Global Influence of Cannabis Legalization on Social Media Discourse: Mixed Methods Study
Bibliographic record
Abstract
Background: Cannabis is the third most consumed drug worldwide, with its use linked to a high number of substance use disorders, particularly among young men. Associated mortality causes include traffic accidents and cardiovascular diseases. The global expansion of cannabis legalization has sparked debates about its impact on risk perception, with risk perception decreasing in countries with permissive laws. Social media analysis, such as on Twitter (subsequently rebranded as X), is a useful tool for studying these perceptions and their variation by geographic region. Objective: This study aims to analyze Twitter users' perceptions of cannabis use and legalization, taking into account the geographic location of the tweets. Methods: A mixed methods approach was used to analyze cannabis-related tweets on Twitter, using keywords such as "cannabis," "marijuana," and "hashish." Tweets were collected from January 1, 2018, to April 30, 2022, in English and Spanish, and only those with at least 10 retweets were included. The content analysis involved an inductive-deductive approach, resulting in the classification of tweets into thematic categories, including discussions on legalization. Results: The tweet analysis showed that in America, Europe, and Asia, political discussions about cannabis were the most common topic, while personal testimonies dominated in Oceania and Africa. In all continents, personal experiences with cannabis use were mostly positive, with Oceania recording the highest percentage (1642/2695, 60.93%). Regarding legalization, Oceania also led with the highest percentage of tweets in favor (1836/2695, 68.13%), followed by America and Africa, while support in Europe and Asia was slightly lower, with about half of the tweets in favor. Conclusions: The political debate has been the most frequently mentioned topic, reflecting the current situation in which legislative changes are being discussed in many countries. The predominance of opinions in favor of legalization, combined with the prevalence of positive experiences expressed about cannabis, suggests that the health risks associated with cannabis use are being underestimated in the public debate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".