Analyzing sentiments and themes on cannabis in Canada using 2018 to 2020 Twitter data
Bibliographic record
Abstract
INTRODUCTION: The Canadian Cannabis Act came into effect on October 17, 2018, which allowed Canadian adults to consume cannabis for non-medical purposes (Government of Canada, Cannabis regulations (SOR/2018-144). Cannabis Act, (2018a); Parliament of Canada, C-45: an Act respecting cannabis and to amend the Controlled Drugs and Substances Act, the Criminal Code and other Acts, 2018). With this major policy change, it is unknown how the attitude of the public changed and how information on cannabis changed. Social media platforms, including Twitter, are significant venues for studying emerging patterns in social issues such as cannabis legalization. This study aimed to examine sentiments, themes and contents of cannabis-related tweets by suppliers (both licensed and unlicensed) and general tweets in Canada. To our knowledge, this is the first analysis that mixes sentiment analysis and thematic analysis of Canadians' cannabis-related Twitter data. METHOD: A sample of Canadian cannabis-related tweets was collected from January 2018 to August 2020 through the Twitter Application Programming Interface (API). Using a standard access token and the Twitter Standard Search API, tweets were extracted based on Twitter handles to capture the content of both licensed and unlicensed cannabis retailers in Canada, as well as relevant cannabis-related keywords to capture public content. We conducted sentiment and positive polarity analyses, and content analysis to identify attitudes and themes around cannabis use in Canada. RESULTS: This study gathered and analyzed a total of 44,970 tweets in the sentiment analysis and a total of 1035 tweets in the thematic analysis. Descriptive analysis showed that monthly tweets peaked prior to legalization in October 2018 and again during the initial wave of the COVID-19 pandemic in February and March 2020. The data showed an overall positive sentiment polarity with a high of + 0.24 in April 2019 and a low of + 0.14 in March 2020. Thematic analysis revealed the themes: (i) education/information, (ii) uses of cannabis, (iii) cannabis products including packing, quality, price, types, and sources, (iv) cannabis policies including regulations and public safety, (v) access, (vi) social issues include gender and stigma, and (vii) COVID-19 impact. CONCLUSION: This study combined the power of big data collection and analysis with manual coding and analysis methods to extract rich content from large data using social media communications on issues related to cannabis in Canada. The findings of this study may inform policies on advertising cannabis products and highlighted some patterns related to education, access, and safety that deserve further investigation.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".