ArticleThe Journal of communication2019
Toward an Aggregate, Implicit, and Dynamic Model of Norm Formation: Capturing Large-Scale Media Representations of Dynamic Descriptive Norms Through Automated and Crowdsourced Content Analysis.
Article in The Journal of communication, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed.
- Comparative research on the influence of incidental exposure to negative fertility-related information on Chinese nulliparous women's fertility intention.Scientific reports · 2026Article
- AI for Tobacco Control: Identifying Tobacco-Promoting Social Media Content Using Large Language Models.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2025Article
- From digital traces to public vaccination behaviors: leveraging large language models for big data classification.Frontiers in artificial intelligence · 2025Article
- Not all norm information is the same: Effects of normative content in the media on young people's perceptions of e-cigarette and tobacco use norms.Communication research · 2024Article
- Using Exogenous Social Media Exposure Measures to Assess the Effects of Smokeless Tobacco-Related Social Media Content on Smokeless Tobacco Sales in the United States.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024Article
- The Effects of Tobacco Coverage in the Public Communication Environment on Young People's Decisions to Smoke Combustible Cigarettes.The Journal of communication · 2022Article
- Marijuana Content on Digital Media and Marijuana Use among Young People in the United States.Cannabis (Albuquerque, N.M.) · 2022Article
- Disclosure Standards for Social Media and Generative Artificial Intelligence Research: Toward Transparency and Replicability.Social media + societyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Media content can shape people's descriptive norm perceptions by presenting either population-level prevalence information or descriptions of individuals' behaviors. Supervised machine learning and crowdsourcing can be combined to answer new, theoretical questions about the ways in which normative perceptions form and evolve through repeated, incidental exposure to normative mentions emanating from the media environment. Applying these methods, this study describes tobacco and e-cigarette norm prevalence and trends over 37 months through an examination of a census of 135,764 long-form media texts, 12,262 popular YouTube videos, and 75,322,911 tweets. Long-form texts mentioned tobacco population norms (4-5%) proportionately less often than e-cigarette population norms (20%). Individual use norms were common across sources, particularly YouTube (tobacco long-form: 34%; Twitter: 33%; YouTube: 88%; e-cigarette long form: 17%; Twitter: 16%; YouTube: 96%). The capacity to capture aggregated prevalence and temporal dynamics of normative media content permits asking population-level media effects questions that would otherwise be infeasible to address.
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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.