ALGORITHMS, ADDICTION, AND ADOLESCENT MENTAL HEALTH: An Interdisciplinary Study to Inform State-level Policy Action to Protect Youth from the Dangers of Social Media
Notice bibliographique
Résumé
A recent Wall Street Journal investigation revealed that TikTok floods child and adolescent users with videos of rapid weight loss methods, including tips on how to consume less than 300 calories a day and promoting a "corpse bride diet," showing emaciated girls with protruding bones. The investigation involved the creation of a dozen automated accounts registered as 13-year-olds and revealed that TikTok algorithms fed adolescents tens of thousands of weight-loss videos within just a few weeks of joining the platform. Emerging research indicates that these practices extend well beyond TikTok to other social media platforms that engage millions of U.S. youth on a daily basis.Social media algorithms that push extreme content to vulnerable youth are linked to an increase in mental health problems for adolescents, including poor body image, eating disorders, and suicidality. Policy measures must be taken to curb this harmful practice. The Strategic Training Initiative for the Prevention of Eating Disorders (STRIPED), a research program based at the Harvard T.H. Chan School of Public Health and Boston Children's Hospital, has assembled a diverse team of scholars, including experts in public health, neuroscience, health economics, and law with specialization in First Amendment law, to study the harmful effects of social media algorithms, identify the economic incentives that drive social media companies to use them, and develop strategies that can be pursued to regulate social media platforms' use of algorithms. For our study, we have examined a critical mass of public health and neuroscience research demonstrating mental health harms to youth. We have conducted a groundbreaking economic study showing nearly $11 billion in advertising revenue is generated annually by social media platforms through advertisements targeted at users 0 to 17 years old, thus incentivizing platforms to continue their harmful practices. We have also examined legal strategies to address the regulation of social media platforms by conducting reviews of federal and state legal precedent and consulting with stakeholders in business regulation, technology, and federal and state government.While nationally the issue is being scrutinized by Congress and the Federal Trade Commission, quicker and more effective legal strategies that would survive constitutional scrutiny may be implemented by states, such as the Age Appropriate Design Code Act recently adopted in California, which sets standards that online services likely to be accessed by children must follow. Another avenue for regulation may be through states mandating that social media platforms submit to algorithm risk audits conducted by independent third parties and publicly disclose the results. Furthermore, Section 230 of the federal Communications Decency Act, which has long shielded social media platforms from liability for wrongful acts, may be circumvented if it is proven that social media companies share advertising revenues with content providers posting illegal or harmful content.Our research team's public health and economic findings combined with our legal analysis and resulting recommendations, provide innovative and viable policy actions that state lawmakers and attorneys general can take to protect youth from the harms of dangerous social media algorithms.
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 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| 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 ».