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Knowledge-based institutions, such as libraries and archives, are ripe venues for knowledge gaps, defined as systemic disparities in content coverage or participation. The core problem is not that knowledge gaps exist, but that they often further disempower already marginalized communities. By reinforcing systemic inequity, knowledge gaps are matters of justice. In this paper, we draw on critical scholarship about knowledge exclusion and take a design lens to a pressing instantiation: knowledge gaps on Wikipedia. We present findings from a two-day Wikipedia expert workshop focused on how knowledge gaps manifest in editors’ interactions with platform policy. Our core contribution is an empirically grounded argument that a more inclusive Wikipedia is one in which editors can acquire the skills to invoke, bend, and even break the platform policies that dictate encyclopedic content. To make this argument, we synthesize author testimony into (1) a problem space articulating contributing factors and (2) a two-dimensional solution space where policy engagement can be at odds with policy adherence. To illustrate this inclusive Wikipedia future, we present speculative prototypes along the engagement-adherence matrix. Finally, we discuss the epistemic implications of our work, returning to formative philosophies on knowledge inclusion and justice. 

As large language models (LLMs) have proliferated, disturbing anecdotal reports of negative psychological e!ects, such as delusions, self-harm, and “AI psychosis,” have emerged in global media and legal discourse. However, it remains unclear how users and chatbots interact over the course of lengthy delusional “spirals,” limiting our ability to understand and mitigate the harm. In our work, we analyze logs of conversations with LLM chatbots from 19 users who report having experienced psychological harms from chatbot use. Many of our participants come from a support group for such chatbot users. We also include chat logs from participants covered by media outlets in widely-distributed stories about chatbot-reinforced delusions. In contrast to prior work that speculates on potential AI harms to mental health, to our knowledge we present the first in-depth study of such high-profile and veridically harmful cases. We develop an inventory of 28 codes and apply it to the 391,562 messages in the logs. Codes include whether a user demonstrates delusional thinking (15.5% of user messages), a user expresses suicidal thoughts (69 validated user messages), or a chatbot misrepresents itself as sentient (21.2% of chatbot messages). We analyze the co-occurrence of message codes. We find, for example, that messages that declare romantic interest and messages where the chatbot describes itself as sentient occur much more often in longer conversations, suggesting that these topics could promote or result from user over-engagement and that safeguards in these areas may degrade in multi-turn settings. We conclude with concrete recommendations for how policymakers, LLM chatbot developers, and users can use our inventory and conversation analysis tool to understand and mitigate harm from LLM chatbots. 

Online community research routinely poses minimal risk to individuals, but does the same hold true for online communities? In response to high-profile breaches of online community trust and increased debate in the social computing research community on the ethics of online community research, this paper investigates community-level harms and benefits of research. Through 9 participatory-inspired workshops with four critical online communities (Wikipedia, InTheRooms, CaringBridge, and r/AskHistorians), we found researchers should engage more directly with communities' primary purpose by rationalizing their methods and contributions in the context of community goals to equalize the beneficiaries of community research. To facilitate deeper alignment of these expectations, we present the FACTORS (Functions for Action with Communities: Teaching, Overseeing, Reciprocating, and Sustaining) framework for ethical online community research. Finally, we reflect on our findings by providing implications for researchers and online communities to identify and implement functions for navigating community-level harms and benefits. 

While online community research is prevalent in CSCW, there are limited ethical principles for conducting research that may affect online communities. At the same time, a growing body of evidence suggests that traditional ethical review focused on research with individuals fails to fully capture the complexities of online community research. To support advancing ethical online community research, we propose a one-day hybrid workshop centered around tensions and challenges in adopting best practices for ethical online community research. This workshop aims to bring together online community researchers to 1) recognize existing approaches for ethical online community research, 2) expose gaps in current practices, and 3) prioritize directions to reconcile these ethical challenges. 

Strategies for engaging in more ethical research with online communities 

Applied machine learning (ML) has not yet coalesced on standard practices for research ethics. For ML that predicts mental illness using social media data, ambiguous ethical standards can impact peoples’ lives because of the area’s sensitivity and material con- sequences on health. Transparency of current ethics practices in research is important to document decision-making and improve research practice. We present a systematic literature review of 129 studies that predict mental illness using social media data and ML, and the ethics disclosures they make in research publications. Rates of disclosure are going up over time, but this trend is slow moving – it will take another eight years for the average paper to have coverage on 75% of studied ethics categories. Certain practices are more readily adopted, or "stickier", over time, though we found pri- oritization of data-driven disclosures rather than human-centered. These inconsistently reported ethical considerations indicate a gap between what ML ethicists believe ought to be and what actually is done. We advocate for closing this gap through increased trans- parency of practice and formal mechanisms to support disclosure. 

Online volunteers are an uncompensated yet valuable labor force for many social platforms. For example, volunteer content moderators perform a vast amount of labor to maintain online communities. However, as social platforms like Reddit favor revenue generation and user engagement, moderators are under-supported to manage the expansion of online communities. To preserve these online communities, developers and researchers of social platforms must account for and support as much of this labor as possible. In this paper, we quantitatively characterize the publicly visible and invisible actions taken by moderators on Reddit, using a unique dataset of private moderator logs for 126 subreddits and over 900 moderators. Our analysis of this dataset reveals the heterogeneity of moderation work across both communities and moderators. Moreover, we find that analyzing only visible work – the dominant way that moderation work has been studied thus far – drastically underestimates the amount of human moderation labor on a subreddit. We discuss the implications of our results on content moderation research and social platforms. 

Online volunteers are a crucial labor force that keeps many for-profit systems afloat (e.g. social media platforms and online review sites). Despite their substantial role in upholding highly valuable technological systems, online volunteers have no way of knowing the value of their work. This paper uses content moderation as a case study and measures its monetary value to make apparent volunteer labor’s value. Using a novel dataset of private logs generated by moderators, we use linear mixed-effect regression and estimate that Reddit moderators worked a minimum of 466 hours per day in 2020. These hours are worth 3.4 million USD based on the median hourly wage for comparable content moderation services in the U.S. We discuss how this information may inform pathways to alleviate the one-sided relationship between technology companies and online volunteers.