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.

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.

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.

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.

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.