E-Vaping Patterns of Use, Information Needs and Risk Perception in the Post Pandemic Era: An International Survey and Social Media Listening Study
Bibliographic record
Abstract
Introduction The rapid proliferation of Novel Psychoactive Substances (NPSs) in e-cigarette products presents an urgent public health concern. These include herbal compounds, synthetic cannabinoids, cathinones, and other potent analogues, often used by young adults. Their unregulated presence in vape liquids raises significant addiction and intoxication risks. This study aimed to elucidate current trends in NPS vaping and user perceptions to inform clinical and regulatory interventions. Methods This study employed a mixed-methods design that combined an international survey with social media listening. The survey was in English and Greek, distributed between April and December 2024 via online forums (e.g., Bluelight), social media, and university mailing lists. Quantitative data were analysed using SPSS. Simultaneously, a netnographic study analyzed 11,721 social media comments (Reddit, YouTube) to extract motivations and perceptions. Data scraping tools Apify and ExportComments were employed, followed by thematic categorization and sentiment analysis. Results The survey received 1,045 responses where 210 respondents (20%) were vapers {49% males; 40% aged 25–39; (66% heterosexuals, 21% bisexuals); (47% from Greece, 22% from the UK, 18% from the US and Canada)}. The majority 52% were either high school or college leavers. 86% trusted online resources for information regarding vaping and only 32% referred to healthcare professionals. 81% stated that reliable online resources would work best as the main source of information. 62% of participants were tobacco smokers, and only 21% were ex-smokers. A total of 46% started e-vaping at 25 years or above; 60% used vapes daily. 72% had frequent or occasional cravings to vape; 37% tried to stop unsuccessfully. Meanwhile 31% complained about adverse events after vaping such as coughing, weakness, dizziness, sore throat, chest pain, palpitations, anxiety, COPD. Natural novel psychoactive substances, synthetic cannabinoids and flavorings as well as nicotine were mainly referred to as preferred in vaping liquids. 55% of participants stated that vaping poses medium risk. 32% of the vapers were also users of prescribed medications such as codeine, oxycodone, and 40% also users of NPSs (mainly herbals and benzodiazepines) combined with vaping. Netnographic findings revealed that the primary motivations for vaping were smoking cessation (63%), perception that vaping was less harmful (15%), and sensory appeal (8%). Youth often cited stress relief, peer influence and social identity as vaping drivers. Posts also highlighted widespread unawareness about NPS presence in vape products, reflecting a dangerous information gap. Conclusions Vapes are increasingly exploited as vehicles for a range of illicit drugs and NPSs, particularly among youth, with high addiction and toxicity risks. Real-time monitoring, better education strategies, and targeted policies are urgently needed. Understanding psychological motivators, such as stress coping or social appeal, is crucial to inform prevention strategies and regulatory responses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".