make some friends, and learn a little bit about yourself.” Replied by another user,

“Because getting a higher rank has both tangible and intangible benefits.”

In the first quote, the users noticed people are over-obsessed with ranks. It would benefit individuals to focus more on the social aspects of the platform, including expanding their social circle and self-discovery. As shown previously, to upgrade to a higher trust level, users dedicated excessive time on the platform, some exceeding one thousand hours, learning the unknown algorithm behind the TS and uploading more than a hundred models, such as worlds and avatars. The second quote revealed that the competition is partially due to the tangible and intangible benefits a higher rank can bring to users.

Newcomers play a crucial role in community growth as a way to replenish the people who leave the community (Kraut and Resnick 2012). Additionally, newcomers often bring fresh ideas and content to a platform. However, attracting and integrating them into an existing community is challenging (Kiene et al. 2016; Zhu et al. 2014). Newcomers do not yet have the same level of commitment to a group as experienced members. Therefore, they are extremely sensitive to their own early experiences on a new platform. Even minor difficulties could cause them to abandon a community altogether (Arguello et al. 2006; Schweik and English 2012). Given that social VR is an emerging online space, it is essential to attract and integrate newcomers for its continued development. However, the TS presents various challenges for newcomers. The TS’s hierarchical structure, distinguished by varying ranks, inherently fosters a power dynamic that can lead to discrimination or harmful interactions between established members and newcomers. In this sense, TS mimics the symbolic system and affords symbolic power to high-rank users (Bourdieu 1979). It is deeply ingrained in the attitudes and behaviors of these high-rank users, reinforcing the hierarchical structure that benefits and perpetuates the dominance of those in established positions of power (Cattani et al. 2014).

Trust System Hinders Newcomers’ Adaption to the Novel Online Space. The TS hinders the adaption of newcomers to the platform in several ways. First, the TS is often used alongside safety settings, which allow users to control the visibility of other users according to their ranks. A low trust rank here implies that users with the rank tend to exhibit toxic behavior or have not proven trustworthy. As a result, many highrank users often avoid contacting low-rank users by either refraining from engaging with them or directly employing safety settings to mute their voices or even making them completely invisible. Such avoidance can be harmful to newcomers and to the whole community, as it limits newcomers’ communication with other users in the novel online spaces, especially the high-rank and experienced users. Newcomers may act in ways that insult other users or otherwise undermine the community’s ability to function due to a lack of familiarity with the community norms that govern behaviors (Kraut and Resnick 2012). Social VR is a novel online space that most people have never been in contact with or have no idea of, and its norms differ from other online spaces (Blackwell et al. 2019; Maloney et al. 2020). Therefore, such avoidance hinders the learning process and leads newcomers to easily make mistakes and be punished. Second, it assigns the newcomers the lowest rank with their nameplates. The opacity and precarity of the system also create obstacles for newcomers to upgrade and discourage their participation. Compared to high-rank users, newcomers are unfamiliar with the algorithm. They need to learn how it works. Learning a new algorithm takes time and requires additional labor (Ma and Kou 2021). Lack of access and guidance from experienced users can hinder this learning, thus limiting the adaptation of newcomers.

The High-rank Oppresses and Discriminates Low-rank Users. The TS makes it more difficult for newcomers to adapt to the new community. It also facilitates the oppression of newcomers from old-timers. The ranks form a hierarchy among users with clear inferiority and superiority. The Trusted User are the most superior ones with privileges from the system, while newcomers are the Visitor at the bottom of the ladder. The TS implicitly indicates that low-rank users are toxic or unreliable. Therefore, other users often assume that newcomers are toxic or have been demoted or punished by the platform, even though they just arrived (Cai and Wohn 2021). Such a stereotype is not conducive for newcomers to adapt to the new platform, even leading to group attack to and dropout of newcomers. Furthermore, it is commonly expressed that high-rank users are generally treated much better than low-rank users.

At the same time, many high-rank users build their competitive advantages over newcomers by leveraging the opacity of the TS. High-rank users are more informed about how the system works, knowing how and what actions make users upgrade and downgrade. Instead of helping new users, many high-rank users leverage their algorithmic knowledge to suppress these newcomers. Our findings show that the high-rank maliciously demote low-rank users through activities such as blocking (blocking is generally considered harmful to a user’s rank). In some cases, high-rank users even coordinated as groups to expel low-rank users and suppress newcomers by massively blocking them.

Our study contributes to the previous literature in using the reputation system as a disciplinary system for addressing toxicity. We have found that ranks in the reputation system may impede the growth of newcomers in an online social community as the system may facilitate disconnection between newcomers and experienced users. Meanwhile, the formed hierarchy due to the assigned ranks may cause discrimination and even harassment against newcomers. Furthermore, the findings supplement the research on social VR harassment. Previous research on the prevalence of toxicity in social VR considers different reasons, such as the affordances of VR, the demographic of the typical VR user (Blackwell et al. 2019), and finally, the influence of gaming culture (Blackwell et al. 2019; Freeman et al. 2022a). Our findings show that the toxicity in social VR is also partially caused by design choices. It also evidences and expands Whitney Phinllips’ argument (Phillips 2015) that people are not the only cause for toxicity online but also system designs, further into social VR. TS also appears to enhance the tension between “game” and “social” culture, making it more difficult to find a consensus on appropriate norms given the diversity in the community. We will discuss it in the next section.

Due to the gaming culture’s influence and novelty of social VR, social VR users may interpret these online spaces in various ways, such as games, social media platforms, or others. As shown in our study, users refer to VRChat in different ways, including “game,” “social game,” and “social platform.” Trust ranks may enhance social VR users’ impression that social VR is a competitive game, thus hindering the formation of less toxic norms.

Social VR is an online space that facilitates an immersive and embodied social experience for its users. Individuals engage in various social activities in social VR, from dancing, sleeping, and partying to fulfilling their different social needs (Maloney and Freeman 2020). Social VR is heavily influenced by gaming culture (Blackwell et al. 2019). For example, many social VR platforms integrate competitive games into their platforms as part of their content, such as “Among Us¹¹” and”Prison Escape!¹²” the very popular game worlds on VRChat that require social VR users to complete designated tasks and compete with others. In addition, many of them, like VRChat and Recroom, are on the Steam Game Store for download. These factors inevitably make some users think that social VR platforms are related to games instead of only for socializing, as evidenced by the different ways viewers call them in our study. For individuals who perceive social VR as a form of a game instead of a social platform toxicity and conflicts are acceptable. Game players tend to accept toxicity, such as trolling, profanity, etc., and think that it is an inextricable part of game culture (Beres et al. 2021; Adinolf and Turkay 2018), thus behaving so. The two groups of people with different perceptions of social VR may create tensions. Many users urge and warn that the platform is meant for social use instead of competition (e.g.,“VRChat is a social platform, not a f**king competitive esport game.”). The rank design, commonly used in games, strengthens the platform’s competitiveness and enhances users’ impression that the platform is a game. The increased competitiveness due to ranks, can make it difficult and unbearable for users who come to this platform to socialize. As our findings demonstrate, many users complain that ranks make VRChat a game and cause too many users to be obsessed with the ranks instead of socializing.

In addition, the increased competitiveness may exacerbate the harassment in the platform as competitiveness fostering toxicity (Adinolf and Turkay 2018; Kou 2020, Gaikwad et al. 2016). Previous research argues that the flux of new users may encourage the reconstruction of the toxic social norms in social VR (Shriram and Schwartz 2017). However, the premise is that more newcomers consider those platforms as social spaces instead of competitive esports. Designs like TS that encourage competition through ranks can strengthen the view that the platform is for gaming instead of socializing and, therefore, support game-oriented norms with a high tolerance for toxicity.

Based on our findings, it appears that many users have faith in TS because they perceive its logic in managing toxicity as reasonable. Specifically, if a user consistently displays toxic behavior towards others, then the more time they spend on the platform, the more likely they are to be reported, blocked, and vote-kicked (those users generally believe the actions have negative impacts on their ranks). Thus, toxic users cannot upgrade their rank. But at the same time, the inappropriate implementation of the system breeds doubt about its effectiveness. Our findings evidence and expand Whitney Phinllips’ argument (Phillips 2015) that people are not the only cause for toxicity online but also system designs. In this study, we argue that when integrating a design into a novel context, we should be cautious of the negative consequences of design choices. Based on the challenges we identified in the TS, we provide several suggestions. Since the VR environment is highly immersive and embedded, we also aligned the design implications with real-world cases to show their potential to improve the TS design. By addressing these implications, our goal is to improve the functionality and fairness of the TS, fostering a more inclusive and supportive environment within the social VR community.