Abstract
Social media debates on e-cigarettes raise public health concerns due to evolving evidence and conflicting guidelines. We analyzed 25,127 vaping-related comments to explore how users discuss vaping-related topics. Our analysis revealed 25 topics grouped into three parent topics: positive effects of vaping, negative effects of vaping, and vaping-related news & information. Significant differences were observed in psycholinguistic characteristics with high emotional exchanges as positive and negative effects of e-cigarettes are debated. Our results can inform interventions promoting science literacy and misinformation resistance.
Keywords: Science literacy, social media, topic modeling, e-cigarettes
1. Introduction
E-cigarettes are devices that produce an aerosol often containing nicotine and flavorings. While linked to health risks, some evidence suggests e-cigarettes may be an effective smoking cessation tool sparking social media debates [1]. Currently, there is limited knowledge on how users engage with scientific information, nudge peers, and resist/accept misinformation. This study aims to (a) explore how peers embed and use science-based evidence in their interactions and (b) assess public attitudes towards vaping-related science topics.
2. Methods
We extracted 26,598 vaping-related comments from the subreddit r/Science using the Pushshift API and key terms like “vape”, “e-cigarettes”, or “vaping” [2]. We removed weblinks, symbols, duplicated words, and comments shorter than 50 characters. We used stemming and lemmatization to address redundant word forms prevalent in social media. We used BERTopic to generate interpretable topic representations [3]. Topics were manually labeled using grounded theory analysis [4]. Sentiment and psycholinguistic features were measured using SEANCE [5]. Differences in component scores by the parent topics were assessed by one-way ANOVA with post-hoc tests (α = 0.05).
3. Results
Of the 25,127 comments, 6,102 comments were clustered into 25 topics with 19,025 comments placed in an outlier cluster to maintain interpretability. Topics were grouped into three parent topics: positive effects of vaping (PEV), negative effects of vaping (NEV), and vaping-related news & information (VNI). VADER sentiment, fear/disgust, and trust component scores were significantly different across parent topics whereas certainty scores were not. Post-hoc tests showed significant differences in fear/disgust between all pairs and in trust scores between NEV and PEV.
4. Discussion
Our findings showed BERTopic extracted interpretable topics reflecting users’ opinions and experiences with vaping. Potential applications of this research include creating chatbots that enhance resistance to science misinformation in behavior change settings. The fear and disgust component scores were higher in the PEV and NEV topics than VNI. Designing chatbot messages to promote smoking cessation could avoid alarming language and use clear trustworthy information. Limitations include using a single coder, an outlier cluster consisting of 75% of the data set, and using only a single platform.
5. Conclusion
We used novel methods in data science to explore psycholinguistic traits in science discussions on social media, offering insights to counter health misinformation.
Acknowledgements
Research reported in this publication was supported by the National Library of Medicine of the National Institutes of Health under award numbers 1R01LM012974 and 1R01AG089193. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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