ArticleJournal of studies on alcohol and drugs2017
A Descriptive Study of the Prevalence and Typology of Alcohol-Related Posts in an Online Social Network for Smoking Cessation.
Article in Journal of studies on alcohol and drugs, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Social Network Analysis of an Online Smoking Cessation Community to Identify Users' Smoking Status.Healthcare informatics research · 2021Article
- Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review.JMIR public health and surveillance · 2020Review
- A Digital Smoking Cessation Program for Heavy Drinkers: Pilot Randomized Controlled Trial.JMIR formative research · 2020Article
- Commentary on Cohn and Colleagues: Discussions of Alcohol Use in an Online Social Network for Smoking Cessation: Analysis of Topics, Sentiment, and Social Network Centrality (ACER, 2019).Alcoholism, clinical and experimental research · 2019Article
- Discussions of Alcohol Use in an Online Social Network for Smoking Cessation: Analysis of Topics, Sentiment, and Social Network Centrality.Alcoholism, clinical and experimental research · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 6 institutions in 2 countries.
Funding
Abstract
objectiveAlcohol use and problem drinking are associated with smoking relapse and poor smoking-cessation success. User-generated content in online social networks for smoking cessation provides an opportunity to understand the challenges and treatment needs of smokers. This study used machine-learning text classification to identify the prevalence, sentiment, and social network correlates of alcohol-related content in the social network of a large online smoking-cessation program, BecomeAnEX.org.
methodData were analyzed from 814,258 posts (January 2012 to May 2015). Posts containing alcohol keywords were coded via supervised machine-learning text classification for information about the user's personal experience with drinking, whether the user self-identified as a problem drinker or indicated problem drinking, and negative sentiment about drinking in the context of a quit attempt (i.e., alcohol should be avoided during a quit attempt).
resultsLess than 1% of posts were related to alcohol, contributed by 13% of users. Roughly a third of alcohol posts described a personal experience with drinking; very few (3%) indicated "problem drinking." The majority (70%) of alcohol posts did not express negative sentiment about drinking alcohol during a quit attempt. Users who did express negative sentiment about drinking were more centrally located within the network compared with those who did not.
conclusionsDiscussion of alcohol was rare, and most posts did not signal the need to quit or abstain from drinking during a quit attempt. Featuring expert information or highlighting discussions that are consistent with treatment guidelines may be important steps to ensure smokers are educated about drinking risks.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.