Loopholes in control of online alcohol marketing in Thailand: Analysis of alcohol-related internet content for Thai audience extracted by artificial intelligence
Bibliographic record
Abstract
Introduction: Thailand’s alcohol control laws include an extensive ban on alcohol marketing. However, loopholes exist in online marketing, yet online marketing of alcohol targeting audiences in Thailand has not yet been systematically described. The objective of this study was to describe online alcohol marketing activities in the Thai language. Methods: We used an artificial intelligence (AI) platform to collect alcohol marketing content on the internet. We prepared a database of search terms related to alcohol marketing. The platform automatically searched and filtered alcohol marketing content that violated control and regulatory measures. We analyzed data using descriptive statistics. Results: Our analyses included data from 13,301 posts generated by 4,638 users. The most common violation of the Alcoholic Beverage Control Act of 2008 was the use of alcohol brand trademarks or symbols (70%). The most common content producers were restaurants, pubs, and bars, followed by wholesale/retail stores and influencers. Content materials focused on driving awareness and drinking methods. Content materials did not mention the location of sales, shipping methods, discounts, free products, or giveaways. Discussion: We found violations of regulations for alcohol control measures in Thailand among online media posts in the Thai language. Potential selection bias from search engine algorithms and the limited generalizability should be considered as caveats in the interpretation of the study findings.
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 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".