Marketing Cannabis Through Social Media: A Descriptive Analysis of X-Posts by Five Prominent Cannabis Companies
Bibliographic record
Abstract
As cannabis legalization spreads across North America, the marketing strategies employed by cannabis companies warrant critical scrutiny, particularly on digital platforms. This study offers quantitative and qualitative descriptive analyses of 3,755 original posts made in 2022 on the X platform (formerly Twitter) by five prominent North American cannabis companies: Canopy Growth Corporation, Aurora Cannabis Inc., Tilray Brands Inc., The Cronos Group, and Organigram Holdings Inc. Findings indicate that besides conventional corporate and informational communication, cannabis companies frame their products as solutions for mental health issues, chronic pain, and opioid addiction—using self-sponsored or poorly contextualized research to support their claims. Marketing content also sought to normalize cannabis consumption through links to fitness, sustainability, and wellness culture, drawing heavily on tactics used by the alcohol and tobacco industries. Notably, product announcements emphasized flavored and lifestyle-friendly items such as fruit-flavored vapes and cannabis-infused edibles. Additionally, companies utilized social and cultural theme days and corporate social responsibility (CSR) to build brand identity and indirectly promote consumption. Despite regulatory restrictions—especially stringent in Canada—many companies appeared to exploit legal ambiguities and weak enforcement mechanisms. The study raises public health concerns about the normalization of cannabis, particularly among youth, and the blurring of lines between factual information and covert promotion. These findings underscore the need for stronger digital marketing regulations, enhanced monitoring, and international cooperation to mitigate public health risks associated with cannabis marketing in online spaces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".