Hiba Arnaout, Anmol Goel, H. Andrew Schwartz, Steffen T. Eberhardt, Dana Atzil-Slonim, Gavin Doherty, Brian Schwartz, Wolfgang Lutz, Tim Althoff, Munmun De Choudhury, Hamidreza Jamalabadi, Raj Sanjay Shah, Flor Miriam Plaza-del-Arco, Dirk Hovy, Maria Liakata, Iryna Gurevych. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Mohit Chandra, Siddharth Sriraman, Harneet Singh Khanuja, Yiqiao Jin, Munmun De Choudhury. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Arka Dutta, Sujan Dutta, Rijul Magu, Soumyajit Datta, Munmun De Choudhury, Ashiqur R. KhudaBukhsh. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Intimate partner violence (IPV) is defined as “abuse or aggression that occurs in a romantic relationship." IPV survivors face barriers when help-seeking, such as epistemic injustice – secondary victimization from dismissal and indifference when disclosing, misdirection, or inappropriate interventions. Survivors may leverage generative AI to make sensitive disclosures and access hermeneutic resources. However, these tools mediate outcomes for IPV survivors through novel manifestations of epistemic injustice. Using mixed-methods, we investigated hermeneutic resource provision by large-language models (LLMs). We evaluated LLM responses to IPV disclosures on three axes: hermeneutic resource provision, readability, and risk. Prompts were derived from a content analysis of IPV and generative AI discussions in 5 abuse subreddits. We contribute a taxonomy of 7 uses of generative AI in the experience of IPV, empirical illustration of epistemic inequity, and considerations for evaluating epistemic harm in generative AI. Content Warning: This study contains descriptions of abuse and violence.

Representation shapes public attitudes and behaviors. With the recent advances and rapid adoption of LLMs, the way these systems are introduced will negotiate societal expectations for their role in high-stakes domains like health. Yet it remains unclear whether current narratives present a balanced view. We analyzed five prominent discourse channels (news, research press, YouTube, TikTok, and Reddit) over a two-year period on lexical style, informational content, and symbolic representation. Discussions were generally positive and episodic, with positivity increasing over time. Risk communication was unthorough and often reduced to information quality incidents, while explanations of LLMs’ generative nature were rare. Compared with professional outlets, TikTok and Reddit highlighted wellbeing applications and showed greater variations in tone and anthropomorphism but little attention to risks. We discuss implications for public discourse as a diagnostic tool in identifying literacy and governance gaps, and for communication and design strategies to support more informed LLM engagement.

Understanding the prevalence of misinformation in health topics online can inform public health policies and interventions.However, measuring such misinformation at scale remains a challenge, particularly for high-stakes but understudied topics like opioid-use disorder (OUD)-a leading cause of death in the U.S. We present the first large-scale study of OUDrelated myths on YouTube, a widely-used platform for health information.With clinical experts, we validate 8 pervasive myths and release an expert-labeled video dataset.To scale labeling, we introduce MYTHTRIAGE, an efficient triage pipeline that uses a lightweight model for routine cases and defers harder ones to a high-performing, but costlier, large language model (LLM).MYTHTRIAGE achieves up to 0.86 macro F1-score while estimated to reduce annotation time and financial cost by over 76% compared to experts and full LLM labeling.We analyze 2.9K search results and 343K recommendations, uncovering how myths persist on YouTube and offering actionable insights for public health and platform moderation. 1 Warning: Some content of this paper, included to contextualize our data, is misleading.

Ensuring the online safety of youth has motivated research towards the development of machine learning (ML) methods capable of accurately detecting social media risks after-the-fact. However, for these detection models to be effective, they must proactively identify high-risk scenarios (e.g., sexual solicitations, cyberbullying) to mitigate harm. This `real-time' responsiveness is a recognized challenge within the risk detection literature. Therefore, this paper presents a novel two-level framework that first uses reinforcement learning to identify conversation stop points to prioritize messages for evaluation. Then, we optimize state-of-the-art deep learning models to accurately categorize risk priority (low, high). We apply this framework to a time-based simulation using a rich dataset of 23K private conversations with over 7 million messages donated by 194 youth (ages 13-21). We conducted an experiment comparing our new approach to a traditional conversation-level baseline. We found that the timeliness of conversations significantly improved from over 2 hours to approximately 16 minutes with only a slight reduction in accuracy (0.88 to 0.84). This study advances real-time detection approaches for social media data and provides a benchmark for future training reinforcement learning that prioritizes the timeliness of classifying high-risk conversations.

Suicide is a leading cause of death in the United States. Global safe reporting guidelines for news reports of suicide intend to mitigate associations of increased suicide incidence and stigma. However, recent research suggests more latent patterns in news beyond the guidelines could still contribute to suicide outcomes such as inhibited help-seeking and isolation. Using the Theory of Spiral of Silence to center isolation, we take a mixed-methods approach to analyze 22,021 articles (2020-2024) and use a zero-shot learning large language model (LLM) classifier to detect suppression of four structural circumstances of suicide: financial/job, legal, school, and access to physical/mental healthcare. We find that circumstance disclosure by news publishers diverges by political leaning, financial (p = 0.016), legal (p < 0.001), and school (p < 0.001); and by regionality, legal (p < 0.001) and health (p < 0.001). We qualify mechanisms of suppression using topic modeling and content sharing networks (CSNs). The spiral of silence lens highlights that left leaning publishers are more likely to disclose systemically or socially collective circumstances. In contrast, right leaning outlets suppress those and instead disclose instances that blame individuals for their experiences. Our work highlights how news reporting can downplay structural factors contributing to suicide. Content Warning: This paper discusses suicide deaths reported in news articles and may be sensitive to readers.

Opioid use disorder (OUD) is a leading health problem that affects individual well-being as well as general public health. Due to a variety of reasons, including the stigma faced by people using opioids, online communities for recovery and support were formed on different social media platforms. In these communities, people share their experiences and solicit information by asking questions to learn about opioid use and recovery. However, these communities do not always contain clinically verified information. In this paper, we study natural language questions asked in the context of OUD-related discourse on Reddit. We adopt transformer-based question detection along with hierarchical clustering across 19 subreddits to identify six coarse-grained categories and 69 fine-grained categories of OUD-related questions. Our analysis uncovers ten areas of information seeking from Reddit users in the context of OUD: drug sales, specific drug-related questions, OUD treatment, drug uses, side effects, withdrawal, lifestyle, drug testing, pain management and others, during the study period of 2018-2021. Our work provides a major step in improving the understanding of OUD-related questions people ask unobtrusively on Reddit. We finally discuss technological interventions and public health harm reduction techniques based on the topics of these questions.

Online communities on Reddit are a popular choice among people with opioid use disorder (OUD) to seek information on drug use, withdrawal symptoms, and recovery. LLM-powered chatbots (e.g., ChatGPT) are widely being adopted as question-answer systems for health-related queries. However, such online health information seeking could potentially be hindered by myths and misinformation on OUD, misleading or causing genuine harm to people with OUD. In this work, we examine the prevalence of 5 OUD-related myths, on treatment models and patient characteristics, within human- (taken from Reddit) and LLM-generated responses to queries on OUD. We further explore the framing strategies used within responses (both human- and LLM-generated) promoting and countering the myths. We found that all 5 myths were more widespread within human-generated responses. In addition, myth-promoting responses adopted trustworthy and authoritative framings, compared to knowledge-imparting linguistic cues within those countering the myths. Our work offers recommendations to reduce online OUD misinformation.

Over one in five adults in the US lives with a mental illness. In the face of a shortage of mental health professionals and offline resources, online short-form video content has grown to serve as a crucial conduit for disseminating mental health help and resources. However, the ease of content creation and access also contributes to the spread of misinformation, posing risks to accurate diagnosis and treatment. Detecting and understanding engagement with such content is crucial to mitigating their harmful effects on public health. We perform the first quantitative study of the phenomenon using YouTube Shorts and Bitchute as the sites of study. We contribute MentalMisinfo, a novel labeled mental health misinformation (MHMisinfo) dataset of 739 videos (639 from Youtube and 100 from Bitchute) and 135372 comments in total, using an expert-driven annotation schema. We first found that few-shot in-context learning with large language models (LLMs) are effective in detecting MHMisinfo videos. Next, we discover distinct and potentially alarming linguistic patterns in how audiences engage with MHMisinfo videos through commentary on both video-sharing platforms. Across the two platforms, comments could exacerbate prevailing stigma with some groups showing heightened susceptibility to and alignment with MHMisinfo. We discuss technical and public health-driven adaptive solutions to tackling the "epidemic" of mental health misinformation online.

Given the limited understanding of how the COVID-19 pandemic impacted mental health on college campuses, this paper examines the evolution of the mental health of college students since the onset of the pandemic. We conducted a large-scale study on over 1.2M posts on 173 U.S. college subreddits over 17 months. In particular, we adopted a quasi-experimental approach to examine how the different stages of the pandemic (isolation period, normalization period, and vaccination period) impacted changes in social media discussions of college students. We measured the temporal shifts in the symptomatic mental health expressions and topics of discussion on college subreddits. We find that while the expressions of depression, anxiety, stress, and suicidal ideation significantly increased in the isolation period. Interestingly, these expressions gradually subsided in the normalization period, only to resurface in the vaccination period. We also find unique occurrences of discussion across social, academic, health, and COVID-19-induced topics. Our findings reveal that despite the fragility of college students' mental health in the face of crisis, college students show resilience with sufficient time. We discuss the implications of our work in terms of building tools for real-time comprehension of college students' mental health, and in designing timely and tailored mental health support for college students.

Mohit Chandra, Siddharth Sriraman, Gaurav Verma, Harneet Singh Khanuja, Jose Suarez Campayo, Zihang Li, Michael L. Birnbaum, Munmun De Choudhury. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Recent breakthroughs in large language models (LLMs) have generated both interest and concern about their potential adoption as information sources or communication tools across different domains. In public health, where stakes are high and impacts extend across diverse populations, adopting LLMs poses unique challenges that require thorough evaluation. However, structured approaches for assessing potential risks in public health remain under-explored. To address this gap, we conducted focus groups with public health professionals and individuals with lived experience to unpack their concerns, situated across three distinct and critical public health issues that demand high-quality information: infectious disease prevention (vaccines), chronic and wellbeing care (opioid use disorder), and community health and safety (intimate partner violence). We synthesize participants' perspectives into a risk taxonomy, identifying and contextualizing the potential harms LLMs may introduce when positioned alongside traditional health communication. This taxonomy highlights four dimensions of risk to individuals, human-centered care, information ecosystems, and technology accountability. For each dimension, we unpack specific risks and offer example reflection questions to help practitioners adopt a risk-reflexive approach. By summarizing distinctive LLM characteristics and linking them to identified risks, we discuss the need to revisit prior mental models of information behaviors and complement evaluations with external validity and domain expertise through lived experience and real-world practices. Together, this work contributes a shared vocabulary and reflection tool for people in both computing and public health to collaboratively reflect and assess risks in deciding when to employ LLM capabilities (or not) and how to mitigate harm.

In remote psychotherapy, challenges arising from remote client-therapist interactions can impact the therapeutic alliance and overall outcomes. HCI research has focused on leveraging sensing technology to bridge gaps in remote interactions. In this work, we investigate the values and risks of integrating sensing technology in remote psychotherapy, specifically to capture and interpret non-verbal cues, by conducting a speculative design study with both clients and therapists. Our findings reveal that sensing technology has the potential to facilitate self-reflection in therapy. The sharing of tracked non-verbal cues could also possibly foster mutual disclosure, supporting therapists' judgments and balancing power dynamics between clients and therapists. However, clients and therapists were concerned about the accuracy of sensing systems, potential privacy threats, and additional cognition burden. Our insights into system values imply how sensing technology could potentially balance power dynamics in client-therapist relationships as well as general interpersonal relationships. We also emphasize the increased considerations in sensing-technology-empowered communication for remote psychotherapy than in non-vulnerable settings.

The impact of culture on how people express distress in online support communities is increasingly a topic of interest within Computer Supported Cooperative Work (CSCW) and Human-Computer Interaction (HCI). In the United States, distinct cultures have emerged from each of the two dominant political parties, forming a primary lens by which people navigate online and offline worlds. We examine whether partisan culture may play a role in how U.S. Republican and Democrat users of online mental health support communities express distress. We present a large-scale observational study of 2,184,356 posts from 8,916 statistically matched Republican, Democrat, and unaffiliated online support community members. We utilize methods from causal inference to statistically match partisan users along covariates that correspond with demographic attributes and platform use, in order to create comparable cohorts for analysis. We then leverage methods from natural language processing to understand how partisan expressions of distress compare between these sets of closely matched opposing partisans, and between closely matched partisans and typical support community members. Our data spans January 2013 to December 2022, a period of both rising political polarization and mental health concerns. We find that partisan culture does play into expressions of distress, underscoring the importance of considering partisan cultural differences in the design of online support community platforms.

People experiencing severe distress increasingly use Large Language Model (LLM) chatbots as mental health support tools. Discussions on social media have described how engagements were lifesaving for some, but evidence suggests that general-purpose LLM chatbots also have notable risks that could endanger the welfare of users if not designed responsibly. In this study, we investigate the lived experiences of people who have used LLM chatbots for mental health support. We build on interviews with 21 individuals from globally diverse backgrounds to analyze how users create unique support roles for their chatbots, fill in gaps in everyday care, and navigate associated cultural limitations when seeking support from chatbots. We ground our analysis in psychotherapy literature around effective support, and introduce the concept of therapeutic alignment, or aligning AI with therapeutic values for mental health contexts. Our study offers recommendations for how designers can approach the ethical and effective use of LLM chatbots and other AI mental health support tools in mental health care.

During times of crisis, heightened anxiety and fear create fertile ground for hate speech and misinformation, as people are more likely to fall for and be influenced by it. This paper looks into the interwoven relationship between anti-Asian hatred and COVID-19 misinformation amid the pandemic. By analyzing 785,798 Asian hate tweets and surveying 308 diverse participants, this empirical study explores how hateful content portrays the Asian community, including its truthfulness and targets, as well as what makes such portrayals harmful. We observed a high prevalence of misinformative hate speech that was lengthier, less emotional, and expressed more motivational drives than general hate speech. Overall, we found that anti-Asian rhetoric was characterized by an antagonism and inferiority framing, with misinformative hate underscoring antagonism and general hate emphasizing calls for action. Among all entities being explicitly criticized, China and the Chinese were constantly named to assign blame, with misinformative hate more likely to finger-point than general hate. Our survey results indicated that hateful messages with misinformation, demographic targeting, or divisive references were perceived as significantly more damaging. Individuals who placed less importance on free speech, had personal encounters with hate speech, or believed in the natural origin of COVID-19 were more likely to perceive higher severity. Taken together, this work highlights the distinct compositions of hate within misinformative hate speech that influences perceived harmfulness and adds to the complexity of defining and moderating harmful content. We discuss the implications for designing more context- and culture-sensitive counter-strategies and building more adaptive and explainable moderation approaches.

Gaurav Verma, Rynaa Grover, Jiawei Zhou, Binny Mathew, Jordan Kraemer, Munmun Choudhury, Srijan Kumar. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Recent increases in self-harm and suicide rates among youth have coincided with prevalent social media use; therefore, making these sensitive topics of critical importance to the HCI research community. We analyzed 1,224 direct message conversations (DMs) from 151 young Instagram users (ages 13-21), who engaged in private conversations using self-harm and suicide-related language. We found that youth discussed their personal experiences, including imminent thoughts of suicide and/or self-harm, as well as their past attempts and recovery. They gossiped about others, including complaining about triggering content and coercive threats of self-harm and suicide but also tried to intervene when a friend was in danger. Most of the conversations involved suicide or self-harm language that did not indicate the intent to harm but instead used hyperbolical language or humor. Our results shed light on youth perceptions, norms, and experiences of self-harm and suicide to inform future efforts towards risk detection and prevention.

Psychedelic-assisted therapy has shown significant promise in alleviating treatment-resistant mental illness, prompting excitement among people with lived experience of mental illness. The emerging collective perception of psychedelics as tools for mental health has been dubbed the Pollan Effect. We investigate whether the Pollan Effect carries to online community discussions concerning psilocybin-containing mushrooms (PCMs). Through a matched computational analysis of 676,875 longform Reddit posts describing PCM use spanning a decade, we provide evidence of the Pollan Effect in terms of increased health discourse around PCMs following two inception points—release of a book and subsequent documentary on PCMs. We then introduce the notion of a Pollan shift, which we witness through increased collective sharing of emotional and social experiences following the two inception points. Our findings offer insights into how online discourse could be representative of emerging social movements around new psychiatric treatments, and the role of platforms in sensemaking and research.

While social media data is a valuable source for inferring human behavior, its in-practice utility hinges on extraneous factors. Notable is the “observer effect,” where awareness of being monitored can alter people’s social media use. We present a causal-inference study to examine this phenomenon on the longitudinal Facebook use of 300+ participants who voluntarily shared their data spanning an average of 82 months before and 5 months after study enrollment. We measured deviation from participants’ expected social media use through time series analyses. Individuals with high cognitive ability and low neuroticism decreased posting immediately after enrollment, and those with high openness increased posting. The sharing of self-focused content decreased, while diverse topics emerged. We situate the findings within theories of self-presentation and self-consciousness. We discuss the implications of correcting observer effect in social media data-driven measurements, and how this phenomenon shines light on the ethics of these measurements.

Algorithmic estimations of worker behavior are gaining popularity. Passive Sensing–enabled AI (PSAI) systems leverage behavioral traces from workers’ digital tools to infer their experience. Despite their conceptual promise, the practical designs of these systems elicit tensions that lead to workers resisting adoption. This paper teases apart the monolithic representation of PSAI by investigating system components that maximize value and mitigate concerns. We conducted an interactive online survey using the Experimental Vignette Method. Using Linear Mixed-effects Models we found that PSAI systems were more acceptable when sensing digital time use or physical activity, instead of visual modes. Inferences using language were only acceptable in work-restricted contexts. Compared to insights into performance, workers preferred insights into mental wellbeing. However, they resisted systems that automatically forwarded these insights to others. Our findings provide a template to reflect on existing systems and plan future implementations of PSAI to be more worker-centered.

Early detection and intervention for relapse is important in the treatment of schizophrenia spectrum disorders. Researchers have developed AI models to predict relapse from patient-contributed data like social media. However, these models face challenges, including misalignment with practice and ethical issues related to transparency, accountability, and potential harm. Furthermore, how patients who have recovered from schizophrenia view these AI models has been underexplored. To address this gap, we first conducted semi-structured interviews with 28 patients and reflexive thematic analysis, which revealed a disconnect between AI predictions and patient experience, and the importance of the social aspect of relapse detection. In response, we developed a prototype that used patients' Facebook data to predict relapse. Feedback from seven patients highlighted the potential for AI to foster collaboration between patients and their support systems, and to encourage self-reflection. Our work provides insights into human-AI interaction and suggests ways to empower people with schizophrenia.

Minority stress is the leading theoretical construct for understanding LGBTQ+ health disparities. As such, there is an urgent need to develop innovative policies and technologies to reduce minority stress. To spur technological innovation, we created the largest labeled datasets on minority stress using natural language from subreddits related to sexual and gender minority people. A team of mental health clinicians, LGBTQ+ health experts, and computer scientists developed two datasets: (1) the publicly available LGBTQ+ Minority Stress on Social Media (MiSSoM) dataset and (2) the advanced request-only version of the dataset, LGBTQ+ MiSSoM+. Both datasets have seven labels related to minority stress, including an overall composite label and six sublabels. LGBTQ+ MiSSoM (N = 27,709) includes both human- and machine-annotated la-bels and comes preprocessed with features (e.g., topic models, psycholinguistic attributes, sentiment, clinical keywords, word embeddings, n-grams, lexicons). LGBTQ+ MiSSoM+ includes all the characteristics of the open-access dataset, but also includes the original Reddit text and sentence-level labeling for a subset of posts (N = 5,772). Benchmark supervised machine learning analyses revealed that features of the LGBTQ+ MiSSoM datasets can predict overall minority stress quite well (F1 = 0.869). Benchmark performance metrics yielded in the prediction of the other labels, namely prejudiced events (F1 = 0.942), expected rejection (F1 = 0.964), internalized stigma (F1 = 0.952), identity concealment (F1 = 0.971), gender dysphoria (F1 = 0.947), and minority coping (F1 = 0.917), were excellent. Descriptive analyses, ethical considerations, limitations, and possible use cases are provided.

Online harassment negatively impacts mental health, with victims expressing increased concerns such as depression, anxiety, and even increased risk of suicide, especially among youth and young adults. Yet, research has mainly focused on building automated systems to detect harassment incidents based on publicly available social media trace data, overlooking the impact of these negative events on the victims, especially in private channels of communication. Looking to close this gap, we examine a large dataset of private message conversations from Instagram shared and annotated by youth aged 13-21. We apply trained classifiers from online mental health to analyze the impact of online harassment on indicators pertinent to mental health expressions. Through a robust causal inference design involving a difference-in-differences analysis, we show that harassment results in greater expression of mental health concerns in victims up to 14 days following the incidents, while controlling for time, seasonality, and topic of conversation. Our study provides new benchmarks to quantify how victims perceive online harassment in the immediate aftermath of when it occurs. We make social justice-centered design recommendations to support harassment victims in private networked spaces. We caution that some of the paper's content could be triggering to readers.

Discussions of Violence Against Women (VAW) in publicly accessible forums like online news media can influence the perceptions of people and organizations. Language reinforcing stigma around VAW can result in negative consequences such as unethical representation of survivors and trivialization of the act of violence. In this work, we study the presence of stigmatized framings in news media and how it differs based on media attributes like regionality, political leaning, veracity, and latent communities of news sources. We also investigate the interactions between VAW-based stigma and 14 issue-generic policies used to describe political communications. We found that articles from national, right-leaning, and conspiratorial news sources contain more stigma compared to their counterparts. Furthermore, alignment of articles to the issue-generic policies offers the highest explanation for the presence of stigma in news articles. We discuss implications for institutions to improve safe reporting guidelines on VAW.

We conducted a study with 173 adolescents (ages 13-21), who self-reported their offline and online risk experiences and uploaded their Instagram data to our study website to flag private conversations as unsafe. Risk profiles were first created based on the survey data and then compared with the risk-flagged social media data. Five risk profiles emerged: Low Risks (51% of the participants), Medium Risks (29%), Increased Sexting (8%), Increased Self-Harm (8%), and High Risk Perpetration (4%). Overall, the profiles correlated well with the social media data with the highest level of risk occurring in the three smallest profiles. Youth who experienced increased sexting and self-harm frequently reported engaging in unsafe sexual conversations. Meanwhile, high risk perpetration was characterized by increased violence, threats, and sales/promotion of illegal activities. A key insight from our study was that offline risk behavior sometimes manifested differently in online contexts (i.e., offline self-harm as risky online sexual interactions). Our findings highlight the need for targeted risk prevention strategies for youth online safety.

We are extremely happy to be able to present the Computer-Supported Cooperative Work and Social Computing (CSCW) community with this issue of the Proceedings of the ACM on Human-Computer Interaction, containing very interesting and relevant scholarship from its members. This issue includes 209 papers, of which 175 were accepted from the Jan 2023 cycle, and 34 were accepted from the Jul 2023 cycle. It reflects great efforts and contributions from external reviewers, Associate Chairs and Editors, who together have conducted a rigorous review process to select contributions of the highest quality advancing the CSCW field. As Track Chairs, we are grateful for the community's collective efforts to continue shaping and sharing CSCW's tradition of high-quality scholarship across the years.

Gender-based violence (GBV) is an ongoing public health issue. Prevention practice and research on GBV contend with an incomplete understanding of its public health burden due to gender-exclusive definitions of GBV and inhibited survivor disclosure. Prior work in CSCW and HCI has explored sensitive disclosures and GBV conversations but excludes examination of conversation dynamics in surfacing knowledge of GBV. We used a mixed-methods approach to understand the phenomenon characterized by Muted Group Theory as a mechanism inhibiting disclosure in the context of GBV discussions on Reddit. Using an iterative process informed by literature of GBV research on cis-gender women, girls, trans, and non-binary populations, we developed comprehensive keywords to obtain, annotate, and analyze 298 posts and 10,369 comments about GBV across 7 subreddits. We found that muting faced by survivors offline, precluding reporting, is replicated on the Reddit platform. This study surfaced 5 categories of muting in discussions of GBV situated by variations in communication norms between dominant and non-dominant groups. These findings were supported by analysis of linguistic attributes that inform an Ensemble Classifier's detection of muting in conversations of GBV. The results offer that muting is a harmful occurrence in online disclosures of GBV that is mediated by existing moderation practices. This work contributes an expanded understanding of GBV conversations online, muting as a feature of those conversations, and an initial foray into detection to inform muting prevention.

Research involving sensitive data often leads to valuable human-centered insights. Yet, the effects of participating in and conducting research about sensitive data with youth are poorly understood. We conducted meta-level research to improve our understanding of these effects. We did the following: (i) asked youth (aged 13-21) to share their private Instagram Direct Messages (DMs) and flag their unsafe DMs; (ii) interviewed 30 participants about the experience of reflecting on this sensitive data; (iii) interviewed research assistants (RAs, n=12) about their experience analyzing youth's data. We found that reflecting about DMs brought discomfort for participants and RAs, although both benefited from increasing their awareness about online risks, their behavior, and privacy and social media practices. Participants had high expectations for safeguarding their private data while their concerns were mitigated by the potential to improve online safety. We provide implications for ethical research practices and the development of reflective practices among participants and RAs through applying trauma-informed principles to HCI research.

In the mental health domain, patient engagement is key to designing human-centered technologies. CSCW and HCI researchers have delved into various facets of collaboration in AI research; however, previous research neglects the individuals who both produce the data and will be most impacted by the resulting technologies, such as patients. This study examines how interdisciplinary researchers and mental health patients who donate their data for AI research collaborate and how we can improve human-centeredness in mental health AI research. We interviewed patient participants, AI researchers, and clinical researchers in a federally funded mental health AI research project. We used the concept of boundary objects to understand stakeholder collaboration. Our findings reveal that the social media data provided by patient participants functioned as boundary objects that facilitated stakeholder collaboration. Although the collaboration appeared to be successful, we argue that building consensus, or understanding each other's perspectives, can improve the human-centeredness of mental health AI research. Based on the findings, we provide suggestions for human-centered mental health AI research, working with data donors as domain experts, making invisible work visible, and privacy implications.

Mental health discussions on public forums influence the perceptions of people. Negative consequences may result from hostile and “othering” portrayals of people with mental disorders. Adopting the lens of Moral Foundation Theory (MFT), we study framings of mental health discourse on Twitter and News, and how moral underpinnings abate or exacerbate stigma. We adopted a large language model based representation framework to score 13,277,115 public tweets and 21,167 news articles against MFT’s five foundations. We found discussions on Twitter to demonstrate compassion, justice and equity-centered moral values for those suffering from mental illness, in contrast to those on News. That said, stigmatized discussions appeared on both Twitter and News, with news articles being more stigmatizing than tweets. We discuss implications for public health authorities to refine measures for safe reporting of mental health, and for social media platforms to design affordances that enable empathetic discourse.

We are witnessing an emergence in Passive Sensing enabled AI (PSAI) to provide dynamic insights for performance and wellbeing of information workers. Hybrid work paradigms have simultaneously created new opportunities for PSAI, but have also fostered anxieties of misuse and privacy intrusions within a power asymmetry. At this juncture, it is unclear if those who are sensed can find these systems acceptable. We conducted scenario-based interviews of 28 information workers to highlight their perspectives as data subjects in PSAI. We unpack their expectations using the Contextual Integrity framework of privacy and information gathering. Participants described appropriateness of PSAI based on its impact on job consequences, work-life boundaries, and preservation of flexibility. They perceived that PSAI inferences could be shared with selected stakeholders if they could negotiate the algorithmic inferences. Our findings help envision worker-centric approaches to implementing PSAI as an empowering tool in the future of work.

Large language models have abilities in creating high-volume human-like texts and can be used to generate persuasive misinformation. However, the risks remain under-explored. To address the gap, this work first examined characteristics of AI-generated misinformation (AI-misinfo) compared with human creations, and then evaluated the applicability of existing solutions. We compiled human-created COVID-19 misinformation and abstracted it into narrative prompts for a language model to output AI-misinfo. We found significant linguistic differences within human-AI pairs, and patterns of AI-misinfo in enhancing details, communicating uncertainties, drawing conclusions, and simulating personal tones. While existing models remained capable of classifying AI-misinfo, a significant performance drop compared to human-misinfo was observed. Results suggested that existing information assessment guidelines had questionable applicability, as AI-misinfo tended to meet criteria in evidence credibility, source transparency, and limitation acknowledgment. We discuss implications for practitioners, researchers, and journalists, as AI can create new challenges to the societal problem of misinformation.

We investigate how representations of Syrian refugees (2011-2021) differ across US partisan news outlets. We analyze 47,388 articles from the online US media about Syrian refugees to detail differences in reporting between left- and right-leaning media. We use various NLP techniques to understand these differences. Our polarization and question answering results indicated that left-leaning media tended to represent refugees as child victims, welcome in the US, and right-leaning media cast refugees as Islamic terrorists. We noted similar results with our sentiment and offensive speech scores over time, which detail possibly unfavorable representations of refugees in right-leaning media. A strength of our work is how the different techniques we have applied validate each other. Based on our results, we provide several recommendations. Stakeholders may utilize our findings to intervene around refugee representations, and design communications campaigns that improve the way society sees refugees and possibly aid refugee outcomes.

Instagram, one of the most popular social media platforms among youth, has recently come under scrutiny for potentially being harmful to the safety and well-being of our younger generations. Automated approaches for risk detection may be one way to help mitigate some of these risks if such algorithms are both accurate and contextual to the types of online harms youth face on social media platforms. However, the imminent switch by Instagram to end-to-end encryption for private conversations will limit the type of data that will be available to the platform to detect and mitigate such risks. In this paper, we investigate which indicators are most helpful in automatically detecting risk in Instagram private conversations, with an eye on high-level metadata, which will still be available in the scenario of end-to-end encryption. Toward this end, we collected Instagram data from 172 youth (ages 13-21) and asked them to identify private message conversations that made them feel uncomfortable or unsafe. Our participants risk-flagged 28,725 conversations that contained 4,181,970 direct messages, including textual posts and images. Based on this rich and multimodal dataset, we tested multiple feature sets (metadata, linguistic cues, and image features) and trained classifiers to detect risky conversations. Overall, we found that the metadata features (e.g., conversation length, a proxy for participant engagement) were the best predictors of risky conversations. However, for distinguishing between risk types, the different linguistic and media cues were the best predictors. Based on our findings, we provide design implications for AI risk detection systems in the presence of end-to-end encryption. More broadly, our work contributes to the literature on adolescent online safety by moving toward more robust solutions for risk detection that directly takes into account the lived risk experiences of youth.

We are again thrilled to be able to present the Computer-Supported Cooperative Work and Social Computing (CSCW) community with an issue of the Proceedings of the ACM on Human-Computer Interaction, containing very interesting and relevant scholarship from its members. This issue includes 138 papers, of which 82 were accepted from the July 2022 cycle, and 56 were accepted from the January 2023 cycle. It reflects great efforts and contributions from external reviewers, Associate Chairs and Editors, who together have conducted a rigorous review process to select contributions of the highest quality advancing the CSCW field. As Track Chairs, we are grateful for the community's collective efforts to continue shaping and sharing CSCW's tradition of high-quality scholarship across the years.

Explainable AI (XAI) systems are sociotechnical in nature; thus, they are subject to the sociotechnical gap-divide between the technical affordances and the social needs. However, charting this gap is challenging. In the context of XAI, we argue that charting the gap improves our problem understanding, which can reflexively provide actionable insights to improve explainability. Utilizing two case studies in distinct domains, we empirically derive a framework that facilitates systematic charting of the sociotechnical gap by connecting AI guidelines in the context of XAI and elucidating how to use them to address the gap. We apply the framework to a third case in a new domain, showcasing its affordances. Finally, we discuss conceptual implications of the framework, share practical considerations in its operationalization, and offer guidance on transferring it to new contexts. By making conceptual and practical contributions to understanding the sociotechnical gap in XAI, the framework expands the XAI design space.

Social support or peer support in mental health has successfully settled down in online spaces by reducing the potential risk of critical mental illness (e.g., suicidal thoughts) of support-seekers. While the prior work has mostly focused on support-seekers, particularly investigating their behavioral characteristics and the effects of online social supports to support-seekers, this paper seeks to understand online social support from supporters' perspectives, who have informational or emotional resources that may affect support-seekers either positively or negatively. To this end, we collect and analyze a large-scale of dataset consisting of the supporting comments and their target posts from 55 mental health communities in Reddit. We also develop a deep-learning-based model that scores informational and emotional support to the supporting comments. Based on the collected and scored dataset, we measure the characteristics of the supporters from the behavioral and content perspectives, which reveals that the supporters tend to give emotional support than informational support and the atmosphere of social support communities tend also to be emotional. We also understand the relations between the supporters and the support-seekers by giving a notion of "social supporting network'', whose nodes and edges are the sets of the users and the supporting comments. Our analysis on top users by out-degrees and in-degrees in social supporting network demonstrates that heavily-supportive users are more likely to give informational support with diverse content while the users who attract much support exhibit continuous support-seeking behaviors by uploading multiple posts with similar content. Lastly, we identified structural communities in social supporting network to explore whether and how the supporters and the support-seeking users are grouped. By conducting topic analysis on both the support-seeking posts and the supporting comments of individual communities, we revealed that small communities deal with a specific topic such as hair-pulling disorder. We believe that the methodologies, dataset, and findings can not only expose more research questions on online social supports in mental health, but also provide insight on improving social support in online platforms.

People experiencing mental illness are often forced into a system in which their chances of finding relief are largely determined by institutions that evaluate whether their distress deserves treatment. These governing institutions can be offline, such as the American healthcare system, and can also be online, such as online social platforms. As work in Human-Computer Interaction (HCI) and Computer Supported Cooperative Work (CSCW) frames technology-mediated support as one method to fill structural gaps in care, in this study, we ask the question: how do online and offline institutions influence how people in resource-scarce areas understand and express their distress online? We situate our work in U.S. Mental Health Professional Shortage Areas (MHPSAs), or areas in which there are too few mental health professionals to meet expected needs. We use an analysis of illness narratives to answer this question, conducting a large scale linguistic analysis of social media posts to understand broader trends in expressions of distress online. We then build on these analyses via in-depth interviews with 18 participants with lived experience of mental illness, analyzing the role of online and offline institutions in how participants express distress online. Through our findings, we argue that a consideration of institutions is crucial in designing effective technology-mediated support, and discuss the implications of considering institutions in mental health support for platform designers.

We collected Instagram data from 150 adolescents (ages 13-21) that included 15,547 private message conversations of which 326 conversations were flagged as sexually risky by participants. Based on this data, we leveraged a human-centered machine learning approach to create sexual risk detection classifiers for youth social media conversations. Our Convolutional Neural Network (CNN) and Random Forest models outperformed in identifying sexual risks at the conversation-level (AUC=0.88), and CNN outperformed at the message-level (AUC=0.85). We also trained classifiers to detect the severity risk level (i.e., safe, low, medium-high) of a given message with CNN outperforming other models (AUC=0.88). A feature analysis yielded deeper insights into patterns found within sexually safe versus unsafe conversations. We found that contextual features (e.g., age, gender, and relationship type) and Linguistic Inquiry and Word Count (LIWC) contributed the most for accurately detecting sexual conversations that made youth feel uncomfortable or unsafe. Our analysis provides insights into the important factors and contextual features that enhance automated detection of sexual risks within youths' private conversations. As such, we make valuable contributions to the computational risk detection and adolescent online safety literature through our human-centered approach of collecting and ground truth coding private social media conversations of youth for the purpose of risk classification.

Social media platforms are being utilized by individuals with mental illness for engaging in self-disclosure, finding support, or navigating treatment journeys. Individuals also increasingly bring their social media data to psychotherapy consultations. This emerging practice during psychotherapy can help us to better understand how patients appropriate social media technologies to develop and iterate patient narratives -- the stories of patients' own experiences that are vital in mental health treatment. In this paper, we seek to understand patients' perspectives regarding why and how they bring up their social media activities during psychotherapy consultations as well as related concerns. Through interviews with 18 mood disorder patients, we found that social media helps augment narratives around interpersonal conflicts, digital detox, and self-expression. We also found that discussion of social media activities shines a light on the power imbalance and privacy concerns regarding use of patient-generated health information. Based on the findings, we discuss that social media data are different from other types of patient-generated health data in terms of supporting patient narratives because of the social interactions and curation social media inherently engenders. We also discuss privacy concerns and trust between a patient and a therapist when patient narratives are supported by patients' social media data. Finally, we suggest design implications for social computing technologies that can foster patient narratives rooted in social media activities.

We collected Instagram Direct Messages (DMs) from 100 adolescents and young adults (ages 13-21) who then flagged their own conversations as safe or unsafe. We performed a mixed-method analysis of the media files shared privately in these conversations to gain human-centered insights into the risky interactions experienced by youth. Unsafe conversations ranged from unwanted sexual solicitations to mental health related concerns, and images shared in unsafe conversations tended to be of people and convey negative emotions, while those shared in regular conversations more often conveyed positive emotions and contained objects. Further, unsafe conversations were significantly shorter, suggesting that youth disengaged when they felt unsafe. Our work uncovers salient characteristics of safe and unsafe media shared in private conversations and provides the foundation to develop automated systems for online risk detection and mitigation.

Contact tracers assist in containing the spread of highly infectious diseases such as COVID-19 by engaging community members who receive a positive test result in order to identify close contacts. Many contact tracers rely on community member’s recall for those identifications, and face limitations such as unreliable memory. To investigate how technology can alleviate this challenge, we developed a visualization tool using de-identified location data sensed from campus WiFi and provided it to contact tracers during mock contact tracing calls. While the visualization allowed contact tracers to find and address inconsistencies due to gaps in community member’s memory, it also introduced inconsistencies such as false-positive and false-negative reports due to imperfect data, and information sharing hesitancy. We suggest design implications for technologies that can better highlight and inform contact tracers of potential areas of inconsistencies, and further present discussion on using imperfect data in decision making.

The field of digital mental health is making strides in the application of technology to broaden access to care. We critically examine how these technology-mediated forms of care might amplify historical injustices, and erase minoritized experiences and expressions of mental distress and illness. We draw on decolonial thought and critiques of identity-based algorithmic bias to analyze the underlying power relations impacting digital mental health technologies today, and envision new pathways towards a decolonial digital mental health. We argue that a decolonial digital mental health is one that centers lived experience over rigid classification, is conscious of structural factors that influence mental wellbeing, and is fundamentally designed to deter the creation of power differentials that prevent people from having agency over their care. Stemming from this vision, we make recommendations for how researchers and designers can support more equitable futures for people experiencing mental distress and illness.

When modeling passive data to infer individual mental wellbeing, a common source of ground truth is self-reports. But these tend to represent the psychological facet of mental states, which might not align with the physiological facet of that state. Our paper demonstrates that when what people “feel” differs from what people “say they feel”, we witness a semantic gap that limits predictions. We show that predicting mental wellbeing with passive data (offline sensors or online social media) is related to how the ground-truth is measured (objective arousal or self-report). Features with psycho-social signals (e.g., language) were better at predicting self-reported anxiety and stress. Conversely, features with behavioral signals (e.g., sleep), were better at predicting stressful arousal. Regardless of the source of ground truth, integrating both signals boosted prediction. To reduce the semantic gap, we provide recommendations to evaluate ground truth measures and adopt parsimonious sensing.

Social media data is being increasingly used to computationally learn about and infer the mental health states of individuals and populations. Despite being touted as a powerful means to shape interventions and impact mental health recovery, little do we understand about the theoretical, domain, and psychometric validity of this novel information source, or its underlying biases, when appropriated to augment conventionally gathered data, such as surveys and verbal self-reports. This talk presents a critical analytic perspective on the pitfalls of social media signals of mental health, especially when they are derived from “proxy” diagnostic indicators, often removed from the real-world context in which they are likely to be used. Then, to overcome these pitfalls, this talk presents results from two case studies, where machine learning algorithms to glean mental health insights from social media were developed in a context-sensitive and human-centered way, in collaboration with domain experts and stakeholders. The first of these case studies, a collaboration with a health provider, focuses on the individual-perspective, and reveals the ability and implications of using social media data of consented schizophrenia patients to forecast relapse and support clinical decision-making. Scaling up to populations, in collaboration with a federal organization and towards influencing public health policy, the second case study seeks to forecast nationwide rates of suicide fatalities using social media signals, in conjunction with health services data. The talk concludes with discussions of the path forward, emphasizing the need for a collaborative, multi-disciplinary research agenda while realizing the potential of social media data and machine learning in mental health – one that incorporates methodological rigor, ethics, and accountability, all at once.

Deviant eating behavior such as skipping meals and consuming unhealthy meals has a significant association with mental well-being in college students. However, there is more to what an individual eats. While eating patterns form a critical component of their mental well-being, insights and assessments related to the interplay of eating patterns and mental well-being remain under-explored in theory and practice. To bridge this gap, we use an existing real-time eating detection system that captures context during meals to examine how college students’ eating context associates with their mental well-being, particularly their affect, anxiety, depression, and stress. Our findings suggest that students’ irregularity or skipping meals negatively correlates with their mental well-being, whereas eating with family and friends positively correlates with improved mental well-being. We discuss the implications of our study in designing dietary intervention technologies and guiding student-centric well-being technologies.

Sexual exploration is a natural part of adolescent development; yet, unmediated internet access has enabled teens to engage in a wider variety of potentially riskier sexual interactions than previous generations, from normatively appropriate sexual interactions to sexually abusive situations. Teens have turned to online peer support platforms to disclose and seek support about these experiences. Therefore, we analyzed posts (N=45,955) made by adolescents (ages 13--17) on an online peer support platform to deeply examine their online sexual risk experiences. By applying a mixed methods approach, we 1) accurately (average of AUC = 0.90) identified posts that contained teen disclosures about online sexual risk experiences and classified the posts based on level of consent (i.e., consensual, non-consensual, sexual abuse) and relationship type (i.e., stranger, dating/friend, family) between the teen and the person in which they shared the sexual experience, 2) detected statistically significant differences in the proportions of posts based on these dimensions, and 3) further unpacked the nuance in how these online sexual risk experiences were typically characterized in the posts. Teens were significantly more likely to engage in consensual sexting with friends/dating partners; unwanted solicitations were more likely from strangers and sexual abuse was more likely when a family member was involved. We contribute to the HCI and CSCW literature around youth online sexual risk experiences by moving beyond the false dichotomy of "safe" versus "risky". Our work provides a deeper understanding of technology-mediated adolescent sexual behaviors from the perspectives of sexual well-being, risk detection, and the prevention of online sexual violence toward youth.

How do the reasons people post misinformation affect how they respond to fact checking interventions? In this research, we conducted a qualitative study of people who shared misinformation. We started with stories marked as false by a popular fact checker, Snopes, and identified people who posted those stories on Reddit. We interviewed the posters about the story they shared and their five behaviorally distinct personas: Reason to Disagree, Changed Belief, Steadfast Non-Standard Belief, Sharing to Debunk, and Sharing for Humor. Our findings suggest that research to craft better interventions to counter misinformation might benefit from tailoring to specific personas that can serve as design tools for on-going misinformation intervention research.

For people diagnosed with mental health conditions, psychiatric hospitalization is a major life transition, involving clinical treatment, crisis stabilization and loss of access of social networks and technology. The period after hospitalization involves not only management of the condition and clinical recovery but also re-establishing social connections and getting back to social and vocational roles for successful reintegration - a significant portion of which is mediated by social technology. However, little is known about how people get back to social lives after psychiatric hospitalization and the role social technology plays during the reintegration process. We address this gap through an interview study with 19 individuals who experienced psychiatric hospitalization in the recent past. Our findings shed light on how people's offline and online social lives are deeply intertwined with management of the mental health condition after hospitalization. We find that social technology supports reintegration journeys after hospitalization as well as presents certain obstacles. We discuss the role of social technology in significant life transitions such as reintegration and conclude with implications for social computing research, platform design and clinical care.

Veterans are a unique marginalized group facing multiple vulnerabilities. Current assessments of veteran needs and support largely come from first-person accounts guided by researchers' prompts. Social media platforms not only enable veterans to connect with each other, but also to self-disclose experiences and seek support. This paper addresses the gap in our understanding of veteran needs and their own support dynamics by examining self-initiated and ecologically-valid self-expressions. In particular, we adopt the Veteran Critical Theory (VCT) to conduct a computational study on the Reddit community of veterans. Using topic modeling, we find veteran-friendly gestures with good intentions might not be appreciated in the subreddit. By employing transfer learning methodologies, we find this community has more informational and emotional support behaviors than general online communities and a higher prevalence of informational support than emotional support. Lastly, an examination of support dynamics reveals some contrasts to previous scholarship in military culture and social media. We discover that positive language and author platform tenure have negative relations with posts receiving replies and replies getting votes, and that replies reflecting personal disclosures tend to get more votes. Through the lens of VCT, we discuss how online communities can help uncover veterans' needs and provide more effective social support.

Eating disorders (EDs) constitute a mental illness with the highest mortality. Today, mobile health apps provide promising means to ED patients for managing their condition. Apps enable users to monitor their eating habits, thoughts, and feelings, and offer analytic insights for behavior change. However, not only have scholars critiqued the clinical validity of these apps, their underlying design principles are not well understood. Through a review of 34 ED apps, we uncovered 11 different data types ED apps collect, and 9 strategies they employ to support collection and reflection. Drawing upon personal health informatics and visualization frameworks, we found that most apps did not adhere to best practices on what and how data should be collected from and reflected to users, or how data-driven insights should be communicated. Our review offers suggestions for improving the design of ED apps such that they can be useful and meaningful in ED recovery.

Individuals in distress adopt varied pathways in pursuit of care that aligns with their individual needs. Prior work has established that the first resource an individual leverages can influence later care and recovery, but less is understood about how the design of a point of care might interact with subsequent pathways to care. We investigate how the design of the Indian mental health helpline system interacts with complex sociocultural factors to marginalize caller needs. We draw on interviews with 18 helpline stakeholders, including individuals who have engaged with helplines in the past, shedding light on how they navigate both technological and structural barriers in pursuit of relief. Finally, we use a design justice framework rooted in Amartya Sen’s conceptualization of realization-focused justice to discuss implications and present recommendations towards the design of technology-mediated points of mental health support.

Social media platforms continue to evolve as archival platforms, where important milestones in an individual’s life are socially disclosed for support, solidarity, maintaining and gaining social capital, or to meet therapeutic needs. However, a limited understanding of how and what life events are disclosed (or not) prevents designing platforms to be sensitive to life events. We ask what life events individuals disclose on a 256 participants’ year-long Facebook dataset of 14K posts against their self-reported life events. We contribute a codebook to identify life event disclosures and build regression models on factors explaining life events’ disclosures. Positive and anticipated events are more likely, whereas significant, recent, and intimate events are less likely to be disclosed on social media. While all life events may not be disclosed, online disclosures can reflect complementary information to self-reports. Our work bears practical and platform design implications in providing support and sensitivity to life events.

Mai ElSherief, Caleb Ziems, David Muchlinski, Vaishnavi Anupindi, Jordyn Seybolt, Munmun De Choudhury, Diyi Yang. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.

involving resuming social roles and responsibilities, overcoming stigma and self-maintenance of the condition. Both clinical recovery and social reintegration need to go hand-in-hand for the overall well-being of individuals. However, research exploring social media for mental health has considered narrower, disjoint conceptualizations of people with mental illness - either as a patient or as a support-seeker. In this paper, we combine medical records with social media data of 254 consented individuals who have experienced a psychiatric hospitalization to address this gap. Adopting a theory-driven, Gaussian Mixture modeling approach, we provide a taxonomy of six heterogeneous behavioral patterns characterizing peoples' mental health status transitions around hospitalizations. Then we present an empirically derived framework, based on feedback from clinical researchers, to understand peoples' trajectories around clinical recovery and social reintegration. Finally, to demonstrate the utility of this taxonomy and the empirical framework, we assess social media signals that are indicative of individuals' reintegration trajectories post-hospitalization. We discuss the implications of combining peoples' clinical and social experiences in mental health care and the opportunities this intersection presents to post-discharge support and technology-based interventions for mental health.

Cyberbullying is a growing problem across social media platforms, inflicting short and long-lasting effects on victims. To mitigate this problem, research has looked into building automated systems, powered by machine learning, to detect cyberbullying incidents, or the involved actors like victims and perpetrators. In the past, systematic reviews have examined the approaches within this growing body of work, but with a focus on the computational aspects of the technical innovation, feature engineering, or performance optimization, without centering around the roles, beliefs, desires, or expectations of humans. In this paper, we present a human-centered systematic literature review of the past 10 years of research on automated cyberbullying detection. We analyzed 56 papers based on a three-prong human-centeredness algorithm design framework - spanning theoretical, participatory, and speculative design. We found that the past literature fell short of incorporating human-centeredness across multiple aspects, ranging from defining cyberbullying, establishing the ground truth in data annotation, evaluating the performance of the detection models, to speculating the usage and users of the models, including potential harms and negative consequences. Given the sensitivities of the cyberbullying experience and the deep ramifications cyberbullying incidents bear on the involved actors, we discuss takeaways on how incorporating human-centeredness in future research can aid with developing detection systems that are more practical, useful, and tuned to the diverse needs and contexts of the stakeholders.

In the era of big data and artificial intelligence, online risk detection has become a popular research topic. From detecting online harassment to the sexual predation of youth, the state-of-the-art in computational risk detection has the potential to protect particularly vulnerable populations from online victimization. Yet, this is a high-risk, high-reward endeavor that requires a systematic and human-centered approach to synthesize disparate bodies of research across different application domains, so that we can identify best practices, potential gaps, and set a strategic research agenda for leveraging these approaches in a way that betters society. Therefore, we conducted a comprehensive literature review to analyze 73 peer-reviewed articles on computational approaches utilizing text or meta-data/multimedia for online sexual risk detection. We identified sexual grooming (75%), sex trafficking (12%), and sexual harassment and/or abuse (12%) as the three types of sexual risk detection present in the extant literature. Furthermore, we found that the majority (93%) of this work has focused on identifying sexual predators after-the-fact, rather than taking more nuanced approaches to identify potential victims and problematic patterns that could be used to prevent victimization before it occurs. Many studies rely on public datasets (82%) and third-party annotators (33%) to establish ground truth and train their algorithms. Finally, the majority of this work (78%) mostly focused on algorithmic performance evaluation of their model and rarely (4%) evaluate these systems with real users. Thus, we urge computational risk detection researchers to integrate more human-centered approaches to both developing and evaluating sexual risk detection algorithms to ensure the broader societal impacts of this important work.

Effective ways to measure employee job satisfaction are fraught with problems of scale, misrepresentation, and timeliness. Current methodologies are limited in capturing subjective differences in expectations, needs, and values at work, and they do not lay emphasis on demographic differences, which may impact people's perceptions of job satisfaction. This study proposes an approach to assess job satisfaction by leveraging large-scale social media data. Starting with an initial Twitter dataset of 1.5M posts, we examine two facets of job satisfaction, pay and supervision. By adopting a theory-driven approach, we first build machine learning classifiers to assess perceived job satisfaction with an average AUC of 0.84. We then study demographic differences in perceived job satisfaction by geography, sex, and race in the U.S. For geography, we find that job satisfaction on Twitter exhibits insightful relationships with macroeconomic indicators such as financial wellbeing and unemployment rates. For sex and race, we find that females express greater pay satisfaction but lower supervision satisfaction than males, whereas Whites express the least pay and supervision satisfaction. Unpacking linguistic differences, we find contrasts in different groups' underlying priorities and concerns, e.g., under-represented groups saliently express about basic livelihood, whereas the majority groups saliently express about self-actualization. We discuss the role of frame of reference and the "job satisfaction paradox", conceptualized by organizational psychologists, in explaining our observed differences. We conclude with theoretical and sociotechnical implications of our work for understanding and improving worker wellbeing.

Technology bears important relationships to our health and wellness and has been utilized over the past two decades as an aid to support both self-management goals as well as collaboration among treatment teams.However, when chronic illnesses such as eating disorders (ED) are managed outside of institutionalized care settings, designing effective technology to support collaboration in treatment necessitates that we understand the relationships between patients, clinicians, and support networks. We conducted in-depth, semi-structured, interviews with 9 ED patients and 10 clinicians to understand the ED journey through the lens of collaborative efforts, technology use, and potential detriments. Based on our analysis of these 19 interviews, we present novel findings on various underlying disconnects within the collaborative ED treatment process disconnects among clinicians, between treatment foci, among preferences in tracking, within support networks, and inpatients' own identities. Our findings highlight how these various disconnects are concomitant with and gaps can stem from a lack of collaboration between different stakeholders in the ED journey. We also identify methods of facilitating collaboration in these disconnects through technological mediators.

Significant research in HCI and beyond has sought to understand end-user needs in formal and informal technology-mediated mental health support (TMMHS) systems. However, little work has been done to understand the experiences and needs of the individuals who power or support these systems, particularly in the Global South. We present a qualitative study of one of the most accessible forms of mental health care in India — helplines. Through in-depth interviews conducted with 12 helpline volunteers, we research the human infrastructure responsible for the functioning of helplines. We foreground the often invisible labor involved in erecting and maintaining the institutional, interpersonal, and individual boundaries that are critical to realizing the goals of these helplines. Finally, we discuss the implications of our research for future work examining human infrastructures, particularly in mental health settings, and for the design of future TMMHS systems that deliver on-demand care to diverse, underserved, and stigmatized populations.

Organizational culture (OC) encompasses the underlying beliefs, values, and practices that are unique to an organization. However, OC is inherently subjective and a coarse construct, and therefore challenging to quantify. Alternatively, self-initiated workplace reviews on online platforms like Glassdoor provide the opportunity to leverage the richness of language to understand OC. In as much, first, we use multiple job descriptors to operationalize OC as a word vector representation. We validate this construct with language used in 650k different Glassdoor reviews. Next, we propose a methodology to apply our construct on Glassdoor reviews to quantify the OC of employees by sector. We validate our measure of OC on a dataset of 341 employees by providing empirical evidence that it helps explain job performance. We discuss the implications of our work in guiding tailored interventions and designing tools for improving employee functioning.

McCarthy (1998) defined Artificial Intelligence (AI) as both “the science and engineering of in- telligent machines, especially computer programs” and the “computational part of the ability to achi...

Social media platforms offer a rich repository of crowdsourced information that has the potential to monitor human rights violations. The challenge is to quantify, interpret, and situate such unstructured data streams in the broader context, which remains under-investigated in existing CSCW research. Addressing these challenges demands computational solutions to extract large volumes of data in conjunction with human intervention to transition the data streams into the offline context to render them usable and actionable. Following an iterative human-in-the-loop computational approach, we explore whether citizen reports of abductions concentrated on Facebook groups can be useful to complete official records on the ongoing crisis of disappearances in Mexico. We conceptualize three key practices of the process of transitioning the data from online to offline, followed by seven qualitative characteristics of the data streams that contribute to each stage of the process. Our research contributes with an initial understanding of the challenges and opportunities of migrating the local knowledge from online communities to be used as evidence by organizations seeking to address institutional failures.

The ubiquitous use of social media enables researchers to obtain self-recorded longitudinal data of individuals in real-time. Because this data can be collected in an inexpensive and unobtrusive way at scale, social media has been adopted as a “passive sensor” to study human behavior. However, such research is impacted by the lack of homogeneity in the use of social media, and the engineering challenges in obtaining such data. This paper proposes a statistical framework to leverage the potential of social media in sensing studies of human behavior, while navigating the challenges associated with its sparsity. Our framework is situated in a large-scale in-situ study concerning the passive assessment of psychological constructs of 757 information workers wherein of four sensing streams was deployed - bluetooth beacons, wearable, smartphone, and social media. Our framework includes principled feature transformation and machine learning models that predict latent social media features from the other passive sensors. We demonstrate the efficacy of this imputation framework via a high correlation of 0.78 between actual and imputed social media features. With the imputed features we test and validate predictions on psychological constructs like personality traits and affect. We find that adding the social media data streams, in their imputed form, improves the prediction of these measures. We discuss how our framework can be valuable in multimodal sensing studies that aim to gather comprehensive signals about an individual's state or situation.

Opioid use disorder (OUD) poses substantial risks to personal well-being and public health. In online communities, users support those seeking recovery, in part by promoting clinically grounded treatments. However, some communities also promote clinically unverified OUD treatments, such as unregulated and untested drugs. Little research exists on which alternative treatments people use, whether these treatments are effective for recovery, or if they cause negative side effects. We provide the first large-scale social media study of clinically unverified, alternative treatments in OUD recovery on Reddit, partnering with an addiction research scientist. We adopt transfer learning across 63 subreddits to precisely identify posts related to opioid recovery. Then, we quantitatively discover potential alternative treatments and contextualize their effectiveness. Our work benefits health research and practice by identifying undiscovered recovery strategies. We also discuss the impacts to online communities dealing with stigmatized behavior and research ethics.

A growing body of research is combining social media data with machine learning to predict mental health states of individuals. An implication of this research lies in informing evidence-based diagnosis and treatment. However, obtaining clinically valid diagnostic information from sensitive patient populations is challenging. Consequently, researchers have operationalized characteristic online behaviors as "proxy diagnostic signals" for building these models. This paper posits a challenge in using these diagnostic signals, purported to support clinical decision-making. Focusing on three commonly used proxy diagnostic signals derived from social media, we find that predictive models built on these data, although offer strong internal validity, suffer from poor external validity when tested on mental health patients. A deeper dive reveals issues of population and sampling bias, as well as of uncertainty in construct validity inherent in these proxies. We discuss the methodological and clinical implications of these gaps and provide remedial guidelines for future research.

"Human-centered machine learning" (HCML) combines human insights and domain expertise with data-driven predictions to answer societal questions. This area's inherent interdisciplinarity causes tensions in the obligations researchers have to the humans whose data they use. This paper studies how scientific papers represent human research subjects in HCML. Using mental health status prediction on social media as a case study, we conduct thematic discourse analysis on 55 papers to examine these representations. We identify five discourses that weave a complex narrative of who the human subject is in this research: Disorder/Patient, Social Media, Scientific, Data/Machine Learning, and Person. We show how these five discourses create paradoxical subject and object representations of the human, which may inadvertently risk dehumanization. We also discuss the tensions and impacts of interdisciplinary research; the risks of this work to scientific rigor, online communities, and mental health; and guidelines for stronger HCML research in this nascent area.

LGBTQ+ (lesbian, gay, bisexual, transgender, queer) individuals are at significantly higher risk for mental health challenges than the general population. Social media and online communities provide avenues for LGBTQ+ individuals to have safe, candid, semi-anonymous discussions about their struggles and experiences. We study minority stress through the language of disclosures and self-experiences on the r/lgbt Reddit community. Drawing on Meyer's minority stress theory, and adopting a combined qualitative and computational approach, we make three primary contributions, 1) a theoretically grounded codebook to identify minority stressors across three types of minority stress-prejudice events, perceived stigma, and internalized LGBTphobia, 2) a machine learning classifier to scalably identify social media posts describing minority stress experiences, that achieves an AUC of 0.80, and 3) a lexicon of linguistic markers, along with their contextualization in the minority stress theory. Our results bear implications to influence public health policy and contribute to improving knowledge relating to the mental health disparities of LGBTQ+ populations. We also discuss the potential of our approach to enable designing online tools sensitive to the needs of LGBTQ+ individuals.

Job roles serve as a boundary between an employee and an organization, and are often considered building blocks in understanding the behavior and functioning of organizational systems. However, a lack of clarity about one's role, that is, one's work responsibilities and degree of authority, can lead to absenteeism, turnover, dissatisfaction, stress, and lower workplace performance. This paper proposes a methodology to quantitatively estimate role ambiguity via unobtrusively gathered data from LinkedIn, shared voluntarily by a cohort of information workers spanning multiple organizations. After successfully validating this LinkedIn based measure of Role Ambiguity, or LibRA against a state-of-the-art gold standard, drawing upon theories in organizational psychology, we examine the efficacy and convergent validity of LibRA in explaining established relationships of role ambiguity with wellbeing and performance measures of individuals. We find that greater LibRA is associated with depleted wellbeing, such as increased heart rate, increased arousal, decreased sleep, and higher stress. In addition, greater LibRA is associated with lower job performance such as decreased organizational citizenship behavior and decreased individual task performance. We discuss how LibRA can help fill gaps in state-of-the-art assessments of role ambiguity, and the potential of this measure in building novel technology-mediated strategies to combat role ambiguity in organizations.

Organizations often strive to recruit and retain individuals who would be a "good fit" with their core values, beliefs and practices. Person-Organization (P-O congruence is known to explain employee satisfaction, commitment and absenteeism. This paper proposes a new measure of P-O fit by empirically investigating the similarity of routine within an organization. This measure of routine fit is motivated by the theory of entrainment, which refers to the synchrony of individual and community behaviors. We use unobtrusive bluetooth sensing to examine how the concurrence of latent activity patterns is related to job performance and wellbeing. Routine fit echoes traditional constructs of congruence as it is significantly related to higher task performance and lower workplace deviance. Additionally however, it is also related to greater stress and higher arousal. Prior work in organizational psychology have used single-occasion survey instruments to infer uni-dimensional models of fit. These methods are limited by subjective perceptions of employees. In contrast, we demonstrate a data-driven and multidimensional approach to study normative routines in an organization as a measure of P-O fit. We discuss the potential of our approach in designing technologies that understand the congruence of employee routines and positively impact employee functioning at the workplace.

Online health communities (OHCs) provide support across conditions; for weight loss, OHCs offer support to foster positive behavior change. However, weight loss behaviors can also be subverted on OHCs to promote disordered eating practices. Using comments as proxies for support, we use computational linguistic methods to juxtapose similarities and differences in two Reddit weight loss communities, r/proED and r/loseit. We employ language modeling and find that word use in both communities is largely similar. Then, by building a word embedding model, specifically a deep neural network on comment words, we contrast the context of word use and find differences that imply different behavior change goals in these OHCs. Finally, these content and context norms predict whether a comment comes from r/proED or r/loseit. We show that norms matter in understanding how different OHCs provision support to promote behavior change and discuss the implications for design and moderation of OHCs.

Many online communities cater to the critical and unmet needs of individuals challenged with mental illnesses. Generally, communities engender characteristic linguistic practices, known as norms. Conformance to these norms, or linguistic accommodation, encourages social approval and acceptance. This paper investigates whether linguistic accommodation impacts a specific social feedback: the support received by an individual in an online mental health community. We first quantitatively derive two measures for each post in these communities: 1) the linguistic accommodation it exhibits, and 2) the level of support it receives. Thereafter, we build a statistical framework to examine the relationship between these measures. Although the extent to which accommodation is associated with support varies, we find a positive link between the two, consistent across 55 Reddit communities serving various psychological needs. We discuss how our work surfaces a tension in the functioning of these sensitive communities, and present design implications for improving their support provisioning mechanisms.

Seeking and providing support is challenging. When people disclose sensitive information, audience responses can substantially impact the discloser's wellbeing. We use mixed methods to understandresponses toonline sexual abuse-related disclosures on Reddit. We characterize disclosure responses, then investigate relationships between post content, comment content, and anonymity. We illustrate what types of support sought and provided in posts and comments co-occur. We find that posts seeking support receive more comments, and comments from “throwaway” (i.e., anonymous) accounts are more likely on posts also from throwaway accounts. Anonymous commenting enables commenters to share intimate content such as reciprocal disclosures and supportive messages, and commenter anonymity is not associated with aggressive or unsupportive comments. We argue that anonymity is an essential factor in designing social technologies that facilitate support seekingandprovision in socially stigmatized contexts, and provide implications for social media site design. CAUTION: This article includes content about sexual abuse.

Distinctive linguistic practices help communities build solidarity and differentiate themselves from outsiders. In an online community, one such practice is variation in orthography, which includes spelling, punctuation, and capitalization. Using a dataset of over two million Instagram posts, we investigate orthographic variation in a community that shares pro-eating disorder (pro-ED) content. We find that not only does orthographic variation grow more frequent over time, it also becomes more profound or “deep,” with variants becoming increasingly distant from the original: as, for example, #anarexyia is more distant than #anarexia from the original spelling #anorexia. We find that the these changes are driven by newcomers, who adopt the most extreme linguistic practices as they enter the community. Moreover, this behavior correlates with engagement with the community: the newcomers that adopt deeper variant orthography tend to remain active for longer in the community, and posts with deeper variation receive more positive feedback in the form of “likes.” Previous work has linked community membership change with language change, and our work casts this connection in a new light, with newcomers driving an evolving practice rather than adapting to it. We also demonstrate the utility of orthographic variation as a new lens to study sociolinguistic change in online communities, particularly when the change results from an exogenous force such as a content ban.

Psychological distress in the form of depression, anxiety and other mental health challenges among college students is a growing health concern. Dearth of accurate, continuous, and multi-campus data on mental well-being presents significant challenges to intervention and mitigation efforts in college campuses. We examine the potential of social media as a new "barometer" for quantifying the mental well-being of college populations. Utilizing student-contributed data in Reddit communities of over 100 universities, we first build and evaluate a transfer learning based classification approach that can detect mental health expressions with 97% accuracy. Thereafter, we propose a robust campus-specific Mental Well-being Index: MWI. We find that MWI is able to reveal meaningful temporal patterns of mental well-being in campuses, and to assess how their expressions relate to university attributes like size, academic prestige, and student demographics. We discuss the implications of our work for improving counselor efforts, and in the design of tools that can enable better assessment of the mental health climate of college campuses.

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.

Content shared on social media platforms has been identified to be valuable in gaining insights into people's mental health experiences. Although there has been widespread adoption of photo-sharing platforms such as Instagram in recent years, the role of visual imagery as a mechanism of self-disclosure is less understood. We study the nature of visual attributes manifested in images relating to mental health disclosures on Instagram. Employing computer vision techniques on a corpus of thousands of posts, we extract and examine three visual attributes: visual features (e.g., color), themes, and emotions in images. Our findings indicate the use of imagery for unique self-disclosure needs, quantitatively and qualitatively distinct from those shared via the textual modality: expressions of emotional distress, calls for help, and explicit display of vulnerability. We discuss the relationship of our findings to literature in visual sociology, in mental health self disclosure, and implications for the design of health interventions.

The growing amount of data collected by quantified self tools and social media hold great potential for applications in personalized medicine. Whereas the first includes health-related physiological signals, the latter provides insights into a user's behavior. However, the two sources of data have largely been studied in isolation. We analyze public data from users who have chosen to connect their MyFitnessPal and Twitter accounts. We show that a user's diet compliance success, measured via their self-logged food diaries, can be predicted using features derived from social media: linguistic, activity, and social capital. We find that users with more positive affect and a larger social network are more successful in succeeding in their dietary goals. Using a Granger causality methodology, we also show that social media can help predict daily changes in diet compliance success or failure with an accuracy of 77%, that improves over baseline techniques by 17%. We discuss the implications of our work in the design of improved health interventions for behavior change.

Cultural and gender norms shape how mental illness and therapy are perceived. However, there is a paucity of adequate empirical evidence around gender and cultural dimensions of mental illness. In this paper we situate social media as a "lens" to examine these dimensions. We focus on a large dataset of individuals who self-disclose to have an underlying mental health concern on Twitter. Having identified genuine disclosures in this data via semi-supervised learning, we examine differences in their posts, as measured via linguistic attributes and topic models. Our findings reveal significant differences between the content shared by female and male users, and by users from two western and two majority world countries. Males express higher negativity and lower desire for social support, whereas majority world users demonstrate more inhibition in their expression. We discuss the implications of our work in providing insights into the relationship of gender and culture with mental health, and in the design of gender and culture-aware health interventions.

When social networking sites give users granular control over their privacy settings, the result is that some content across the site is public and some is not. How might this content--or characteristics of users who post publicly versus to a limited audience--be different? If these differences exist, research studies of public content could potentially be introducing systematic bias. Via Mechanical Turk, we asked 1,815 Facebook users to share recent posts. Using qualitative coding and quantitative measures, we characterize and categorize the nature of the content. Using machine learning techniques, we analyze patterns of choices for privacy settings. Contrary to expectations, we find that content type is not a significant predictor of privacy setting; however, some demographics such as gender and age are predictive. Additionally, with consent of participants, we provide a dataset of nearly 9,000 public and non-public Facebook posts.

Widespread and pervasive adoption of smartphones has led to instant sharing of photographs that capture events ranging from mundane to life-altering happenings. We propose to capture sentiment information of such social event images leveraging their visual content. Our method extracts an intermediate visual representation of social event images based on the visual attributes that occur in the images going beyond sentiment-specific attributes. We map the top predicted attributes to sentiments and extract the dominant emotion associated with a picture of a social event. Unlike recent approaches, our method generalizes to a variety of social events and even to unseen events, which are not available at training time. We demonstrate the effectiveness of our approach on a challenging social event image dataset and our method outperforms state-of-the-art approaches for classifying complex event images into sentiments.

Self-disclosure of stigmatized conditions is known to yield therapeutic benefits. Social media sites are emerging as promising platforms enabling disclosure around a variety of stigmatized concerns, including mental illness. What kind of behavioral changes precede and follow such disclosures? Do the therapeutic benefits of "opening up" manifest in these changes? In this paper, we address these questions by focusing on disclosures of schizophrenia diagnoses made on Twitter. We adopt a clinically grounded quantitative approach to first identify temporal phases around disclosure during which symptoms of schizophrenia are likely to be significant. Then, to quantify behaviors before and after disclosures, we define linguistic measures drawing from literature on psycholinguistics and the socio-cognitive model of schizophrenia. Along with significant linguistic differences before and after disclosures, we find indications of therapeutic outcomes following disclosures, including improved readability and coherence in language, future orientation, lower self preoccupation, and reduced discussion of symptoms and stigma perceptions. We discuss the implications of social media as a new therapeutic tool in supporting disclosures of stigmatized conditions.

Stress constitutes a persistent wellbeing challenge to college students, impacting their personal, social, and academic life. However, violent events on campuses may aggravate student stress, due to the induced fear and trauma. In this paper, leveraging social media as a passive sensor of stress, we propose novel computational techniques to quantify and examine stress responses after gun violence on college campuses. We first present a machine learning classifier for inferring stress expression in Reddit posts, which achieves an accuracy of 82%. Next, focusing on 12 incidents of campus gun violence in the past five years, and social media data gathered from college Reddit communities, our methods reveal amplified stress levels following the violent incidents, which deviate from usual stress patterns on the campuses. Further, distinctive temporal and linguistic changes characterize the campus populations, such as reduced cognition, higher self pre-occupation and death-related conversations. We discuss the implications of our work in improving mental wellbeing and rehabilitation efforts around crisis events in college student populations.

Support seeking in stigmatized contexts is useful when the discloser receives the desired response, but it also entails social risks. Thus, people do not always disclose or seek support when they need it. One such stigmatized context for support seeking is sexual abuse. In this paper, we use mixed methods to understand abuse-related posts on reddit. First, we take a qualitative approach to understand post content. Then we use quantitative methods to investigate the use of "throwaway" accounts, which provide greater anonymity, and report on factors associated with support seeking and first-time disclosures. In addition to significant linguistic differences between throwaway and identified accounts, we find that those using throwaway accounts are significantly more likely to engage in seeking support. We also find that men are significantly more likely to use throwaway accounts when posting about sexual abuse. Results suggest that subreddit moderators and members who wish to provide support pay attention to throwaway accounts, and we discuss the importance of context-specific anonymity in support seeking.

Social media sites like Facebook and Instagram remove content that is against community guidelines or is perceived to be deviant behavior. Users also delete their own content that they feel is not appropriate within personal or community norms. In this paper, we examine characteristics of over 30,000 pro-eating disorder (pro-ED) posts that were at one point public on Instagram but have since been removed. Our work shows that straightforward signals can be found in deleted content that distinguish them from other posts, and that the implications of such classification are immense. We build a classifier that compares public pro-ED posts with this removed content that achieves moderate accuracy of 69%. We also analyze the characteristics in content in each of these post categories and find that removed content reflects more dangerous actions, self-harm tendencies, and vulnerability than posts that remain public. Our work provides early insights into content removal in a sensitive community and addresses the future research implications of the findings.

Online communities can promote illness recovery and improve well-being in the cases of many kinds of illnesses. However, for challenging mental health condition like anorexia, social media harbor both recovery communities as well as those that encourage dangerous behaviors. The effectiveness of such platforms in promoting recovery despite housing both communities is underexplored. Our work begins to fill this gap by developing a statistical framework using survival analysis and situating our results within the cognitive behavioral theory of anorexia. This model identifies content and participation measures that predict the likelihood of recovery. From our dataset of over 68M posts and 10K users that self-identify with anorexia, we find that recovery on Tumblr is protracted - only half of the population is estimated to exhibit signs of recovery after four years. We discuss the effectiveness of social media in improving well-being around anorexia, a unique health challenge, and emergent questions from this line of work.

History of mental illness is a major factor behind suicide risk and ideation. However research efforts toward characterizing and forecasting this risk is limited due to the paucity of information regarding suicide ideation, exacerbated by the stigma of mental illness. This paper fills gaps in the literature by developing a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation. We utilize semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts. We develop language and interactional measures for this purpose, as well as a propensity score matching based statistical approach. Our approach allows us to derive distinct markers of shifts to suicidal ideation. These markers can be modeled in a prediction framework to identify individuals likely to engage in suicidal ideation in the future. We discuss societal and ethical implications of this research.

Social media platforms have emerged as rich repositories of information relating to our activities, emotion and linguistic expression. In this talk I will discuss how this data may be harnessed to reason about our well-being. First, I will discuss a series of projects that examine how cues derived from social media around life-altering events (e.g., giving birth to a child, experience of abuse) and societal disruptions (e.g., urban violence, socio-political unrest) may help infer risks to mental health challenges. Second, I will discuss the emergent role of social media in providing a platform of self-disclosure and support to distressed, vulnerable, and under-privileged communities. Broadly, I will reflect on how this new line of research bears potential in informing the design of timely and tailored interventions, provisions for improved personal and societal well-being assessment, privacy and ethical considerations, and in this context, the challenges and opportunities of the increasing ubiquity of social media.

Social media sites have struggled with the presence of emotional and physical self-injury content. Individuals who share such content are often challenged with severe mental illnesses like eating disorders. We present the first study quantifying levels of mental illness severity (MIS) in social media. We examine a set of users on Instagram who post content on pro-eating disorder tags (26M posts from 100K users). Our novel statistical methodology combines topic modeling and novice/clinician annotations to infer MIS in a user's content. Alarmingly, we find that proportion of users whose content expresses high MIS have been on the rise since 2012 (13%/year increase). Previous MIS in a user's content over seven months can predict future risk with 81% accuracy. Our model can also forecast MIS levels up to eight months in the future with performance better than baseline. We discuss the health outcomes and design implications as well as ethical considerations of this line of research.

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.

Social media has emerged as a promising source of data for public health. This paper examines how these platforms can provide empirical quantitative evidence for understanding dietary choices and nutritional challenges in “food deserts” -- Census tracts characterized by poor access to healthy and affordable food. We present a study of 3 million food related posts shared on Instagram, and observe that content from food deserts indicate consumption of food high in fat, cholesterol and sugar; a rate higher by 5-17% compared to non-food desert areas. Further, a topic model analysis reveals the ingestion language of food deserts to bear distinct attributes. Finally, we investigate to what extent Instagram ingestion language is able to infer whether a tract is a food desert. We find that a predictive model that uses ingestion topics, socio-economic and food deprivation status attributes yields high accuracy (>80%) and improves over baseline methods by 6-14%. We discuss the role of social media in helping address inequalities in food access and health.

The availability of big data [James et al. 2011; Steve 2012] and the emergence of wearable computing [Thad 1996; Alex 2000], network science [Barabasi 2002], and computational social science [Hanna 2016; Watts and Strogatz 1998] as areas of inquiry has been revolutionizing the landscape of how we decipher our lives, our social interactions, and our day-to-day activities.This well-connected world has promised novel requirements on transforming healthcare from reactive and hospital-centered, to preventive, proactive, evidence-based, person-centered, and focused on well-being rather than ailment recovery.A multitude of various types of data are involved in this broad context of healthcare, including the following:-Clinical data [Prather et al. 1997; Riccardo and Zupan 2008], mainly the patient records from clinical institutions, such as medical imaging, patient electronic health records, clinical trial data, etc. -Genotype data [Eibe et al. 2004; Leslie et al. 1999], basically the genetic makeups of the individuals, such as DNA, protein, etc. -Social media data [Reza et al. 2014; Sitaram and Huberman 2010], which is the information the individuals posted on online social platforms such as Facebook, Twitter, PatientsLikeMe, etc. -Environmental sensory data [Ruchi and Bhatia 2010], which is the information sampled from the surrounding environment where the individuals are living in, such as air pollution and humidity information.-Behavioral and sentiment data [Bo and Lee 2008], which could be the data recorded by the wearable devices on patient's activities.-Mobile data [Miller and Han 2009; Fosca and Pedreschi 2008], which is sampled from individuals' mobile devices.Integrating multiple types of information to make people healthier is also a problem of vital importance that requires collective effort from different parties, where data mining plays a pivotal role.Toward this aim, the National Science Foundation of United

Social media platforms have emerged as prominent information sharing ecosystems in the context of a variety of recent crises, ranging from mass emergencies, to wars and political conflicts. We study affective responses in social media and how they might indicate desensitization to violence experienced in communities embroiled in an armed conflict. Specifically, we examine three established affect measures: negative affect, activation, and dominance as observed on Twitter in relation to a number of statistics on protracted violence in four major cities afflicted by the Mexican Drug War. During a two year period (Aug 2010 - Dec 2012), while violence was on the rise in these regions, our findings show a decline in negative emotional expression as well as a rise in emotional arousal and dominance in Twitter posts: aspects known to be psychological markers of desensitization. We discuss the implications of our work for behavioral health, facilitating rehabilitation efforts in communities enmeshed in an acute and persistent urban warfare, and the impact on civic engagement.

Search engines and social media are two of the most com-monly used online services; in this paper, we examine how users appropriate these platforms for online health activi-ties via both large-scale log analysis and a survey of 210 people. While users often turn to search engines to learn about serious or highly stigmatic conditions, a surprising amount of sensitive health information is also sought and shared via social media, in our case the public social plat-form Twitter. We contrast what health content people seek via search engines vs. share on social media, as well as why they choose a particular platform for online health activi-ties. We reflect on the implications of our results for design-ing search engines, social media, and social search tools that better support people's health information seeking and sharing needs.

The birth of a child is a major milestone in the life of parents. We leverage Facebook data shared voluntarily by 165 new mothers as streams of evidence for characterizing their postnatal experiences. We consider multiple measures including activity, social capital, emotion, and linguistic style in participants' Facebook data in pre- and postnatal periods. Our study includes detecting and predicting onset of post-partum depression (PPD). The work complements recent work on detecting and predicting significant postpartum changes in behavior, language, and affect from Twitter data. In contrast to prior studies, we gain access to ground truth on postpartum experiences via self-reports and a common psychometric instrument used to evaluate PPD. We develop a series of statistical models to predict, from data available before childbirth, a mother's likelihood of PPD. We corroborate our quantitative findings through interviews with mothers experiencing PPD. We find that increased social isolation and lowered availability of social capital on Facebook, are the best predictors of PPD in mothers.

This paper focuses on the fifth year of a cross-sectional trend study of enterprise social networking. Several stable patterns are evident--some activities have plateaued, others steadily increase in frequency. The fifth year did see a new development: As social networking companies visibly embraced behavior tracking and targeted advertising, concerns shifted from boundary regulation within personal networks to unsettling evidence of activity monitoring. However, benefits of use continue to outweigh drawbacks.

We consider social media as a promising tool for public health, focusing on the use of Twitter posts to build predictive models about the forthcoming influence of childbirth on the behavior and mood of new mothers. Using Twitter posts, we quantify postpartum changes in 376 mothers along dimensions of social engagement, emotion, social network, and linguistic style. We then construct statistical models from a training set of observations of these measures before and after the reported childbirth, to forecast significant postpartum changes in mothers. The predictive models can classify mothers who will change significantly following childbirth with an accuracy of 71%, using observations about their prenatal behavior, and as accurately as 80-83% when additionally leveraging the initial 2-3 weeks of postnatal data. The study is motivated by the opportunity to use social media to identify mothers at risk of postpartum depression, an underreported health concern among large populations, and to inform the design of low-cost, privacy-sensitive early-warning systems and intervention programs aimed at promoting wellness postpartum.

We investigate the landscape of affective expression of employees at a large Fortune 500 software corporation via an internal microblogging tool. We present three analyses of emotional expression among employees, based on literature in organizational behavior: its relationship to (1) exogenous/lifestyle and endogenous workplace factors, (2) geography and (3) organizational structure. We find that employees tend to make significant accommodations in affect expression when interacting with others over the organizational hierarchy. We also find that positive affect is expressed through interpersonal communications that connect disparate geographic regions. Our findings have implications for enabling emotional reflection of employees and for management in that they can help uncover emotional patterns associated with episodes of high and low productivity, allowing organizations to improve employee engagement and promote positive attitudes.

We explore the harnessing of social media as a window on changes around major life events in individuals and larger populations. We specifically examine patterns of activity, emotional, and linguistic correlates for childbirth and postnatal course. After identifying childbirth events on Twitter, we analyze daily posting patterns and language usage before and after birth by new mothers, and make inferences about the status and dynamics of changes in emotions expressed following childbirth. We find that childbirth is associated with some changes for most new mothers, but approximately 15% of new mothers show significant changes in their online activity and emotional expression postpartum. We observe that these mothers can be distinguished by linguistic changes captured by shifts in a relatively small number of words in their social media posts. We introduce a greedy differencing procedure to identify the type of language that characterizes significant changes in these mothers during postpartum. We conclude with a discussion about how such characterizations might be applied to recognizing and understanding health and well-being in women following childbirth.

In this paper we examine the information sharing practices of people living in cities amid armed conflict. We describe the volume and frequency of microblogging activity on Twitter from four cities afflicted by the Mexican Drug War, showing how citizens use social media to alert one another and to comment on the violence that plagues their communities. We then investigate the emergence of civic media "curators," individuals who act as "war correspondents" by aggregating and disseminating information to large numbers of people on social media. We conclude by outlining the implications of our observations for the design of civic media systems in wartime.

Social media is already a fixture for reporting for many journalists, especially around breaking news events where non-professionals may already be on the scene to share an eyewitness report, photo, or video of the event. At the same time, the huge amount of content posted in conjunction with such events serves as a challenge to finding interesting and trustworthy sources in the din of the stream. In this paper we develop and investigate new methods for filtering and assessing the verity of sources found through social media by journalists. We take a human centered design approach to developing a system, SRSR ("Seriously Rapid Source Review"), informed by journalistic practices and knowledge of information production in events. We then used the system, together with a realistic reporting scenario, to evaluate the filtering and visual cue features that we developed. Our evaluation offers insights into social media information sourcing practices and challenges, and highlights the role technology can play in the solution.

Social media platforms such as Twitter garner significant attention from very large audiences in response to real-world events. Automatically establishing who is participating in information production or conversation around events can improve event content consumption, help expose the stakeholders in the event and their varied interests, and even help steer subsequent coverage of an event by journalists. In this paper, we take initial steps towards building an automatic classifier for user types on Twitter, focusing on three core user categories that are reflective of the information production and consumption processes around events: organizations, journalists/media bloggers, and ordinary individuals. Exploration of the user categories on a range of events shows distinctive characteristics in terms of the proportion of each user type, as well as differences in the nature of content each shared around the events.

This paper reviews the state of the art and some emerging issues in research areas related to pattern analysis and monitoring of web-based social communities. This research area is important for several reasons. First, the presence of near-ubiquitous low-cost computing and communication technologies has enabled people to access and share information at an unprecedented scale. The scale of the data necessitates new research for making sense of such content. Furthermore, popular websites with sophisticated media sharing and notification features allow users to stay in touch with friends and loved ones; these sites also help to form explicit and implicit social groups. These social groups are an important source of information to organize and to manage multimedia data. In this article, we study how media-rich social networks provide additional insight into familiar multimedia research problems, including tagging and video ranking. In particular, we advance the idea that the contextual and social aspects of media are as important for successful multimedia applications as is the media content. We examine the inter-relationship between content and social context through the prism of three key questions. First, how do we extract the context in which social interactions occur? Second, does social interaction provide value to the media object? Finally, how do social media facilitate the repurposing of shared content and engender cultural memes? We present three case studies to examine these questions in detail. In the first case study, we show how to discover structure latent in the social media data, and use the discovered structure to organize Flickr photo streams. In the second case study, we discuss how to determine the interestingness of conversations—and of participants—around videos uploaded to YouTube. Finally, we show how the analysis of visual content, in particular tracing of content remixes, can help us understand the relationship among YouTube participants. For each case, we present an overview of recent work and review the state of the art. We also discuss two emerging issues related to the analysis of social networks—robust data sampling and scalable data analysis.

In this article, we present a novel algorithm to discover multirelational structures from social media streams. A media item such as a photograph exists as part of a meaningful interrelationship among several attributes, including time, visual content, users, and actions. Discovery of such relational structures enables us to understand the semantics of human activity and has applications in content organization, recommendation algorithms, and exploratory social network analysis. We are proposing a novel nonnegative matrix factorization framework to characterize relational structures of group photo streams. The factorization incorporates image content features and contextual information. The idea is to consider a cluster as having similar relational patterns; each cluster consists of photos relating to similar content or context. Relations represent different aspects of the photo stream data, including visual content, associated tags, photo owners, and post times. The extracted structures minimize the mutual information of the predicted joint distribution. We also introduce a relational modularity function to determine the structure cost penalty, and hence determine the number of clusters. Extensive experiments on a large Flickr dataset suggest that our approach is able to extract meaningful relational patterns from group photo streams. We evaluate the utility of the discovered structures through a tag prediction task and through a user study. Our results show that our method based on relational structures, outperforms baseline methods, including feature and tag frequency based techniques, by 35%--420%. We have conducted a qualitative user study to evaluate the benefits of our framework in exploring group photo streams. The study indicates that users found the extracted clustering results clearly represent major themes in a group; the clustering results not only reflect how users describe the group data but often lead the users to discover the evolution of the group activity.

Vacation planning is a frequent laborious task which requires skilled interaction with a multitude of resources. This paper develops an end-to-end approach for constructing intra-city travel itineraries automatically by tapping a latent source reflecting geo-temporal breadcrumbs left by millions of tourists. In particular, the popular rich media sharing site, Flickr, allows photos to be stamped by the date and time of when they were taken, and be mapped to Points Of Interest (POIs) by latitude-longitude information as well as semantic metadata (e.g., tags) that describe them.

Researchers increasingly use electronic communication data to construct and study large social networks, effectively inferring unobserved ties (e.g. i is connected to j) from observed communication events (e.g. i emails j). Often overlooked, however, is the impact of tie definition on the corresponding network, and in turn the relevance of the inferred network to the research question of interest. Here we study the problem of network inference and relevance for two email data sets of different size and origin. In each case, we generate a family of networks parameterized by a threshold condition on the frequency of emails exchanged between pairs of individuals. After demonstrating that different choices of the threshold correspond to dramatically different network structures, we then formulate the relevance of these networks in terms of a series of prediction tasks that depend on various network features. In general, we find: a) that prediction accuracy is maximized over a non-trivial range of thresholds corresponding to 5-10 reciprocated emails per year; b) that for any prediction task, choosing the optimal value of the threshold yields a sizable (~30%) boost in accuracy over naive choices; and c) that the optimal threshold value appears to be (somewhat surprisingly) consistent across data sets and prediction tasks. We emphasize the practical utility in defining ties via their relevance to the prediction task(s) at hand and discuss implications of our empirical results.

This article analyzes communication within a set of individuals to extract the representative prototypical groups and provides a novel framework to establish the utility of such groups. Corporations may want to identify representative groups (which are indicative of the overall communication set) because it is easier to track the prototypical groups rather than the entire set. This can be useful for advertising, identifying “hot” spots of resource consumption as well as in mining representative moods or temperature of a community. Our framework has three parts: extraction, characterization, and utility of prototypical groups. First, we extract groups by developing features representing communication dynamics of the individuals. Second, to characterize the overall communication set, we identify a subset of groups within the community as the prototypical groups. Third, we justify the utility of these prototypical groups by using them as predictors of related external phenomena; specifically, stock market movement of technology companies and political polls of Presidential candidates in the 2008 U.S. elections. We have conducted extensive experiments on two popular blogs, Engadget and Huffington Post. We observe that the prototypical groups can predict stock market movement/political polls satisfactorily with mean error rate of 20.32%. Further, our method outperforms baseline methods based on alternative group extraction and prototypical group identification methods. We evaluate the quality of the extracted groups based on their conductance and coverage measures and develop metrics: predictivity and resilience to evaluate their ability to predict a related external time-series variable (stock market movement/political polls). This implies that communication dynamics of individuals are essential in extracting groups in a community, and the prototypical groups extracted by our method are meaningful in characterizing the overall communication sets.

In this paper we develop a recommendation framework to connect image content with communities in online social media. The problem is important because users are looking for useful feedback on their uploaded content, but finding the right community for feedback is challenging for the end user. Social media are characterized by both content and community. Hence, in our approach, we characterize images through three types of features: visual features, user generated text tags, and social interaction (user communication history in the form of comments). A recommendation framework based on learning a latent space representation of the groups is developed to recommend the most likely groups for a given image. The model was tested on a large corpus of Flickr images comprising 15,689 images. Our method outperforms the baseline method, with a mean precision 0.62 and mean recall 0.69. Importantly, we show that fusing image content, text tags with social interaction features outperforms the case of only using image content or tags.

Online social networking sites such as Flickr and Facebook provide a diverse range of functionalities that foster online communities to create and share media content. In particular, Flickr groups are increasingly used to aggregate and share photos about a wide array of topics or themes. Unlike photo repositories where images are typically organized with respect to static topics, the photo sharing process as in Flickr often results in complex time-evolving social and visual patterns. Characterizing such time-evolving patterns can enrich media exploring experience in a social media repository. In this paper, we propose a novel framework that characterizes distinct time-evolving patterns of group photo streams. We use a nonnegative joint matrix factorization approach to incorporate image content features and contextual information, including associated tags, photo owners and post times. In our framework, we consider a group as a mixture of themes - each theme exhibits similar patterns of image content and context. The theme extraction is to best explain the observed image content features and associations with tags, users and times. Extensive experiments on a Flickr dataset suggest that our approach is able to extract meaningful evolutionary patterns from group photo streams. We evaluate our method through a tag prediction task. Our prediction results outperform baseline methods, which indicate the utility of our theme based joint analysis.

Rich media social networks promote not only creation and consumption of media, but also communication about the posted media item. What causes a conversation to be interesting, that prompts a user to participate in the discussion on a posted video? We conjecture that people participate in conversations when they find the conversation theme interesting, see comments by people whom they are familiar with, or observe an engaging dialogue between two or more people (absorbing back and forth exchange of comments). Importantly, a conversation that is interesting must be consequential - i.e. it must impact the social network itself.

We have developed a computational framework to characterize social network dynamics in the blogosphere at individual, group and community levels. Such characterization could be used by corporations to help drive targeted advertising and to track the moods and sentiments of consumers. We tested our model on a widely read technology blog called Engadget. Our results show that communities transit between states of high and low entropy, depending on sentiments (positive / negative) about external happenings. We also propose an innovative method to establish the utility of the extracted knowledge, by correlating the mined knowledge with an external time series data (the stock market). Our validation results show that the characterized groups exhibit high stock market movement predictability (89%) and removal of 'impactful' groups makes the community less resilient by lowering predictability (26%) and affecting the composition of the groups in the rest of the community.

The paper develops a novel computational framework for predicting communication flow in social networks based on several contextual features. The problem is important because prediction of communication flow can impact timely sharing of specific information across a wide array of communities. We determine the intent to communicate and communication delay between users based on several contextual features in a social network corresponding to (a) neighborhood context, (b) topic context and (c) recipient context. The intent to communicate and communication delay are modeled as regression problems which are efficiently estimated using Support Vector Regression. We predict the intent and the delay, on an interval of time using past communication data. We have excellent prediction results on a real-world dataset from MySpace.com with an accuracy of 13-16%. We show that the intent to communicate is more significantly influenced by contextual factors compared to the delay.