Personally identifiable information (PII) anonymization is a high-stakes task that poses a barrier to many open-science data sharing initiatives.While PII identification has made large strides in recent years, in practice, error thresholds and the recall/precision trade-off still limit the uptake of these anonymization pipelines.We present PIIvot, a lighter-weight framework for PII anonymization that leverages knowledge of the data context to simplify the PII detection problem.

Online health communities (OHCs) offer the promise of connecting with supportive peers. Forming these connections first requires finding relevant peers—a process that can be time-consuming. Peer recommendation systems are a computational approach to make finding peers easier during a health journey. By encouraging OHC users to alter their online social networks, peer recommendations could increase available support. But these benefits are hypothetical and based on mixed, observational evidence. To experimentally evaluate the effect of peer recommendations, we conceptualize these systems as health interventions designed to increase specific beneficial connection behaviors. In this paper, we designed a peer recommendation intervention to increase two behaviors: reading about peer experiences and interacting with peers. We conducted an initial feasibility assessment of this intervention by conducting a 12-week field study in which 79 users of CaringBridge.org received weekly peer recommendations via email. Our results support the usefulness and demand for peer recommendation and suggest benefits to evaluating larger peer recommendation interventions. Our contributions include practical guidance on the development and evaluation of peer recommendation interventions for OHCs.

How does online anonymity impact the perceived effectiveness of person-centered recovery support messages? In online health communities, privacy preferences can be at odds with community goals for diversity, similar others, and effective support exchange. Prior work has shown how the multifaceted concept of identity plays an important role in support-seeking behaviors, but its effects concerning support providers are still unclear. Through a mixed-methods controlled online experiment, we demonstrate the effects of different facets of anonymity and person-centeredness in comments on their perceived effectiveness. We found that in instances of low person-centered support, the presence of in-group identity disclosures had the strongest effect on facilitating positive support outcomes. We contextualize our findings with an analysis of participants' motivations for individual support preferences related to person-centered, social identity disclosures, and recovery practices. Finally, we discuss the implications for the design of supporting diverse support preferences in online recovery communities.

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.