Shifting Narratives in Media Coverage Across a Decade of Drug Discourse in the Philadelphia Inquirer: Qualitative Sentiment Analysis
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".