Understanding Negative Health Outcomes of Vaping by Mining Millions of Posts and Comments in Reddit.

Dian Hu, Dezhi Wu, Erin Kasson, Patricia Cavazos-Rehg, Hongfang Liu, Ming Huang

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Dian HuMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, TX, USA.

Dezhi WuMolinaroli College of Engineering and Computing, University of South Carolina, Columbia, SC, USA.

Erin KassonSchool of Medicine, Washington University in St. Louis, MO, USA.

Patricia Cavazos-RehgSchool of Medicine, Washington University in St. Louis, MO, USA.

Hongfang LiuMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, TX, USA.

Ming HuangMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electronic cigarette (vaping) usage in the U.S. has steadily increased, raising significant public health concerns. Extensive research demonstrates various negative health outcomes associated with vaping. However, many potential harms remain understudied, especially those directly reported by users. Social media platforms such as Reddit offer rich, real-time sources of unfiltered personal accounts, presenting a unique opportunity to explore health outcomes beyond traditional clinical research. In this study, we systematically investigated potential negative health outcomes (NHOs) by analyzing millions of posts and comments from 15 active vaping-related subreddits in 2019. Employing robust data-driven methodologies, including advanced natural language processing (NLP) techniques such as sentiment analysis, UMLS tagging, and topic modeling, we identified distinct patterns of vaping-related health concerns. Our findings highlight the value of user-generated content for early detection of emerging risks, guiding clinicians, policymakers, and public health initiatives aimed at mitigating vaping-related harms, particularly among younger populations.

Indexed as

Data MiningSocial MediaVapingHumansNatural Language ProcessingUnited States

Identifiers

PMID41726526

PMCPMC12919411

What Socratic holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.