Evidence map›Paper›PMID 29107961›Full record

ArticlePloS one2017

National substance use patterns on Twitter.

Hsien-Wen Meng, Suraj Kath, Dapeng Li, Quynh C Nguyen

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
4.5field-weighted citation impact, top 5% of its field
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

17 citing papers in PubMed, 38 citations in OpenAlex.

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  16. Interpol review of controlled substances 2016-2019.Forensic science international. Synergy · 2020
    Review
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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

4 authors at 3 institutions in 1 country.

Hsien-Wen MengDepartment of Health, Kinesiology, and Recreation, College of Health, University of Utah, Salt Lake City, Utah, United States of America.ORCID http://orcid.org/0000-0002-8952-4286
Suraj KathSchool of Computing, University of Utah, Salt Lake City, Utah, United States of America.
Dapeng LiDepartment of Geography, South Dakota State University, Brookings, South Dakota, United States of America.
Quynh C NguyenDepartment of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, Maryland, United States of America.
University of Utah · USSouth Dakota State University · USUniversity of Maryland, College Park · US

Funding

HashtagHealth: A Social Media Big Data Resource for Neighborhood Effects ResearchK01ES025433 · NIEHS · UNIV OF MARYLAND, COLLEGE PARK · PI NGUYEN, QUYNH · 2014 to 2018
$816k
NIEHS NIH HHS K01 ES025433
6 · The paper itself

Abstract

purposeWe examined openly shared substance-related tweets to estimate prevalent sentiment around substance use and identify popular substance use activities. Additionally, we investigated associations between substance-related tweets and business characteristics and demographics at the zip code level.

methodsA total of 79,848,992 tweets were collected from 48 states in the continental United States from April 2015-March 2016 through the Twitter API, of which 688,757 were identified as being related to substance use. We implemented a machine learning algorithm (maximum entropy text classifier) to estimate sentiment score for each tweet. Zip code level summaries of substance use tweets were created and merged with the 2013 Zip Code Business Patterns and 2010 US Census Data.

resultsQuality control analyses with a random subset of tweets yielded excellent agreement rates between computer generated and manually generated labels: 97%, 88%, 86%, 75% for underage engagement in substance use, alcohol, drug, and smoking tweets, respectively. Overall, 34.1% of all substance-related tweets were classified as happy. Alcohol was the most frequently tweeted substance, followed by marijuana. Regression results suggested more convenience stores in a zip code were associated with higher percentages of tweets about alcohol. Larger zip code population size and higher percentages of African Americans and Hispanics were associated with fewer tweets about substance use and underage engagement. Zip code economic disadvantage was associated with fewer alcohol tweets but more drug tweets.

conclusionsThe patterns in substance use mentions on Twitter differ by zip code economic and demographic characteristics. Online discussions have great potential to glorify and normalize risky behaviors. Health promotion and underage substance prevention efforts may include interactive social media campaigns to counter the social modeling of risky behaviors.

Indexed as

Social MediaHumansSubstance-Related DisordersUnited States

Identifiers

PMID29107961
PMCPMC5673183
OpenAlexW2767551651

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.