Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».