Early recovery after substance use disorder (SUD) treatment is a period of high risk. The majority of people will relapse, often within weeks of completing treatment. In the modern era, re-entry upon completion of treatment includes both digital and non-digital spaces. Digital spaces, including social media, present unique challenges to the recovery journey. However, research has rarely focused on this critical period and the ways in which technology affect it. We conducted in-depth interviews with 29 participants (8 recoverees and 21 support professionals) across two treatment sites to explore this gap. Using an inductive thematic approach, we gained insights into digital social re-entry, a term that we introduce to describe the process of re-engaging with social spaces online. We describe the work of digital social re-entry, which includes 1) remaking social networks, 2) maintaining boundaries, 3) managing triggering content, 4) resisting access to substances, and 5) shifting personal identity. We conclude by characterizing strategies for navigating digital social re-entry and discussing ways to better support recoverees during this aspect of their recovery journey.

The user study is a fundamental method used in HCI. In designing user studies, we often use compensation strategies to incentivize recruitment. However, compensation can also lead to ethical issues, such as coercion. The CHI community has yet to establish best practices for participant compensation. Through a systematic review of manuscripts at CHI and other associated publication venues, we found high levels of variation in the compensation strategies used within the community and how we report on this aspect of the study methods. A qualitative analysis of justifications offered for compensation sheds light into how some researchers are currently contextualizing this practice. This paper provides a description of current compensation strategies and information that can inform the design of compensation strategies in future studies. The findings may be helpful to generate productive discourse in the HCI community towards the development of best practices for participant compensation in user studies.

The use of legal and illegal drugs has grown to such an acute level that it now represents a public health crisis in the United States. To support clinical treatments of substance use disorders (SUDs), formal non-clinical peer recovery support programs pairing coaches with people new to recovery are gaining in popularity. Using a user-centered design approach, we designed a mobile application to support the peer coach recovery program of a health system. The application addresses the needs associated with the coaches' workflows, encompasses social supports for recoverees, and provides a space for fostering the coach-recoveree relationship. Finally, we then evaluated a prototype with recoverees and program coaches. Through this process, we identified tensions between stakeholder needs and translated these tensions into design features and future design considerations.

Bayesian statistical analysis has gained attention in recent years, including in HCI. The Bayesian approach has several advantages over traditional statistics, including producing results with more intuitive interpretations. Despite growing interest, few papers in CHI use Bayesian analysis. Existing tools to learn Bayesian statistics require significant time investment, making it difficult to casually explore Bayesian methods. Here, we present a tool that lowers the barrier to exploration: a set of R code templates that guide Bayesian novices through their first analysis. The templates are tailored to CHI, supporting analyses found to be most common in recent CHI papers. In a user study, we found that the templates were easy to understand and use. However, we found that participants without a statistical background were not confident in their use. Together our contributions provide a concise analysis tool and empirical results for understanding and addressing barriers to using Bayesian analysis in HCI.

E-commerce designers must decide how many products to display at one time. Choice overload research has demonstrated the surprising finding that more choice is not necessarily better?selecting from larger choice sets can be more cognitively demanding and can result in lower levels of choice satisfaction. This research tests the choice overload effect in an e-commerce context and explores how the choice overload effect is influenced by an individual's tendency to maximize or satisfice decisions. We conducted an online experiment with 611 participants randomly assigned to select a gourmet chocolate bar from either 12, 24, 40, 50, 60, or 72 different options. Consistent with prior work, we find that maximizers are less satisfied with their product choice than satisficers. However, using Bayesian analysis, we find that it's unlikely that choice set size affects choice satisfaction by much, if at all. We discuss why the decision-making process may be different in e-commerce contexts than the physical settings used in previous choice overload experiments.

Many social media systems explicitly connect individuals (e.g., Facebook or Twitter); as a result, they are the targets of most research on social networks. However, many systems do not emphasize or support explicit linking between people (e.g., Wikipedia or Reddit), and even fewer explicitly link communities. Instead, network analysis is performed through inference on implicit connections, such as co-authorship or text similarity. Depending on how inference is done and what data drove it, different networks may emerge. While correlated structures often indicate stability, in this work we demonstrate that differences, or misalignment, between inferred networks also capture interesting behavioral patterns. For example, high-text but low-author similarity often reveals communities "at war" with each other over an issue or high-author but low-text similarity can suggest community fragmentation. Because we are able to model edge direction, we also find that asymmetry in degree (in-versus-out) co-occurs with marginalized identities (subreddits related to women, people of color, LGBTQ, etc.). In this work, we provide algorithms that can identify misaligned links, network structures and communities. We then apply these techniques to Reddit to demonstrate how these algorithms can be used to decipher inter-group dynamics in social media.

Undergraduates interviewed about privacy concerns related to online data collection made apparently contradictory statements. The same issue could evoke concern or not in the span of an interview, sometimes even a single sentence. Drawing on dual-process theories from psychology, we argue that some of the apparent contradictions can be resolved if privacy concern is divided into two components we call intuitive concern, a "gut feeling," and considered concern, produced by a weighing of risks and benefits. Consistent with previous explanations of the so-called privacy paradox, we argue that people may express high considered concern when prompted, but in practice act on low intuitive concern without a considered assessment. We also suggest a new explanation: a considered assessment can override an intuitive assessment of high concern without eliminating it. Here, people may choose rationally to accept a privacy risk but still express intuitive concern when prompted.