Shifting Narratives in Media Coverage Across a Decade of Drug Discourse in the Philadelphia Inquirer: Qualitative Sentiment Analysis
Notice bibliographique
Résumé
BACKGROUND: The media has immense power in shaping public narratives surrounding sensitive topics such as substance use. Its portrayals can unintentionally fuel harmful stereotypes and stigma, negatively impacting individuals struggling with addiction, influencing policy decisions, and hindering broader public health efforts. OBJECTIVE: This study aimed to examine how the regional newspaper, The Philadelphia Inquirer, covered events related to illicit drug use between 2013 and 2022, focusing on linguistic patterns and themes associated with specific types of substances. METHODS: We collected a dataset of 157,476 articles published in The Philadelphia Inquirer between 2013 and 2022 and categorized mentioned substances into 8 classes: stimulants, narcotics, cannabis, hallucinogens, depressants, designer drugs, drugs of concern, and treatment medications. From these 157,476 articles, we identified 3661 (2.32%) that mentioned at least 1 substance with potential for misuse. Using dynamic topic modeling, we analyzed thematic evolution in coverage across different drug classes. We then applied aspect-based sentiment analysis to extract the most significant phrases mentioned in each distinct drug class annually and examined the sentiments around these aspects to understand shifting discourse patterns. RESULTS: Cannabis (1575/3661, 43.02%) and narcotics (1361/3661, 37.17%) dominated the coverage, with 2018 showing peak drug-related reporting (666/3661, 18.19%). Our substance co-occurrence analysis revealed that heroin was most frequently discussed alongside treatment medications (methadone, naloxone, and buprenorphine), reflecting evolving approaches to opioid use disorder. Topic modeling revealed distinct themes across drug classes: legislative and medical aspects dominated cannabis coverage, while narcotics coverage focused heavily on overdose deaths and safe injection sites, particularly during 2017 to 2018. Stimulant coverage centered on feature news and crime-related reporting, while treatment coverage showed an increasing focus on overdose prevention by 2021. The aspect-based sentiment analysis showed that 74.3% (165/222) of extracted aspects were portrayed negatively across all drug classes, with narcotics maintaining consistently negative sentiment throughout the period. However, some drug classes showed notable evolution: hallucinogens demonstrated a marked shift in sentiment score (SS) from negative coverage in 2013 (-0.79 SS) to positive coverage of therapeutic applications by 2021 (+0.47 SS), while cannabis coverage reflected complex societal debates, with industry and business aspects showing strong positive sentiment score peaks (0.64 SS in 2019) even as legislation and policy aspects remained volatile (-0.76 SS in 2013 to 0.61 SS in 2019 and declining to -0.31 SS by 2022). CONCLUSIONS: Our analysis revealed a predominance of negative and punitive language in drug-related news coverage, with limited representation of harm reduction principles. While some drug classes, particularly cannabis and hallucinogens, saw evolving narratives toward medical applications and policy reform, coverage of narcotics remained primarily focused on crime and overdose. These findings suggest a need for more balanced reporting that incorporates harm reduction perspectives and avoids potentially stigmatizing language when covering substance use disorders.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| 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 ».