Evidence map›Paper›PMID 37711755›Full record

ArticlePsychiatric research and clinical practice2023

Exposure to Alcohol-Related Content on Social-Media.

Kathryn Janiuk, Nasim Maleki

Abstract read
In one paragraph

Article in Psychiatric research and clinical practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Modern sources of controls in case-control studies.American journal of epidemiology · 2025
    Article
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

2 authors.

Kathryn JaniukPsychiatric Neuroimaging Division Department of Psychiatry Massachusetts General Hospital Harvard Medical School Boston Massachusetts USA.
Nasim MalekiPsychiatric Neuroimaging Division Department of Psychiatry Massachusetts General Hospital Harvard Medical School Boston Massachusetts USA.

Funding

Identifying Markers of Chronic Pain Development in AlcoholismK01AA027833 · NIAAA · MASSACHUSETTS GENERAL HOSPITAL · PI MALEKI, NASIM · 2020 to 2024
$911k
NIAAA NIH HHS K01 AA027833
6 · The paper itself

Abstract

Objective: Exposure to alcohol-related content is unavoidable on social media. In this study level of exposure to alcohol marketing content on Twitter and Instagram for those in recovery was examined, and the effectiveness of privacy settings to mitigate exposing content was assessed. Methods: Four fictitious accounts were created on Instagram and Twitter in a case-control design in Spring 2022. All accounts followed 19 alcohol brands, with two accounts (1 male, 1 female) following only alcohol brand accounts while the other two (1 male, 1 female) additionally followed recovery resources. Four weeks of data collection were done with default privacy and advertisement settings, followed by two additional weeks of data collection with advertisement and privacy settings for blocking alcohol content. Results: Privacy settings did not help with blocking the alcoholic brand-related photographic content on either platform, meaning that the accounts received the same amount of alcohol-related content from the accounts they were following with and without changing the privacy settings. However, Twitter algorithms were more effective in suppressing alcohol-related content for accounts following recovery resources and alcohol brands. Conclusions: These results suggest that risks of exposure to triggering/cueing photographic alcohol content may outweigh the benefits of social media for social connection if an individual is considering seeking recovery.

Identifiers

PMID37711755
PMCPMC10499190

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.