Following Clubhouse's surge in popularity, there has been a rise in the development of other audio-based platforms, as well as the inclusion of audio-calling features to existing platforms. Unlike other platforms such as Discord, Clubhouse is entirely audio-based, and is not organized by specific communities. Our findings indicate that we can predict video deletion/removal with high accuracy even at the time of posting-a strategy that could help users perceive the removal of their videos as fair as well as reduce public and moderators exposure to problematic videos.Ĭlubhouse is an audio-based social platform that launched in April 2020 and rose to popularity amidst the global COVID-19 pandemic. Based on our analysis, we have developed machine learning prediction models that predict videos that will get deleted or removed at three different stages of a video’s lifetime, namely at the time of posting, 3 h after a video has been posted, and after up to 7 days have elapsed. We have then conducted a large-scale analysis of this data and reported on the most informative features that distinguish deleted/removed videos from the ones that remain available, as well as the features that distinguish videos removed by YouTube from those deleted by the uploader. To do this, we tracked over 73,000 recent YouTube videos for 1 week and identified those that got deleted or removed. In this paper, we provide a detailed analysis of videos removed by YouTube or deleted by the uploader. In addition to YouTube removing videos, uploaders sometimes delete their videos for a variety of reasons, such as paraphrasing or preserving online self-image. Such strategies, especially if seen as unjust by the affected users, could be met with resentment, anger, and in some cases, violence. YouTube strives to moderate its content by censoring, demonetizing or removing videos that allegedly violate their community guidelines. With deeper insights into the patterns of successful self-governance, we can help more communities overcome the challenges of self-governance and create for their members powerful experiences of shared meaning and collective empowerment. These findings suggest that designers and founders of online communities converge on styles of governance practice that are correlated with successful self-governance. Second, we find that online communities employ similar governance styles across platforms, strongly favoring 'weak' norms to 'strong' requirements. First, institutional formalization, the size and complexity of an online community's governance system, is generally positively associated with maturity, as measured by age, population size, or degree of user engagement. From this analysis we extract two major findings. We classify communities' written rules to test predictors of institutional formalization. To understand the relationship of formal institutions to community maturity and governance style, we conduct a large-scale quantitative analysis applying institutional analysis frameworks of self-governance scholar Elinor Ostrom to 80,000 communities across 3 platforms: the sandbox game Minecraft, the MMO game World of Warcraft, and Reddit. But in a community with formal rules, decisions are more consistent, transparent, and legitimate. Indeed, scholars often downplay the role that formal rules relative to unwritten rules, norms, and values.

Alternatively, they may require less formalization precisely because of their maturity. Communities that are more mature and established may have more elaborate formal policy systems. But the characteristics of successful institutional designs are unclear.

This challenge often requires communities to design formal governance systems from scratch. Building a successful community means governing active populations and limited resources.

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