The Impact of Social Media Platforms on Privacy: Examining Legal and Ethical Boundaries in the U.S
Bibliographic record
Abstract
This paper explores the growing tension between user privacy and the operational models of major social media platforms in the United States. As these platforms expand their influence, the boundaries between ethical responsibility and legal compliance become increasingly blurred. The paper critically examines existing U.S. data privacy laws, evaluates ethical theories relevant to digital surveillance and consent, and assesses platform practices using recent case studies. Through qualitative analysis of legal frameworks, platform policies, and regulatory actions from 2018-2023, this study reveals significant gaps between legal compliance and ethical responsibility. Findings suggest that legal protections lag behind technological capabilities, and ethical frameworks are inconsistently applied across platforms. The research proposes a four-quadrant legal-ethical framework for evaluating platform practices and provides evidence that most social media companies operate in the "legal but unethical" category. Recommendations for comprehensive federal privacy legislation, algorithmic transparency mandates, and enhanced digital literacy programs are presented as essential steps toward protecting user privacy in the digital age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".