Social Media Shadows: Unveiling the Hidden Struggles of African American Youth on Facebook
Bibliographic record
Abstract
While Facebook has become a central element of digital culture, its impact on the mental health of African American youth remains underexplored, particularly in relation to intersecting marginalized identities. Despite extensive research on social media and youth well-being, there is a significant gap in understanding how Facebook contributes to mental health challenges like depression and anxiety, especially for African American adolescents who experience systemic racism and discrimination both offline and online. This paper addresses these shortcomings by examining the intersection of race, socio-economic status, and the pervasive influence of social media on African American youth. Utilizing the Ecological Systems Model and Cultural Historical Activity Theory (CHAT), this study critically analyzes existing literature to uncover how Facebook exacerbates mental health struggles through online discrimination, cyberbullying, and harmful social comparison. Furthermore, the research highlights the underrepresentation of African American LGBTQ+ youth in current studies, emphasizing the need for intersectional approaches. Findings reveal that while Facebook offers opportunities for connection and identity exploration, it also intensifies mental health challenges due to frequent exposure to online racism and social exclusion. This study concludes by advocating for targeted interventions, including digital literacy programs, supportive online communities, and improved content moderation, to mitigate the negative mental health impacts on African American youth in the digital age.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".