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Many researchers studying online communities seek to make them better. However, beyond a small set of widely-held values, such as combating misinformation and abuse, determining what `better’ means can be challenging, as community members may disagree, values may be in conflict, and different communities may have differing preferences as a whole.In this work, we present the first study that elicits values directly from members across a diverse set of communities.We survey 212 members of 627 unique subreddits and ask them to describe their values for their communities in their own words. Through iterative categorization of 1,481 responses, we develop and validate a comprehensive taxonomy of community values, consisting of 29 subcategories within nine top-level categories enabling principled, quantitative study of community values by researchers. Using our taxonomy, we reframe existing research problems, such as managing influxes of new members, as tensions between different values, and we identify understudied values, such as those regarding content quality and community size. We call for greater attention to vulnerable community members' values, and we make our codebook public for use in future research.

 

Social media systems are as varied as they are pervasive. They have been almost universally adopted for a broad range of purposes including work, entertainment, activism, and decision making. As a result, they have also diversified, with many distinct designs differing in content type, organization, delivery mechanism, access control, and many other dimensions. In this work, we aim to characterize and then distill a concise design space of social media systems that can help us understand similarities and differences, recognize potential consequences of design choice, and identify spaces for innovation. Our model, which we call Form-From, characterizes social media based on (1) the form of the content, either threaded or flat, and (2) from where or from whom one might receive content, ranging from spaces to networks to the commons. We derive Form-From inductively from a larger set of 62 dimensions organized into 10 categories. To demonstrate the utility of our model, we trace the history of social media systems as they traverse the Form-From space over time, and we identify common design patterns within cells of the model.

 

The increasing harms caused by hate, harassment, and other forms of abuse online have motivated major platforms to explore hierarchical governance. The idea is to allow communities to have designated members take on moderation and leadership duties; meanwhile, members can still escalate issues to the platform. But these promising approaches have only been explored in plaintext settings where community content is public to the platform. It is unclear how one can realize hierarchical governance in the huge and increasing number of online communities that utilize end-to-end encrypted (E2EE) messaging for privacy. We propose the design of private, hierarchical governance systems. These should enable similar levels of community governance as in plaintext settings, while maintaining cryptographic privacy of content and governance actions not reported to the platform. We design the first such system, taking a layered approach that adds governance logic on top of an encrypted messaging protocol; we show how an extension to the message layer security (MLS) protocol suffices for achieving a rich set of governance policies. Our approach allows developers to rapidly prototype new governance features, taking inspiration from a plaintext system called PolicyKit. We report on an initial prototype encrypted messaging system called MlsGov that supports content-based community and platform moderation, elections of community moderators, votes to remove abusive users, and more. 

Social computing is the study of how technology shapes human social interactions. This topic has become increasingly relevant to secondary school students (ages 11--18) as more of young people's everyday social experiences take place online, particularly with the continuing effects of the COVID-19 pandemic. However, social computing topics are rarely touched upon in existing middle and high school curricula. We seek to introduce concepts from social computing to secondary school students so they can understand how computing has wide-ranging social implications that touch upon their everyday lives, as well as think critically about both the positive and negative sides of different social technology designs. In this report, we present a series of six lessons combining presentations and hands-on activities covering topics within social computing and detail our experience teaching these lessons to approximately 1,405 students across 13 middle and high schools in our local school district. We developed lessons covering how social computing relates to the topics of Data Management, Encrypted Messaging, Human-Computer Interaction Careers, Machine Learning and Bias, Misinformation, and Online Behavior. We found that 81.13% of students expressed greater interest in the content of our lessons compared to their interest in STEM overall. We also found from pre- and post-lesson comprehension questions that 63.65% learned new concepts from the main activity. We release all lesson materials on a website for public use. From our experience, we observed that students were engaged in these topics and found enjoyment in finding connections between computing and their own lives. 

When groups of people are tasked with making a judgment, the issue of uncertainty often arises. Existing methods to reduce uncertainty typically focus on iteratively improving specificity in the overall task instruction. However, uncertainty can arise from multiple sources, such as ambiguity of the item being judged due to limited context, or disagreements among the participants due to different perspectives and an under-specified task. A one-size-fits-all intervention may be ineffective if it is not targeted to the right source of uncertainty. In this paper we introduce a new workflow, Judgment Sieve, to reduce uncertainty in tasks involving group judgment in a targeted manner. By utilizing measurements that separate different sources of uncertainty during an initial round of judgment elicitation, we can then select a targeted intervention adding context or deliberation to most effectively reduce uncertainty on each item being judged. We test our approach on two tasks: rating word pair similarity and toxicity of online comments, showing that targeted interventions reduced uncertainty for the most uncertain cases. In the top 10% of cases, we saw an ambiguity reduction of 21.4% and 25.7%, and a disagreement reduction of 22.2% and 11.2% for the two tasks respectively. We also found through a simulation that our targeted approach reduced the average uncertainty scores for both sources of uncertainty as opposed to uniform approaches where reductions in average uncertainty from one source came with an increase for the other. 

Many have criticized the centralized and unaccountable governance of prominent online social platforms, leading to renewed interest in platform governance that incorporates multiple centers of power. Decentralization of power can arise horizontally, through parallel communities, each with local administration, and vertically, through multiple hierarchies of overlapping jurisdiction. Drawing from literature on federalism and polycentricity in analogous offline institutions, we scrutinize the landscape of existing platforms through the lens of multi-level governance. Our analysis describes how online platforms incorporate varying forms and degrees of decentralized governance. In particular, we propose a framework that characterizes the general design space and the various ways that middle levels of governance vary in how they can interact with a centralized governance system above and end users below. This focus provides a starting point for new lines of inquiry between platform- and community-governance scholarship. By engaging themes of decentralization, hierarchy, power, and responsibility, while discussing concrete examples, we connect designers and theorists of online spaces.

 

User reporting is an essential component of content moderation on many online platforms--in particular, on end-to-end encrypted (E2EE) messaging platforms where platform operators cannot proactively inspect message contents. However, users' privacy concerns when considering reporting may impede the effectiveness of this strategy in regulating online harassment. In this paper, we conduct interviews with 16 users of E2EE platforms to understand users' mental models of how reporting works and their resultant privacy concerns and considerations surrounding reporting. We find that users expect platforms to store rich longitudinal reporting datasets, recognizing both their promise for better abuse mitigation and the privacy risk that platforms may exploit or fail to protect them. We also find that users have preconceptions about the respective capabilities and risks of moderators at the platform versus community level--for instance, users trust platform moderators more to not abuse their power but think community moderators have more time to attend to reports. These considerations, along with perceived effectiveness of reporting and how to provide sufficient evidence while maintaining privacy, shape how users decide whether, to whom, and how much to report. We conclude with design implications for a more privacy-preserving reporting system on E2EE messaging platforms.