Bibliographic record
Abstract
Social media platforms collect and analyze large amounts of user data.This enables pervasive surveillance that shapes attention, advertising, and civic life.This paper asks: How do individuals perceive social media surveillance?How do those perceptions relate to privacy behaviors and demographic factors?Building on existing literature about targeted advertising, platform design, and privacy harms, the study combines a technical review of social media infrastructure with an empirical survey.The survey used Google Forms, with 127 responses from August to September 2023.Survey measures were ordinal-coded and analyzed with pairwise exclusion for missing data.Results show 72.4% of respondents believe their activity is monitored.Half (50.4%) are "partially worried" about data being sold.About 68.5% have either deleted platforms or are considering doing so (27.6% deleted; 40.9% considering).Statistical tests indicate a significant association between gender and worry level ((8) = 21.091,p = .007,V = .228).The relationship between worry and deleting a platform approached significance ((12) = 19.027,p = .088,V = .137).Age was not significantly associated with worry.These findings challenge generational privacy indifference and support targeted interventions for gender-specific concerns. Introduction: Overview of Social Media SurveillanceSocial media apps have transformed how people form relationships, consume news, and construct their identities.This connectivity depends on systems that collect and analyze large amounts of behavioral and social data.Platforms focus on capturing attention and enabling targeted advertising.These commercial logics drive surveillance practices that shape political persuasion and civic life [1][2].Companies also share data with or respond to requests from state actors.This extends surveillance into governance and law enforcement [3].Scholars describe social media surveillance through distinct practices: collaborative identity construction, monitoring of social ties, searchable social relations, shifting interfaces, and combining diverse social contexts into single profiles [4].To understand how these elements contribute to surveillance, we can utilize the framework of contextual integrity.This highlights the importance of context and the proper movement of information in privacy.This framework illustrates how platform design fosters surveillance by disrupting contextual norms for information flow.This paper examines how users perceive social media surveillance and how these perceptions relate to privacy behaviors and demographic factors.The study combines a brief technical review of platform mechanisms with an anonymous survey.The survey was administered via Google Forms using snowball sampling (N = 127, August-September 2023).Survey items were ordinal-coded and analyzed with pairwise exclusion and chi-square tests.Key findings are concise.Most respondents (72.4%) believed they were monitored on platforms.Concern about companies selling information was moderate.In total, 50.4 percent reported partial worry, 22.0 percent expressed worry, and 7.9 percent expressed extreme concern.Protective intentions were common.About 68.5 percent had either deleted platforms or were considering it (27.6 percent deleted; 40.9 percent considering).Statistical tests show a
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".