Social media platforms enable diverse users to engage in everyday political talk with (un)known audiences.Platform features and affordances may shape political discussions and how audiences make sense of them, potentially shifting political attitudes.Using affirmative action (AA) -a controversial, identity-centric higher education policy -as a context for analysis, we investigate social media features' and affordances' role in AA discussions.Our qualitative content analysis of over 38,000 social media posts and comments across Reddit, Twitter/X, and TikTok demonstrates how features (e.g., Green Screen) and affordances (e.g., visibility) shape the presentation of external evidence and cues on social media that help users determine information veracity.We introduce sociotechnical rhetorical strategies to describe rhetorical devices enabled by platform features and affordances and consider how these strategies are used to express and refute racism online.Finally, we suggest ways that social media designers may leverage visibility, navigability, and association affordances to enhance users' ability to make sense of and safely experience AA discussions.

Peer Reviewed

Identity facets such as gender, sexuality, and race shape consent processes, including in dating. Increasingly, dating apps play important roles in consent exchange processes. Examining consent in gendered and racialized communities, as mediated by dating apps, is an overlooked yet important space for illuminating the interplay between identity, technology, and consent. We draw from a guided reflective writing questionnaire (N=20) and semi-structured interviews (N=13) with self-identified second- and subsequent Arab and Southwest Asian and North African (SWANA) diaspora generations in the U.S. We investigate participants' online dating experiences with attention to consent-related values, behaviors, and experiences. Findings highlight the U.S. Arab/SWANA diaspora's technocultures of consent - a conceptual framework we use to describe the understandings and practices of consent that are influenced, co-produced, or expressed by the interaction between technology and people. We demonstrate how the technocultures of consent conceptual framework reveals connections between individuals' identities and social positions, consent-related beliefs and behaviors and technology design, norms, and expectations. We also introduce the concepts of networked consent and consent concept alignment tests, and offer design considerations to promote consent for all.

The overturning of Roe v. Wade in 2022 by the U.S. Supreme Court in Dobbs v. Jackson exposed and exacerbated existing gaps in reproductive privacy. In the post-Roe era, aggressive surveillance by both government and private entities has made real and heightened concerns about privacy violations for people capable of pregnancy (PCOP). We investigate PCOPs' reproductive privacy concerns and the strategies they use to address these concerns post-Roe. We conducted semi-structured interviews with 18 adult cisgender women and transgender men in the U.S. Our findings show that for PCOPs, privacy risks are both persistent and anomalous, imposing what we conceptualize as reproductive privacy labor , a type of safety and data work that is, to them, both necessary and exhausting. We introduce the conceptual framework Sociotechnical Reproductive Privacy , outlining the relationships between actors, technologies, and identities that are implicated in the many contexts of reproductive privacy vulnerabilities post-Roe. We conclude with considerations for research and design, and explore the utility of approaches like refusal and regulation (e.g., technical, policy) in promoting sociotechnical reproductive privacy. This research underscores the urgent need to address the intersection of reproductive rights, privacy, and technology, offering insights into how affected individuals navigate and manage their reproductive health decisions in an increasingly surveilled sociotechnical landscape.

The historically controversial U.S. college admissions process is increasingly shaped by algorithmic systems, exacerbating the potential for controversies over admissions and their fairness. Despite their increased use, questions remain about how vendors who provide algorithmic admissions technologies legitimize them and how applicants perceive these technologies. We report on 1) a qualitative content analysis of admissions technology vendor websites, and 2) interviews with college applicants, highlighting the distance between vendors' claimed benefits for universities (e.g., increased decision-making efficiency) and applicants (e.g., ''unbiased'' decisions) and applicants' perceived harms to themselves (e.g., undermining holistic review, hindering diversity, equity, and inclusion efforts). We consider the implications of algorithmic admissions decision-making, including privacy harms, discuss regulatory implications, and offer recommendations to guide algorithmic transparency efforts. However, we caution that transparency would not address some harms perceived by applicants, like inaccuracy and privacy violations.

The landscape of digital privacy risks faced by individuals seeking abortions has grown increasingly complex following the overturn of Roe v. Wade. Reproductive healthcare providers are uniquely positioned to offer critical privacy guidance. We conducted interviews with 22 reproductive healthcare providers across the U.S. to explore their perceptions of privacy threats for abortion-seeking patients and the types of guidance they provide. Our findings show that providers are most concerned about privacy risks for vulnerable patients—minors, individuals seeking gender-affirming care, and those in abusive relationships—particularly regarding information that could be intercepted by people close to them, such as partners or relatives. However, providers generally do not perceive government surveillance or hostile actors as major threats to abortion-seeking patients. We conclude with an updated notion of informed consent and preliminary recommendations for ways healthcare providers can revise their threat models to better support the privacy of abortion-seeking patients.

Patent applications provide insight into how inventors imagine and legitimize uses of their imagined technologies; as part of this imagining they envision social worlds and produce sociotechnical imaginaries. Examining sociotechnical imaginaries is important for emerging technologies in high-stakes contexts such as the case of emotion AI to address mental health care. We analyzed emotion AI patent applications (N=58) filed in the U.S. concerned with monitoring and detecting emotions and/or mental health. We examined the described technologies' imagined uses and the problems they were positioned to address. We found that inventors justified emotion AI inventions as solutions to issues surrounding data accuracy, care provision and experience, patient-provider communication, emotion regulation, and preventing harms attributed to mental health causes. We then applied an ethical speculation lens to anticipate the potential implications of the promissory emotion AI-enabled futures described in patent applications. We argue that such a future is one filled with mental health conditions' (or 'non-expected' emotions') stigmatization, equating mental health with propensity for crime, and lack of data subjects' agency. By framing individuals with mental health conditions as unpredictable and not capable of exercising their own agency, emotion AI mental health patent applications propose solutions that intervene in this imagined future: intensive surveillance, an emphasis on individual responsibility over structural barriers, and decontextualized behavioral change interventions. Using ethical speculation, we articulate the consequences of these discourses, raising questions about the role of emotion AI as positive, inherent, or inevitable in health and care-related contexts. We discuss our findings' implications for patent review processes, and advocate for policy makers, researchers and technologists to refer to patent (applications) to access, evaluate and (re)consider potentially harmful sociotechnical imaginaries before they become our reality.

Emotion AI is increasingly used to automatically evaluate asynchronous hiring interviews. Although touted for increasing hiring fit and reducing bias, it is unclear how job-seekers perceive emotion AI-enabled asynchronous interviews. This gap is striking, given job-seekers' marginalized position in hiring and how job-seekers with marginalized identities may be particularly vulnerable to this technology's potential harms. Addressing this gap, we conducted exploratory interviews with 14 U.S.-based participants with direct, recent experience with emotion AI-enabled asynchronous interviews. While participants acknowledged the asynchronous, virtual modality's potential benefits to employers and job-seekers, they perceived harms to job-seekers associated with automatic emotion inferences that our analysis maps to distributive, procedural, and interactional injustices. We find that social identity can inform job-seekers' perceptions of emotion AI, extending prior work's understandings of the factors contributing to job-seekers' perceptions of AI (broadly) in hiring. Moreover, our results suggest that emotion AI use may reconfigure demands for emotional labor in hiring and that deploying this technology in its current state may unjustly risk harmful outcomes for job-seekers - or, at the very least, perceptions thereof, which shape behaviors and attitudes. Accordingly, we recommend against the present adoption of emotion AI in hiring, identifying opportunities for the design of future asynchronous hiring interview platforms to be meaningfully transparent, contestable, and privacy-preserving. We emphasize that only a subset of perceived harms we surface may be alleviated by these efforts; some injustices may only be resolved by removing emotion AI-enabled features.

Migrants experience unique needs and use social media, in part, to address them. While prior work has primarily focused on migrant populations who are vulnerable socio-economically and legally, less is known about how highly educated migrant populations use social media. Additionally, a growing body of work focuses on algorithmic perceptions and resistance, primarily from laypersons' perspectives rather than people with high degrees of algorithmic literacy. To address these gaps, we draw from interviews with 20 Chinese-born migrant technology professionals. We found that social media played an integral role in helping participants meet their unique needs but that participants perceived social media algorithms to negatively shape the content they consumed, which ultimately influenced their mobility-related aspirations and goals. We discuss how findings challenge the promise of algorithmic literacy and contribute to a human-centered conceptualization of algorithmic mobility as socially and algorithmically produced motion that concerns the movement of physical bodies and interactions as well as associated digital movement. Specifically, we introduce a fourth dimension of algorithmic mobility: algorithmically curated content on social media and elsewhere based on facets of users' identities directly influences users' mobility-related aspirations and goals, such as how, when, and where they go. Finally, we call for transnational policy interventions related to algorithms and highlight design considerations around content moderation, algorithmic user-control, and contestability.

Emotion AI, or AI that claims to infer emotional states from various data sources, is increasingly deployed in myriad contexts, including mental healthcare. While emotion AI is celebrated for its potential to improve care and diagnosis, we know little about the perceptions of data subjects most directly impacted by its integration into mental healthcare. In this paper, we qualitatively analyzed U.S. adults' open-ended survey responses (n = 395) to examine their perceptions of emotion AI use in mental healthcare and its potential impacts on them as data subjects. We identify various perceived impacts of emotion AI use in mental healthcare concerning 1) mental healthcare provisions; 2) data subjects' voices; 3) monitoring data subjects for potential harm; and 4) involved parties' understandings and uses of mental health inferences. Participants' remarks highlight ways emotion AI could address existing challenges data subjects may face by 1) improving mental healthcare assessments, diagnoses, and treatments; 2) facilitating data subjects' mental health information disclosures; 3) identifying potential data subject self-harm or harm posed to others; and 4) increasing involved parties' understanding of mental health. However, participants also described their perceptions of potential negative impacts of emotion AI use on data subjects such as 1) increasing inaccurate and biased assessments, diagnoses, and treatments; 2) reducing or removing data subjects' voices and interactions with providers in mental healthcare processes; 3) inaccurately identifying potential data subject self-harm or harm posed to others with negative implications for wellbeing; and 4) involved parties misusing emotion AI inferences with consequences to (quality) mental healthcare access and data subjects' privacy. We discuss how our findings suggest that emotion AI use in mental healthcare is an insufficient techno-solution that may exacerbate various mental healthcare challenges with implications for potential distributive, procedural, and interactional injustices and potentially disparate impacts on marginalized groups.

Self-presentation undergirds social interaction on social media. HCI and social computing scholarship draw on visibility to theorize self-presentation management; while research addresses how social media users leverage (in)visibility for self-presentation goals, how users perceive and assess the visibility of themselves and others merits investigation. We conducted interviews ( \(n=20\) ) to explore how U.S.-based social media users perceive and assess self visibility. Findings indicate that self visibility comprises a set of related objects’ visibility—content, persons, and identity—associated with distinct, but related, visibility attributes. We develop the visibility objects lens to examine self-presentation, contributing a unified social media visibility framework. We show how users perceive themselves as visible to platforms and algorithms, which act as visibility agents. We introduce reflected algorithmic visibility to describe awareness of visibility to platforms and algorithms informed by algorithmic feedback. We conclude with design implications of a visibility objects lens.

Algorithmic systems have infiltrated many aspects of our society, mundane to high-stakes, and can lead to algorithmic harms known as representational and allocative. In this paper, we consider what stigma theory illuminates about mechanisms leading to algorithmic harms in algorithmic assemblages. We apply the four stigma elements (i.e., labeling, stereotyping, separation, status loss/discrimination) outlined in sociological stigma theories to algorithmic assemblages in two contexts : 1) "risk prediction" algorithms in higher education, and 2) suicidal expression and ideation detection on social media. We contribute the novel theoretical conceptualization of algorithmic stigmatization as a sociotechnical mechanism that leads to a unique kind of algorithmic harm: algorithmic stigma. Theorizing algorithmic stigmatization aids in identifying theoretically-driven points of intervention to mitigate and/or repair algorithmic stigma. While prior theorizations reveal how stigma governs socially and spatially, this work illustrates how stigma governs sociotechnically.

Workplaces are increasingly adopting emotion AI, promising benefits to organizations. However, little is known about the perceptions and experiences of workers subject to emotion AI in the workplace. Our interview study with (n=15) US adult workers addresses this gap, finding that (1) participants viewed emotion AI as a deep privacy violation over the privacy of workers’ sensitive emotional information; (2) emotion AI may function to enforce workers’ compliance with emotional labor expectations, and that workers may engage in emotional labor as a mechanism to preserve privacy over their emotions; (3) workers may be exposed to a wide range of harms as a consequence of emotion AI in the workplace. Findings reveal the need to recognize and define an individual right to what we introduce as emotional privacy, as well as raise important research and policy questions on how to protect and preserve emotional privacy within and beyond the workplace.

Vulnerability intimately shapes the lived human experience and continues to gain attention in computer-supported cooperative work and human-computer interaction scholarship broadly, and in social media studies specifically. Social media comprise sociotechnical affordances that may uniquely shape lived experiences with vulnerability, rendering existing frameworks inadequate for comprehensive examinations of vulnerability as mediated on social media. Through interviews with social media users in the United States (N = 20) and drawing on feminist conceptualizations of vulnerability and social media disclosure and privacy scholarship, we propose a feminist taxonomy of social media vulnerability (FSMV). The FSMV taxonomy reflects vulnerabilitysources, states, andvalences, within which we introduce the state ofnetworked vulnerability andambivalent, desired, andundesired valences. We describe how social media enable forms of vulnerability different from in-person settings, challenge framings that synonymize vulnerability with risk/harm, and facilitate interdisciplinary theory-building. Additionally, we discuss hownetworked, ambivalent, andun/desired vulnerability extend and diverge from prior work to create a theoretically rich taxonomy that is useful for future work on social media and vulnerability. Finally, we discuss implications for design related to granular control over profile, content, and privacy settings, as well as implications for platform accountability, as they pertain to social media vulnerability.

Social comparison and social support have implications for individuals' wellbeing, offline and on social media. Perceptions of similarity underlie both social comparison and social support processes, though how comparison and support function in tandem in online spaces, and which aspects of identity and experiential similarity are salient to which comparison and support outcomes, merits investigation. Through interviews with people who have joined or considered joining social media-based support groups following pregnancy loss (N=18), we provide an intracommunity view into social comparison within online support groups. We identify a set of identity and experience attributes that inform perceptions of similarity and difference in these support spaces. We characterize tensions arising from these attributes and propose the preliminary Social Comparison and Social Support in Online Support Groups model to describe interactions between social support and comparison processes within online support groups. We further discuss findings' implications for design, including via introducing the tolerance principle of online health support groups. CAUTION: This paper includes quotes about pregnancy loss.

Emotion recognition technologies, while critiqued for bias, validity, and privacy invasion, continue to be developed and applied in a range of domains including in high-stakes settings like the workplace. We set out to examine emotion recognition technologies proposed for use in the workplace, describing the input data and training, outputs, and actions that these systems take or prompt. We use these design features to reflect on these technologies' implications using the ethical speculation lens. We analyzed patent applications that developed emotion recognition technologies to be used in the workplace (N=86). We found that these technologies scope data collection broadly; claim to reveal not only targets' emotional expressions, but also their internal states; and take or prompt a wide range of actions, many of which impact workers' employment and livelihoods. Technologies described in patent applications frequently violated existing guidelines for ethical automated emotion recognition technology. We demonstrate the utility of using patent applications for ethical speculation. In doing so, we suggest that 1) increasing the visibility of claimed emotional states has the potential to create additional emotional labor for workers (a burden that is disproportionately distributed to low-power and marginalized workers) and contribute to a larger pattern of blurring boundaries between expectations of the workplace and a worker's autonomy, and more broadly to the data colonialism regime; 2) Emotion recognition technology's failures can be invisible, may inappropriately influence high-stakes workplace decisions and can exacerbate inequity. We discuss the implications of making emotions and emotional data visible in the workplace and submit for consideration implications for designers of emotion recognition, employers who use them, and policymakers.

The workplace has experienced extensive digital transformation, in part due to artificial intelligence's commercial availability. Though still an emerging technology, emotional artificial intelligence (EAI) is increasingly incorporated into enterprise systems to augment and automate organizational decisions and to monitor and manage workers. EAI use is often celebrated for its potential to improve workers' wellbeing and performance as well as address organizational problems such as bias and safety. Workers subject to EAI in the workplace are data subjects whose data make EAI possible and who are most impacted by it. However, we lack empirical knowledge about data subjects' perspectives on EAI, including in the workplace. To this end, using a relational ethics lens, we qualitatively analyzed 395 U.S. adults' open-ended survey (partly representative) responses regarding the perceived benefits and risks they associate with being subjected to EAI in the workplace. While participants acknowledged potential benefits of being subject to EAI (e.g., employers using EAI to aid their wellbeing, enhance their work environment, reduce bias), a myriad of potential risks overshadowed perceptions of potential benefits. Participants expressed concerns regarding the potential for EAI use to harm their wellbeing, work environment and employment status, and create and amplify bias and stigma against them, especially the most marginalized (e.g., along dimensions of race, gender, mental health status, disability). Distrustful of EAI and its potential risks, participants anticipated conforming to (e.g., partaking in emotional labor) or refusing (e.g., quitting a job) EAI implementation in practice. We argue that EAI may magnify, rather than alleviate, existing challenges data subjects face in the workplace and suggest that some EAI-inflicted harms would persist even if concerns of EAI's accuracy and bias are addressed.

Documenting weight-related journeys (e.g., weight loss, weight gain) is prevalent on social media, as is weight stigma, resulting in easily accessible personal archives filled with emotional, and potentially stigmatizing content. Through semi-structured interviews with 17 U.S.-based social media users sharing weight-related journeys, we investigate the motivations for and impacts of returning to previously posted weight-related social media content. We show how these personal archives foster a contested relationship between one's past and current self, where returning to past content facilitates dynamic interpretations of the self. We argue these interpretations' impacts cannot be understood without acknowledging and addressing the socio-technical context in which they exist: one filled with weight stigma, fatphobia, and narrow body ideals. We introduce the novel concepts of Transtemporal Support and Transtemporal Harm to describe the support and harms that one experiences in the present from returning to their past social media content. We posit that designs accounting for transtemporal support 1) can facilitate reflective sense-making for users who create repositories of digital artifacts about sensitive, potentially stigmatizing experiences on social media, and 2) should not perpetuate transtemporal harm.

First-generation, low-income (FGLI) students face barriers to college access and retention that reproduce socioeconomic inequities. These students turn to social media for college-related social support. However, while students can reap benefits from social media, it is crucial to investigate under what conditions social media interactions facilitate or hinder students' access to college-related social support. We conducted in-depth, semi-structured interviews with 20 FGLI students in the United States who applied for college in the 2020-2021 application cycle. Our findings illustrate how FGLI identity disclosures on social media can facilitate access to college-related social support when met with supportive or neutral responses, while stigmatizing reactions can disrupt access to these benefits. We draw from the lenses of the "doubly disadvantaged'' and "privileged poor'' used to describe FGLI students in post-secondary education to argue that engaging in FGLI identity disclosures on social media can help students become academically and psychosocially prepared for collegiate environments. Finally, we discuss the implications of this work for theoretical frameworks centering social media and social support, consider when stigma might lead to support space abandonment, and describe the potential implications for social media design.

Despite debates about emotion artificial intelligence's (EAI) validity, legality, and social consequences, EAI is increasingly present in the high stakes context of hiring, with potential to shape the future of work and the workforce. The values laden in technology play a significant role in its societal impact.We conducted qualitative content analysis on the public-facing websites (N=229) of EAI hiring services. We identify the organizational problems that EAI hiring services claim to solve and reveal the values emerging in desired EAI uses as promoted by EAI hiring services to solve organizational problems. Our findings show that EAI hiring services market their technologies as technosolutions to three purported organizational hiring problems: 1) hiring (in)accuracy, 2) hiring (mis)fit, and 3) hiring (in)authenticity. We unpack these problems to expose how these desired uses of EAI are legitimized by the corporate ideals of data-driven decision making, continuous improvement, precision, loyalty, and stability. We identify the unfair and deceptive mechanisms by which EAI hiring services claim to solve the purported organizational hiring problems, suggesting that they unfairly exclude and exploit job candidates through EAI's creation, extraction, and affective commodification of a candidate's affective value through pseudoscientific approaches. Lastly, we interrogate EAI hiring service claims to reveal the core values that underpin their stated desired use: techno-omnipresence, techno-omnipotence, and techno-omniscience. We show how EAI hiring services position desired use of their technology as a moral imperative for hiring organizations with supreme capabilities to solve organizational hiring problems, then discuss implications for fairness, ethics, and policy in EAI-enabled hiring within the US policy landscape.

Now that the protections of Roe v. Wade are no longer available throughout the United States, the free flow of personal data can be used by legal authorities to provide evidence of felony. However, we know little about how impacted individuals approach their reproductive privacy in this new landscape. We conducted interviews with 15 individuals who may get/were pregnant to address this gap. While nearly all reported deleting period tracking apps, they were not willing to go much further, even while acknowledging the risks of generating data. Quite a few considered a more inhospitable, Handmaid's Tale like climate in which their medical history and movements would put them in legal peril but felt that, by definition, this reality was insuperable, and also that they were not the target—the notion that privileged location, stage of life did not make them the focus of government or vigilante efforts. We also found that certain individuals (often younger and/or with reproductive risks) were more attuned to the need to modify their technology or equipped to employ high and low-tech strategies. Using an intersectional lens, we discuss implications for media advocacy and propose privacy intermediation to frame our thinking about reproductive privacy.

The growth of technologies promising to infer emotions raises political and ethical concerns, including concerns regarding their accuracy and transparency. A marginalized perspective in these conversations is that of data subjects potentially affected by emotion recognition. Taking social media as one emotion recognition deployment context, we conducted interviews with data subjects (i.e., social media users) to investigate their notions about accuracy and transparency in emotion recognition and interrogate stated attitudes towards these notions and related folk theories. We find that data subjects see accurate inferences as uncomfortable and as threatening their agency, pointing to privacy and ambiguity as desired design principles for social media platforms. While some participants argued that contemporary emotion recognition must be accurate, others raised concerns about possibilities for contesting the technology and called for better transparency. Furthermore, some challenged the technology altogether, highlighting that emotions are complex, relational, performative, and situated. In interpreting our findings, we identify new folk theories about accuracy and meaningful transparency in emotion recognition. Overall, our analysis shows an unsatisfactory status quo for data subjects that is shaped by power imbalances and a lack of reflexivity and democratic deliberation within platform governance.

Some life transitions can be difficult to discuss on social media, especially with networks of known ties, due to challenges such as stigmatization. Separate online networks can provide alternative spaces to discuss life transitions. To understand why and how people turn to separate networks, we interviewed 28 participants who had recently experienced life transitions. While prior research tends to focus on one life transition in isolation, this work examines social media sharing behaviors across a wide variety of life transitions. We describe how people often turn to separate networks during life transitions due to challenges faced in networks of known ties, yet encounter new challenges such as difficulty locating these networks. We describe support from waiting contributors and virtual friends. Finally, we provide insight into how online separate networks can be better designed through enhancing search functionality, promoting contribution, and providing context-sensitive templates for sharing in online spaces.

Navigating conception, pregnancy, and loss is challenging for lesbian, gay, bisexual, transgender, and queer (LGBTQ) people, who experience stigma due to LGBTQ identity, other identities (e.g., loss), and intersections thereof. We conducted interviews with 17 LGBTQ people with recent pregnancy loss experiences. Taking LGBTQ identity and loss as a starting point, we used an intracategorical intersectional lens to uncover the benefits and challenges of LGBTQ-specific and non-LGBTQ-specific pregnancy and loss-related online spaces. Participants used LGBTQ-specific online spaces to enact individual, interpersonal, and collective resilience. However, those with multiple marginalized identities (e.g., people of color and non-partnered individuals), faced barriers in finding support within LGBTQ-specific spaces compared to those holding privileged identities (e.g., White and married). Non-LGBTQ spaces were beneficial for some informational needs, but not community and emotional needs due to pervasive heteronormativity, cisnormativity, and a perceived need to educate. We conceptualize experiences of exclusion as symbolic annihilation and intersectional invisibility, and discuss clinical implications and design directions.

Pregnancy loss is a common yet stigmatized experience. We investigate (non)disclosure of pregnancy loss among LGBTQ people to known ties on identified social media as well as what constitutes ideal socio-technical disclosure environments. LGBTQ persons experiencing loss face intersectional stigma for holding a marginalized sexual and/or gender identity and experiencing pregnancy loss. We interviewed 17 LGBTQ people in the U.S. who used social media and had recently experienced pregnancy loss. We demonstrate how the Disclosure Decision-Making (DDM) framework explains LGBTQ pregnancy loss (non)disclosure decisions, thereby asserting the framework's ability to explain (non)disclosure decisions for those facing intersectional stigma. We illustrate how one's LGBTQ identity shapes (non)disclosure decisions of loss. We argue that social media platforms can better facilitate disclosures about silenced topics by enabling selective disclosure, enabling proxy content moderation, providing education about silenced experiences, and prioritizing such disclosures in news feeds. CAUTION: This paper includes quotes about pregnancy loss.

Emotional validation describes when one believes that their activities, emotions, beliefs, or other reactions are relevant and meaningful given the circumstance. When people experience distressing, stigmatizing life events, their state of emotional validation and thus their perceived sense of normalcy is often disrupted. Online spaces offer opportunities for coping, managing, and making sense of distress and stigma. In this paper, we focus on pregnancy loss as the context of inquiry and as an important example of a disruptive experience that is also associated with stigma. We examine how online spaces help facilitate or disrupt the process of achieving emotional validation among pregnancy loss survivors. We conducted in-depth interviews with women in the United States who had recently experienced a pregnancy loss. We found that individuals seeking a sense of perceived normalcy after pregnancy loss engage in two forms of validation processes that result in emotional validation - informational and experiential. We identified encounters that disrupt the process of seeking, achieving, and maintaining emotional validation related to: information, designs, algorithms, and interpersonal interactions. We introduce the concept of algorithmic symbolic annihilation to describe the representational and emotional harm participants experienced when they felt they were targets of algorithms assuming that all desired pregnancies proceed as expected. Algorithmic symbolic annihilation refers to how algorithms perpetuate normative and stereotypical narratives about phenomena, where what they account for has power and authority, and what they do not account for does not. To aid in seeking, achieving and sustaining emotional validation among pregnancy loss survivors, we suggest designing for 1) representational belonging to combat symbolic annihilation and 2) information avoidance.

Authenticity, generally regarded as coherence between one's inner self and outward behavior, is associated with myriad social values (e.g., integrity) and beneficial outcomes, such as psychological well-being. Scholarship suggests, however, that behaving authentically online is complicated by self-presentation norms that make it difficult to present a complex self as well as encourage sharing positive emotions and facets of self and discourage sharing difficult emotions. In this paper, we position authenticity as a self-presentation norm and identify the sociomaterial factors that contribute to the learning, enactment, and enforcement of authenticity on the short-video sharing platform TikTok. We draw on interviews with 15 U.S. TikTok users to argue that normative authenticity and understanding of TikTok as a "fun" platform are mutually constitutive in supporting a "just be you" attitude on TikTok that in turn normalizes expressions of both positive and difficult emotions and experiences. We consider the social context of TikTok and use an affordance lens to identify anonymity, of oneself and one's audience; association between content and the "For You" landing page; and video modality of TikTok as factors informing authenticity as a self-presentation norm. We argue that these factors similarly contribute to TikTok's viability as a space for social support exchange and address the utility of the comments section as a site for both supportive communication and norm judgment and enforcement. We conclude by considering the limitations of authenticity as social norm and present implications for designing online spaces for social support and connection.

Algorithms in online platforms interact with users' identities in different ways. However, little is known about how users understand the interplay between identity and algorithmic processes on these platforms, and if and how such understandings shape their behavior on these platforms in return. Through semi-structured interviews with 15 US-based TikTok users, we detail users' algorithmic folk theories of the For You Page algorithm in relation to two inter-connected identity types: person and social identity. Participants identified potential harms that can accompany algorithms' tailoring content to their person identities. Further, they believed the algorithm actively suppresses content related to marginalized social identities based on race and ethnicity, body size and physical appearance, ability status, class status, LGBTQ identity, and political and social justice group affiliation. We propose a new algorithmic folk theory of social feeds-The Identity Strainer Theory-to describe when users believe an algorithm filters out and suppresses certain social identities. In developing this theory, we introduce the concept of algorithmic privilege as held by users positioned to benefit from algorithms on the basis of their identities. We further propose the concept of algorithmic representational harm to refer to the harm users experience when they lack algorithmic privilege and are subjected to algorithmic symbolic annihilation. Additionally, we describe how participants changed their behaviors to shape their algorithmic identities to align with how they understood themselves, as well as to resist the suppression of marginalized social identities and lack of algorithmic privilege via individual actions, collective actions, and altering their performances. We theorize our findings to detail the ways the platform's algorithm and its users co-produce knowledge of identity on the platform. We argue the relationship between users' algorithmic folk theories and identity are consequential for social media platforms, as it impacts users' experiences, behaviors, sense of belonging, and perceived ability to be seen, heard, and feel valued by others as mediated through algorithmic systems.

Automatic emotion recognition (ER)-enabled wellbeing interventions use ER algorithms to infer the emotions of a data subject (i.e., a person about whom data is collected or processed to enable ER) based on data generated from their online interactions, such as social media activity, and intervene accordingly. The potential commercial applications of this technology are widely acknowledged, particularly in the context of social media. Yet, little is known about data subjects' conceptualizations of and attitudes toward automatic ER-enabled wellbeing interventions. To address this gap, we interviewed 13 US adult social media data subjects regarding social media-based automatic ER-enabled wellbeing interventions. We found that participants' attitudes toward automatic ER-enabled wellbeing interventions were predominantly negative. Negative attitudes were largely shaped by how participants compared their conceptualizations of Artificial Intelligence (AI) to the humans that traditionally deliver wellbeing support. Comparisons between AI and human wellbeing interventions were based upon human attributes participants doubted AI could hold: 1) helpfulness and authentic care; 2) personal and professional expertise; 3) morality; and 4) benevolence through shared humanity. In some cases, participants' attitudes toward automatic ER-enabled wellbeing interventions shifted when participants conceptualized automatic ER-enabled wellbeing interventions' impact on others, rather than themselves. Though with reluctance, a minority of participants held more positive attitudes toward their conceptualizations of automatic ER-enabled wellbeing interventions, citing their potential to benefit others: 1) by supporting academic research; 2) by increasing access to wellbeing support; and 3) through egregious harm prevention. However, most participants anticipated harms associated with their conceptualizations of automatic ER-enabled wellbeing interventions for others, such as re-traumatization, the spread of inaccurate health information, inappropriate surveillance, and interventions informed by inaccurate predictions. Lastly, while participants had qualms about automatic ER-enabled wellbeing interventions, we identified three development and delivery qualities of automatic ER-enabled wellbeing interventions upon which their attitudes toward them depended: 1) accuracy; 2) contextual sensitivity; and 3) positive outcome. Our study is not motivated to make normative statements about whether or how automatic ER-enabled wellbeing interventions should exist, but to center voices of the data subjects affected by this technology. We argue for the inclusion of data subjects in the development of requirements for ethical and trustworthy ER applications. To that end, we discuss ethical, social, and policy implications of our findings, suggesting that automatic ER-enabled wellbeing interventions imagined by participants are incompatible with aims to promote trustworthy, socially aware, and responsible AI technologies in the current practical and regulatory landscape in the US.

Emotion recognition algorithms recognize, infer, and harvest emotions using data sources such as social media behavior, streaming service use, voice, facial expressions, and biometrics in ways often opaque to the people providing these data. People's attitudes towards emotion recognition and the harms and outcomes they associate with it are important yet unknown. Focusing on social media, we interviewed 13 adult U.S. social media users to fill this gap. We find that people view emotions as insights to behavior, prone to manipulation, intimate, vulnerable, and complex. Many find emotion recognition invasive and scary, associating it with autonomy and control loss. We identify two categories of emotion recognition's risks: individual and societal. We discuss findings' implications for algorithmic accountability and argue for considering emotion data as sensitive. Using a Science and Technology Studies lens, we advocate that technology users should be considered as a relevant social group in emotion recognition advancements.

Disclosures of distress and stigma on identified social media can be beneficial. Yet, many who may benefit from such disclosures do not engage in them. I examine factors that inform decisions to not disclose stigmatized experiences on identified social media. I conducted in-depth interviews with women in the US who used social media, had experienced pregnancy loss, and had not disclosed about their loss on identified social media. I detail six types of factors related to the self, audience, network, society, platform, and temporality that contribute to non-disclosure decisions. I show that the Disclosure Decision-Making (DDM) framework introduced in prior work explaining disclosures when they do occur, also explains non-disclosure decisions on social media. I show how DDM builds from and bridges prior privacy theories, namely, Communication Privacy Management and Contextual Integrity. I discuss design implications around removing barriers to disclosure to facilitate beneficial disclosures and reduce stigma.

Disclosing stigmatized experiences or identity facets on identified social media (e.g., Facebook) can be risky, inhibited, yet beneficial for the discloser. I investigate such disclosures' outcomes when they do happen on identified social media as perceived by the individuals who perform them. I draw on interviews with women who have experienced pregnancy loss and are social media users in the U.S. I document outcomes at the social/network, individual, and dyad levels. I highlight the powerful role of connecting with others with a similar experience within networks of known ties, how disclosures lead to relationship changes, how disclosers take on new social roles as mentors and support sources, and how helpful connections following disclosures originate from various kinds of ties via diverse communication channels. I emphasize reciprocal disclosures as an outcome contributing to further outcomes (e.g., destigmatizing pregnancy loss). I provide design implications related to facilitating being a support source and mentor, helpful reciprocal disclosures, and finding similar others within networks of known ties.

Social media platforms offer people a variety of ways to interact, ranging from public broadcast posts, to comments on posts, to private messages, to paralinguistic interactions such as "liking" posts. In 2015, the commenting function "replies" was temporarily removed from Tumblr, providing a unique opportunity to study the deprivation of a standard social media feature. We administered a survey to investigate Tumblr users' perceptions and use of replies. Respondents reported that they used replies to simultaneously support others' performance and their own. Respondents compared replies to other digital interaction channels such as paralinguistic interactions, the sharing feature "reblogs", and "direct messages" (DMs), citing social considerations and norms around each. We used Goffman's performance theory to draw insights on the perceived semi-public / semi-private space of replies, which enabled users to perform supportive actions that did not belong in their main blogging identity frontstage but that were not backstage either. We discuss the limitation of performance theory to describe a presentation to a limited but unknown audience, and we describe how replies enabled new frontstages such as the delicate ramp up to the performance of intimacy in DMs. We discuss implications for performing support and identity on social media with audiences that are perceived as limited but are unknown.

Pregnancy loss is a common experience that is often not disclosed in spite of potential disclosure benefits such as social support. To understand how and why people disclose pregnancy loss online, we interviewed 27 women in the U.S. who are social media users and had recently experienced pregnancy loss. We developed a decision-making framework explaining pregnancy loss disclosures on identified social network sites (SNS) such as Facebook. We introduce network-level reciprocal disclosure, a theory of how disclosure reciprocity, usually applied to understand dyadic exchanges, can operate at the level of a social network to inform decision-making about stigmatized disclosures in identified SNSs. We find that 1) anonymous disclosures on other sites help facilitate disclosure on identified sites (e.g., Facebook), and 2) awareness campaigns enable sharing about pregnancy loss for many who would not disclose otherwise. Finally, we discuss conceptual and design implications. CAUTION: This paper includes quotes about pregnancy loss.

In this multi-method study, we examine citation activity on English-language Wikipedia to understand how information claims are supported in a non-scientific open collaboration context. We draw on three data sources-edit logs, interview data, and document analysis-to present an integrated interpretation of citation activity and found pervasive themes related to controversy and conflict. Based on this analysis, we present and discuss information fortification as a concept that explains online citation activity that arises from both naturally occurring and manufactured forms of controversy. This analysis challenges a workshop position paper from Group 2005 by Forte and Bruckman, which draws on Latour's sociology of science and citation to explain citation in Wikipedia with a focus on credibility seeking. We discuss how information fortification differs from theories of citation that have arisen from bibliometrics scholarship and are based on scientific citation practices.

Indirect disclosure strategies include hinting about an experience or a facet of one's identity or relaying information explicitly but through another person. These strategies lend themselves to sharing stigmatized or sensitive experiences such as a pregnancy loss, mental illness, or abuse. Drawing on interviews with women in the U.S. who use social media and experienced pregnancy loss, we investigated factors guiding indirect disclosure decisions on social media. Our findings include 1) a typology of indirect disclosure strategies based on content explicitness, original content creator, and content sharer, and 2) an examination of indirect disclosure decision factors related to the self, audience, platform affordances, and temporality. We identify how people intentionally adapt social media and indirect disclosures to meet psychological (e.g., keeping a personal record) and social (e.g., feeling out the audience) needs associated with loss. We discuss implications for design and research, including features that support disclosures through proxy, and relevance for algorithmic detection and intervention. CAUTION: This paper includes quotes about pregnancy loss.

When people disclose information on social media that is sensitive or potentially stigmatized (e.g., mental illness, pregnancy loss), how do others decide to respond? We use interviews and vignettes to provide a response decision-making framework (RDM) that explains factors informing whether and how individuals respond to sensitive disclosures from their social media connections. The RDM framework includes factors related to the self, poster, and disclosure context (i.e., relational, temporal, social). Our findings include how people's decisions are complicated by balancing their own needs (e.g., privacy, wellbeing) as well as the posters’ (e.g., support) when seeing what they consider sensitive posts on social media. We identify empirically grounded insights and information that social media designs could surface to support both potential disclosers and responders. We argue that social media sites should provide privacy controls for both disclosers and responders, and facilitate the visibility of network-level support.

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.

Much of the existing research literature on email use focuses on productivity or work settings. However, personal use of email has rarely been studied in depth. With the growth of messaging platforms being used for an increasing amount of personal communication, yet email use remaining high, we were interested in learning what Americans are using email for in their daily lives in 2016. To explore this topic, we use qualitative data from over 150 interviews with personal email users as well as quantitative data from several larger survey-based studies. We will show that personal email use is very different from what has been previously studied by workplace researchers and that daily use is largely focused on receiving and viewing B2C messages such as coupons, deals, receipts, and event notifications with personal communication over email diminished to a rarer, less-than-daily occurrence. We discuss the implications of this for the design of email and communications clients and present a design and prototype for an application that seeks to support these more frequent uses of consumer email.

In recent years, several mobile applications allowed individuals to anonymously share information with friends and contacts, without any persistent identity marker. The functions of these "tie-based" anonymity services may be notably different than other social media services. We use semi-structured interviews to qualitatively examine motivations, practices and perceptions in two tie-based anonymity apps: Secret (now defunct, in the US) and Mimi (in China). Among the findings, we show that: (1) while users are more comfortable in self-disclosure, they still have specific practices and strategies to avoid or allow identification; (2) attempts for deidentification of others are prevalent and often elaborate; and (3) participants come to expect both negativity and support in response to posts. Our findings highlight unique opportunities and potential benefits for tie-based anonymity apps, including serving disclosure needs and social probing. Still, challenges for making such applications successful, for example the prevalence of negativity and bullying, are substantial.

In this paper, we examine stigma related to class identity online through an empirical examination of Elite University Class Confessions (EUCC). EUCC is an online space that includes a Facebook page and a surrounding sociotechnical ecosystem. It is a community of, for, and about low-income and first generation students at an elite university. By bringing in a community that learns and engages with users' socioeconomic struggles, EUCC engenders unique restorative properties for students experiencing class stigma. EUCC's restorative properties foster new ways of understanding one's stigmatized identity through meaning- making interactions in a networked sociotechnical system. We discuss how EUCC's design shapes the nature of user interactions around class stigma, and explore in depth how people experience stigma differently through the restorative properties of EUCC.

People can benefit from disclosing negative emotions or stigmatized facets of their identities, and psychologists have noted that imagery can be an effective medium for expressing difficult emotions. Social network sites like Instagram offer unprecedented opportunity for image-based sharing. In this paper, we investigate sensitive self-disclosures on Instagram and the responses they attract. We use visual and textual qualitative content analysis and statistical methods to analyze self-disclosures, associated comments, and relationships between them. We find that people use Instagram to engage in social exchange and story-telling about difficult experiences. We find considerable evidence of social support, a sense of community, and little aggression or support for harmful or pro-disease behaviors. Finally, we report on factors that influence engagement and the type of comments these disclosures attract. Personal narratives, food and beverage, references to illness, and self-appearance concerns are more likely to attract positive social support. Posts seeking support attract significantly more comments. CAUTION: This paper includes some detailed examples of content about eating disorders and self-injury illnesses.

This qualitative study examines privacy practices and concerns among contributors to open collaboration projects. We collected interview data from people who use the anonymity network Tor who also contribute to online projects and from Wikipedia editors who are concerned about their privacy to better understand how privacy concerns impact participation in open collaboration projects. We found that risks perceived by contributors to open collaboration projects include threats of surveillance, violence, harassment, opportunity loss, reputation loss, and fear for loved ones. We explain participants' operational and technical strategies for mitigating these risks and how these strategies affect their contributions. Finally, we discuss chilling effects associated with privacy loss, the need for open collaboration projects to go beyond attracting and educating participants to consider their privacy, and some of the social and technical approaches that could be explored to mitigate risk at a project or community level.

With recent shifts from email to messaging apps for personal communication, many communication partners are no longer able to converse using multiple threads within one platform as they used to via email. Romantic couples are the most frequently messaged contacts on mobile and communicate about a wide range of topics with each other, need to make joint decisions, and may have several roles in relation to each other (e.g., spouse, co-parent). Through a 3-week field study with ten U.S. diverse couples we studied how couples use a messaging app that allows them to compartmentalize their conversations into multiple "channels" around topics of their choice. We detail how couples managed and used channels in daily life, and channels' strengths and limitations. We found that channels made couples feel more organized, and helped with finding content and keeping track of topics. However, it was sometimes difficult to choose or navigate channels. We discuss perceived impacts of channels on relationships (e.g., topic-switching during conflicts), and outline design opportunities for messaging apps.

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.

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

I study self-disclosure and investigate ways in which social computing systems can be designed to allow people to disclose negatively-perceived or stigmatized experiences and find support in their social networks. My prior work has given me insight about online disclosures of depression and sexual abuse, the role of anonymity in support seeking, and the ways that people respond to such disclosures. In my dissertation I will use miscarriage as a context to investigate online disclosure and response practices around stigmatized and traumatizing topics with the goal of improving both theory and social media design practices.

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

This paper provides first steps toward an empirically grounded design vocabulary for assessable design as an HCI response to the global need for better information literacy skills. We present a framework for synthesizing literatures called the Interdisciplinary Literacy Framework and use it to highlight gaps in our understanding of information literacy that HCI as a field is particularly well suited to fill. We report on two studies that lay a foundation for developing guidelines for assessable information system design. The first is a study of Wikipedians', librarians', and laypersons' information assessment practices from which we derive two important features of assessable designs: information provenance and stewardship. The second is an experimental study in which we operationalize these concepts in designs and test them using Amazon Mechanical Turk (MTurk).