SOCIAL MEDIA AND COLLEGE-RELATED SOCIAL SUPPORT EXCHANGE FOR
FIRST-GENERATION, LOW-INCOME STUDENTS: THE ROLE OF IDENTITY DISCLOSURES
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.
CONCEPTUALIZING ALGORITHMIC STIGMATIZATION
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.
TOWARD A FEMINIST SOCIAL MEDIA VULNERABILITY TAXONOMY
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 vulnerability sources, states, and valences, within which we introduce the state of networked vulnerability and ambivalent, desired, and undesired 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 how networked, ambivalent, and un/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.
"ON MY HEAD ABOUT IT": COLLEGE ASPIRATIONS, SOCIAL MEDIA PARTICIPATION, AND COMMUNITY CULTURAL WEALTH
Abstract: Given the widespread use of social media among adolescents, online interactions that facilitate high school students’ college knowledge acquisition could have a transformative impact on college access patterns, especially for underrepresented students. Our study uses interview data collected from Black high school students in Detroit (N=24) to examine their experiences and perceptions as they prepare for the transition to post-secondary education. In contrast to traditional social capital perspectives that tend to dominate social media scholarship, we instead employ a Community Cultural Wealth framework to reveal how students access distinctive forms of cultural resources via online and offline interactions. Our findings suggest students used social media to access cultural wealth as they (1) developed post-secondary educational aspirations, (2) planned to navigate the post-secondary admissions process, (3) resisted stereotypes about youth from Detroit, and (4) engaged in platform-switching to cultivate their college information networks online.
SCHOLARSHIP ON WELL-BEING & SOCIAL MEDIA: A SOCIOTECHNICAL PERSPECTIVE
Abstract: Evaluating the well-being implications of social media use is challenging for many reasons, including finding appropriate theoretical and methodological approaches that do not exclusively center either the technology (and its structural features) or the user (and their motivations, psychological disposition, etc.). We argue that many research questions would benefit from a more integrated approach that fully acknowledges both these elements and their mutually constitutive relationship to one another. This essay highlights the possibilities presented by one intellectual tradition that acknowledges how the materiality of an artifact intertwines with social factors and allows us to better understand how technology and people mutually shape one another: the sociotechnical perspective. We describe three broad domains—self-presentation, social capital, and social support—that are relevant to one's well-being and are especially well-aligned with this approach.
LGBTQ PERSONS’ PREGNANCY LOSS DISCLOSURES TO KNOWN TIES ON SOCIAL MEDIA: DISCLOSURE DECISIONS & IDEAL DISCLOSURE ENVIRONMENTS
Abstract: 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.
FIRST-GENERATION, LOW-INCOME STUDENTS AS DATA SUBJECTS IN HIGHER EDUCATION PROFILING AND PREDICTION AI/ML APPLICATIONS
Abstract: Artificial intelligence and machine learning applications span myriad contexts ranging from policing to disease diagnostics. While scholars have demonstrated and condemned both potential and extant harms brought about by these technologies, arguably less critical attention has been paid to the ways in which institutions of higher education are leveraging these technological capabilities in ways that may implicate low-resourced college students. We argue that existing AI/ML research articles in the higher education domain sometimes claim to support first-generation, lowincome students, but do so without a robust consideration of how their developments may be deployed at the expense of these students’ self-concept and agency. Furthermore, we assert that these applications produce stigmatizing data bodies around first-generation, low-income students that these students, as data subjects, have little control over.