With the increasing adoption of automatic text classifications driven by AI, there is a growing need to explore their safe and accurate applications, particularly in sensitive online communities. Sentiment analysis of X (formerly known as Twitter) data is widely used by researchers to automatically categorize textual data, providing valuable insights into the content of specific online communities. In this study, we investigate the effectiveness of automatic sentiment classification models (TextBlob and Vader) by analyzing n=6930 #meanspo tagged tweets from 2020 to 2022 from X. This community is known for promoting harmful eating disorder related content, often in a harsh and derogatory manner. By comparing these models with human coding, our analysis reveals significant limitations in the models' ability to capture the nuanced contextual values inherent to these communities. Both TextBlob and Vader demonstrate poor performance compared to human coding, highlighting the need for improved sentiment analysis techniques tailored to sensitive online communities like #meanspo. Additional limitations occur when media is attached with tweets contributing to the sentiments. This study contextualizes how human involvement and expertise are essential for exploring these communities, as relying solely on automatic classifications can be risky and fail to grasp the complex dynamics and implications of such online interactions. Future contextual work is essential to evaluate the risks and harms of using automatic classification models in sensitive online communities and to develop effective human-centered strategies to mitigate these impacts. TRIGGER WARNING: potentially triggering content.

COVID Long Haul (CLH) is an emerging chronic illness with varied patient experiences. Our understanding of CLH is often limited to data from electronic health records (EHRs), such as diagnoses or problem lists, which do not capture the volatility and severity of symptoms or their impact. To better understand the unique presentation of CLH, we conducted a 3-month long cohort study with 14 CLH patients, collecting objective (EHR, daily Fitbit logs) and subjective (weekly surveys, interviews) data. Our findings reveal a complex presentation of symptoms, associated uncertainty, and the ensuing impact CLH has on patients’ personal and professional lives. We identify patient needs, practices, and challenges around adhering to clinical recommendations, engaging with health data, and establishing "new normals" post COVID. We reflect on the potential found at the intersection of these various data streams and the persuasive heuristics possible when designing for this new population and their specific needs.

COVID Long Haul (CLH) is an emerging chronic illness for which the healthcare system continues to seek a common understanding of symptoms, diagnosis, and treatment. CLH experiences can differ drastically, necessitating personalized care plans. Because patients interact with different clinicians during their CLH journey, it becomes important to ensure interoperability and understand clinical relevance of different data that can support clinicians in making appropriate recommendations. We conducted qualitative research where we interviewed 13 patients, conducted a focus group with 8 clinicians, and analyzed care plan follow-up records. We report patient and clinician expectations from and interactions with clinic data. We uncover logistical challenges, personal contexts, and health barriers impacting patient compliance. As researchers embedded in the clinical system, we identify the potential of using multiple patient data streams to support personalized treatment and clinical decisions. We discuss technology design opportunities and provide actionable recommendations for improving clinical workflows and cross-provider collaboration.

Access to mortality data is critical for researchers for various reasons, including the development of life-saving medical interventions. However, high-quality mortality data is typically unavailable for researchers to access in a timely manner due to bottlenecks and inefficiencies in the coordination of managing mortality data across a vast information ecosystem. To identify opportunities to improve access to mortality data, we conducted a qualitative interview study with 20 experts with direct knowledge and experience with mortality data systems. From these interviews, we mapped the mortality data ecosystem and found that policy constraints are the main underlying cause of bottlenecks and inefficiencies. We argue that policy intervention is required to optimize the coordination of mortality data between databases and between organizations. To support researcher access to mortality data, we present guiding principles for designers seeking to improve the mortality data ecosystem. These principles contribute to CSCW scholarship focused on the challenges of coordination across large information ecosystems and the tradeoffs between technology and policy when designing such systems.

Meanspo, an antagonistic form of online support within the eating disorder (ED) community, involves the direct solicitation or sharing of aggressive and insulting online content. This study presents findings from a comprehensive qualitative analysis of #meanspo content on X (previously Twitter ) from May 2020 (N=752). Our analysis of tweets reveals that posts tagged with #meanspo can be of various natures. While commonly associated with extremely derogatory ED content, more than 80% of posts with the meanspo tag on X were non-aggressive. The study also explores potential inconsistencies in voluntary and involuntary meanspo specific content moderation, prompting inquiries into X's regulatory policies against such content and the distinct online self-presentation strategies employed by community members. Future contextual research is needed to understand the evolving nature of this social phenomenon and its potential clinical impacts on users over time, particularly concerning the unhealthy adoption of such content. TRIGGER WARNING: Explicit language & potentially triggering content.

The COVID-19 pandemic transformed many aspects of health and daily life. A subset of people who were infected with the virus have ongoing chronic health issues that range in type of symptom and severity. In this study, we conducted a qualitative assessment of self-reported post-COVID symptoms from patients' electronic health records (EHR, n=564) and a randomized collection of Reddit and Twitter posts (n=500 for each). We show the inconsistencies in what types of symptoms are shared between platforms in addition to assessing the severity of the symptoms and how social media characterizations of post-COVID do not tell a complete story of this phenomenon. This research contributes to CSCW health literature by connecting digital traces of post-COVID with EHR data, critiquing the use of social media as a health proxy and points to its potential to add context to the analysis of traditional health data extracted from the EHR.

Explorations into groupwork research have expanded over the years to encompass broader themes like human-centered computing, human-computer interaction, social computing, and human- centered data science, and other related contexts. Like the previous PACM:HCI volumes for Group Work (volumes 5 and 6), this volume contains the tradition of design fictions in addition to short- form and long-from research articles. The work presented in this issue spans a wide range of disciplines, methods, and theoretical foundations. Paper topics range from perceptions of justice and participation to using AI to increase inclusivity and participation to safety. Application domains were varied ranging from social media norms to internet safety to robotics to collaborative programming and beyond. Specific communities were also the focus of the research, including specific gender and ethnic groups to non-profits and other community groups.

OBJECTIVE: We investigated patient experiences with medication- and test-related cost conversations with healthcare providers to identify their preferences for future informatics tools to facilitate cost-sensitive care decisions. MATERIALS AND METHODS: We conducted 18 semistructured interviews with diverse patients (ages 24-81) in a Midwestern health system in the United States. We identified themes through 2 rounds of qualitative coding. RESULTS: Patients believed their providers could help reduce medication-related costs but did not see how providers could influence test-related costs. Patients viewed cost conversations about medications as beneficial when providers could adjust medical recommendations or provide resources. However, cost conversations did not always occur when patients felt they were needed. Consequently, patients faced a "cascade of work" to address affordability challenges. To prevent this, collaborative informatics tools could facilitate cost conversations and shared decision-making by providing information about a patient's financial constraints, enabling comparisons of medication/testing options, and addressing transportation logistics to facilitate patient follow-through. DISCUSSION: Like providers, patients want informatics tools that address patient out-of-pocket costs. They want to discuss healthcare costs to reduce the frequency of unaffordable costs and obtain proactive assistance. Informatics interventions could minimize the cascade of patient work through shared decision-making and preventative actions. Such tools might integrate information about efficacy, costs, and side effects to support decisions, present patient decision aids, facilitate coordination among healthcare units, and eventually improve patient outcomes. CONCLUSION: To prevent a burdensome cascade of work for patients, informatics tools could be designed to support cost conversations and decisions between patients and providers.

A growing body of HCI research has sought to understand how online networks are utilized in the adoption and maintenance of disordered activities and behaviors associated with mental illness, including eating habits. However, individual-level influences over discrete online eating disorder (ED) communities are not yet well understood. This study reports results from a comprehensive network and content analysis (combining computational topic modeling and qualitative thematic analysis) of over 32,000 public tweets collected using popular ED-related hashtags during May 2020. Our findings indicate that this ED network in Twitter consists of multiple smaller ED communities where a majority of the nodes are exposed to unhealthy ED contents through retweeting certain influential central nodes. The emergence of novel linguistic indicators and trends (e.g., "#meanspo") also demonstrates the evolving nature of the ED network. This paper contextualizes ED influence in online communities through node-level participation and engagement, as well as relates emerging ED contents with established online behaviors, such as self-harassment.

Adverse Childhood Events (ACEs) are potentially traumatic events that occur in childhood (e.g., sexual abuse and maternal violence). Clinical research highlights the significant impact ACEs have on youth's mental health similar to other youth-related issues like traditional bullying and cyberbullying. However, research focused on the intersection of these two are limited. We report the results from a qualitative study that used electronic health record (EHR) data and clinical narratives from Parkview Behavioral Health hospital (n=719) to better understand the presentation of ACEs in patients who indicated cyber/bullying contributed to their inpatient hospital admission. Our deductive thematic analyses on the clinical narratives/notes and diagnoses highlight the connection of ACEs with cyber/bullying and other clinical diagnoses like depression, anxiety, PTSD, and ADD/ADHD. Additionally, our results point to potential impacts of the gender spectrum and other non-ACE indicators like adoption and the need for Department of Child Services (DCS). The outcome of this study provides distinct computational and clinical design guidelines for better collaborative decision making in healthcare, including the need for ACEs screening as standard-of-care within acute mental health settings. CAUTION: This paper includes graphic contents about adverse childhood traumas and events.

Many research communities routinely conduct activities that fall outside the bounds of traditional human subjects research, yet still frequently rely on the determinations of institutional review boards (IRBs) or similar regulatory bodies to scope ethical decision-making. Presented as a U.S. university-based fictional memo describing a post-hoc IRB review of a research study about social media and public health, this design fiction draws inspiration from current debates and uncertainties in the HCI and social computing communities around issues such as the use of public data, privacy, open science, and unintended consequences, in order to highlight the limitations of regulatory bodies as arbiters of ethics and the importance of forward-thinking ethical considerations from researchers and research communities.

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.

A growing body of research in HCI focuses on understanding how social media and other social technologies impact a given user’s mental health, including eating disorders. In this paper, we review the results of an interview study with 10 clinicians spanning various specialties who treat people with eating disorders, in order to understand the clinical contexts of eating disorders and social media use. We found various tensions related to clinician comfort and education into the (mis)use of technologies and balancing the positive and negative aspects of social media use within active disease states as well as in recovery. Understanding these tensions as well as the variation in the current process of diagnosing patients is a critical component in connecting HCI research focused on eating disorders to clinical practice and ultimately assessing how digital self-harm could be addressed clinically in the future.

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.

Eating disorders (EDs) are a worldwide public health concern that impact approximately 10% of the U.S. population. Our previous research characterized these behaviors across online spaces. These characterizations have used clinical terminology, and their lexical variants, to identify ED content online. However, previous HCI research on EDs (including our own) suffers from a lack of gender and cultural diversity. In this paper, we designed a follow-up study of online ED characterizations, extending our previous methodologies to focus specifically on male/masculine-related content. We highlight the similarities and differences found in the terminology utilized and media archetypes associated with the social media content. Finally, we discuss other considerations highlighted through our analysis of the male-related content that is missing from the previous research.

OBJECTIVE: Researchers conduct studies with selection biases, which may limit generalizability and outcomes of intervention research. In this methodological reflection, we examined demographic and health characteristics of implantable cardioverter defibrillator patients who were excluded from an informatics intervention due to lack of access to a computer and/or the internet. MATERIALS AND METHODS: Using information gathered from surveys and electronic health records, we compared the intervention group to excluded patients on demographic factors, computer skills, patient activation, and medical history. RESULTS: Excluded patients were older, less educated, less engaged and activated in their health, and had worse health (ie, more medical comorbidities) than nonexcluded patients. DISCUSSION: Although excluded from the intervention based solely on lack of access to a computer and/or internet, excluded patients may have needed the intervention more because they were sicker with more comorbidities. CONCLUSION: Researchers must be mindful of enrollment biases and demographic and health-related inequities that may exist during recruitment for technology-based interventions.

Digital self-harm encompasses a variety of activities, including the use of social media to facilitate or amplify mental illness-related behaviors. To understand the extent of these behaviors and their impacts, we conducted an in-depth case study with three patients who are in recovery from an eating disorder. We collected survey data, conducted interviews reflecting back to their technology use during their active disease state, and reviewed up to 18 months of their social media data leading up to the start of their initial point of recovery. Through the triangulation of this data, we explore the role of social media and social technologies in relation to their eating disorder. By utilizing this methodology, we were able to provide a contextually rich and nuanced lens for exploring the impacts of digital self-harm on this group of patients. We found that patients acknowledged that the eating disordered content on social media had a negative impact on their health, often contributing to a worsening of the physical manifestations of their disorder. Conversely, while they actively consumed this content, our participants did not produce online content related to eating disordered activities or behaviors. Finally, we discuss the patterns within their social media data and how platform designers and operators could use these findings in the future through design considerations for future platform-based interventions.

As technology and data access continue to evolve, research ethics in the areas of Human-Computer Interaction and social computing are becoming increasingly complex. Despite increasing interest among researchers, there is still a lack of consistent community norms around ethical gray areas. One charge of the SIGCHI ethics committee is to help develop these norms by facilitating open conversations with different stakeholders. This panel will be an opportunity to develop a collective understanding of diverse perspectives on ethics, and to gather input from the GROUP research community around the ethical challenges we face as researchers who study social and collaborative computing systems and those who use these systems.

Social media sites are challenged by both the scale and variety of deviant behavior online. While algorithms can detect spam and obscenity, behaviors that break community guidelines on some sites are difficult because they have multimodal subtleties (images and/or text). Identifying these posts is often regulated to a few moderators. In this paper, we develop a deep learning classifier that jointly models textual and visual characteristics of pro-eating disorder content that violates community guidelines. Using a million Tumblr photo posts, our classifier discovers deviant content efficiently while also maintaining high recall (85%). Our approach uses human sensitivity throughout to guide the creation, curation, and understanding of this approach to challenging, deviant content. We discuss how automation might impact community moderation, and the ethical and social obligations of this area.

Self-harm is the infliction of pain or injury onto oneself. Though historically these behaviors were relegated to the fringes of communities, information technology now enables new ways to foster and encourage these dangerous activities. This paper defines the concept of digital self-harm as the online communication and activity that leads to, supports, or exacerbates, non-suicidal yet intentional harm or impairment of an individual's physical wellbeing. We outline a research agenda for the CSCW community to understand the correlation and possible causation of offline self-harm behaviors due to online activities, and to design and assess technologies focused on prevention, mitigation and treatment. CAUTION: This paper includes media that could potentially be triggering to those dealing with an eating disorder or with other self-harm related illnesses. Please use caution when reading or disseminating this paper.

Pro-eating disorder (pro-ED) communities on social media encourage the adoption and maintenance of disordered eating habits as acceptable alternative lifestyles rather than threats to health. In particular, the social networking site Instagram has reacted by banning searches on several pro-ED tags and issuing content advisories on others. We pre-sent the first large-scale quantitative study investigating pro-ED communities on Instagram in the aftermath of moderation -- our dataset contains 2.5M posts between 2011 and 2014. We find that the pro-ED community has adopted non-standard lexical variations of moderated tags to circumvent these restrictions. In fact, increasingly complex lexical variants have emerged over time. Communities that use lexical variants show increased participation and support of pro-ED (15-30%). Finally, the tags associated with content on these variants express more toxic, self-harm, and vulnerable content. Despite Instagram's moderation strategies, pro-ED communities are active and thriving. We discuss the effectiveness of content moderation as an intervention for communities of deviant behavior.

Within the CSCW community, little has been done to systematically analyze online eating disorder (ED) user generated content. In this paper, we present the results of a cross-platform content analysis of ED-related posts. We analyze the way that hashtags are used in ad-hoc ED- focused networks and present a comprehensive corpus of ED-terminology that frequently accompanies ED activities online. We provide exemplars of the types of ED-related content found online. Through this characterization of activities, we draw attention to the increasingly important role that these platforms play and how they are used and misappropriated for negative health purposes. We also outline specific challenges associated with researching these types of networks online. CAUTION: This paper includes media that could potentially be a trigger to those dealing with an eating disorder or with other self-injury illnesses. Please use caution when reading, printing, or disseminating this paper.

Many of the most prominent and unanswered ethical questions within HCI and social computing involve our ethical obligation to the communities that we study. Some of these questions fall under the purview of more traditional human subjects research ethics, but others hinge on when, for example, studies of public data trigger similar obligations. Basic rules to "do no harm" are complicated in digital communities by issues of consent and privacy, and ethics review boards are struggling to keep up even as research communities are similarly struggling to form appropriate norms. The goals of this workshop are to continue seeding conversations about research ethics within the SIGCHI community, to work towards norm setting, and in the meantime, to collectively help community members make good ethical decisions about research into sociotechnical systems and digital communities.

Harassment in online spaces is increasingly part of public debate and concern. Pervasive problems like cyberbullying, hate speech, and the glorification of self-harm have highlighted the breadth and depth of harassment taking place online. In this study we conduct a content analysis of the governing policies for fifteen social media platforms as they relate to harassment (of oneself and/or of community members) and other associated behaviors. We find that there is a striking inconsistency in how platform-specific policies depict harassment. Additionally, how these policies prescribe responses to harassment vary from mild censuring to the involvement of law enforcement. Finally, based on our analysis and findings, we discuss the potential for harnessing the power of the online communities to create norms around problematic behaviors.

In this paper, we present the results of a multi-year study of the social computing practices of 179 adolescents (Mage=12.4 years, SD=1.3; range: 10-14) living in a majority-minority lower-income urban neighborhood in the Southeast U.S. We investigate shifting social media practices using annual surveys and focus groups. We describe participants' social media use and motivations and show how that use has shifted over time. We show how participants identify social pressures and influences as well as specific behaviors including computer-mediated risky behaviors and self-harm. We discuss the implications of our findings for the CHI research community, including methodological challenges and the need for further study of computer-mediated harmful behaviors in youth populations. By demonstrating how large-scale trends are enacted on the ground, we describe participants' uses, motivations and behaviors as they deal with the increasing influence of technology in their social lives.

Personal health-tracking technologies have become a part of mainstream culture. Their growing popularity and widespread adoption present an opportunity for the design of new interventions to improve wellness and health. However, there is an increasing concern that these technologies are failing to inspire long-term adoption. In order to understand why users abandon personal health-tracking technologies, we analyzed advertisements of secondary sales of such technologies on Craigslist. We conducted iterative inductive and deductive analyses of approximately 1600 advertisements of personal health-tracking technologies posted over the course of one month across the US. We identify health motivations and rationales for abandonment and present a set of design implications. We call for improved theories that help translate between existing theories designed to explain psychological effects of health behavior change and the technologies that help people make those changes.

The Something Awful Forums (SAF) is an online community comprised of a loosely connected federation of forums, united in a distinctive brand of humor with a focus on the quality of member contributions. In this case study we find that the site has sustained success while deviating from common conventions and norms of online communities. Humor and the quality of content contributed by SAF members foster practices that seem counterintuitive to the development of a stable and thriving community. In this case study we show how design decisions are contextual and inter-dependent and together these heuristics create a different kind of online third place that challenges common practices.