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.

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.

This note explores how various technology-mediated negative experiences and safety concerns of non-Western LGBTQ+ users, particularly from Bangladesh, hinder their continuing online interactions and self-presentation practices. Based on face-to-face and Skype semi-structured interviews (n=31), our initial results report that along with facing life-threatening harassing experiences online, Bangladeshi LGBTQ+ users also struggle with audience management and perceived privacy affordances that critically restrict their identity exploration and overall online participation, often forcing them to adopt fake/pseudo-identity online. These findings advocate for better design implications on safer social media participation, especially for LGBTQ+ users from non-Western contexts, and call for more attention to inclusive technologies.

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.

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.

Social interaction across multiple online platforms is a challenge for gender and sexual minorities (GSM) due to the stigmatization they face, which increases the complexity of their self-presentation decisions. These online interactions and identity disclosures can be more complicated for GSM in non-Western contexts due to consequentially different audiences and perceived affordances by the users, and limited baseline understanding of the conflation of these two with local norms and the opportunities they practically represent. Using focus group discussions and semi-structured interviews, we engaged with 61 Hijra individuals from Bangladesh, a severely stigmatized GSM from south Asia, to understand their overall online participation and disclosure behaviors through the lens of personal social media ecosystems. We find that along with platform audiences, affordances, and norms, participant skill/knowledge, and cultural influences also impact navigation through multiple platforms, resulting in differential benefits from privacy features. This impacts how Hijra perceive online spaces, and shape their self-presentation and disclosure behaviors over time. Content Warning: This paper discusses graphic contents (e.g. rape and sexual harassment) related to Hijra.

Prior research on anonymous social media (ASM) has studied the issue of sexual harassment and has revealed its connections to stereotyping, aggression, interpersonal relationships, and mental health among others [16, 24, 60]. However, the characteristics of such harassment in the context of low and middle-income countries (LMICs) in the global south has not received enough attention in the literature. This paper presents our findings on the use of ASM in Bangladesh based on an anonymous online survey of (n= 291) participants and semi-structured interviews with (n= 27) participants. Our study shows a wide prevalence of sexual harassment on anonymous social networks in Bangladesh, the relationship between a closely-knitted communal culture and anonymous harassment, and the lack of infrastructural support for the victims. We also propose a set of design and policy recommendations for such anonymous social media to extend the current ICTD literature on ensuring a safer online environment for women, especially in an LMIC.