Evidence map›Paper›PMID 41164457›Full record

ArticleTranslational animal science2025

Quantifying interest in and sentiment of online media about greenhouse gas emissions from cattle production in the United States.

Michael L Smith, Jinho Jung, Nicole Olynk Widmar, Danielle J Ufer, Maria Berikou, Jayson Lusk

Abstract read
In one paragraph

Article in Translational animal science, 2025. 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. 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

6 authors.

Michael L SmithPurdue University College of Agriculture, Department of Agricultural Economics, West Lafayette IN 47906, USA.
Jinho JungPurdue University College of Agriculture, Department of Agricultural Economics, West Lafayette IN 47906, USA.
Nicole Olynk WidmarPurdue University College of Agriculture, Department of Agricultural Economics, West Lafayette IN 47906, USA.ORCID https://orcid.org/0000-0002-6574-5295
Danielle J UferFormer Research Agricultural Economist with the USDA Economic Research Service, Washington DC 20250, USA.
Maria BerikouPurdue University College of Agriculture, Department of Agricultural Economics, West Lafayette IN 47906, USA.
Jayson LuskOklahoma State University College of Agriculture, Department of Agricultural Economics, Stillwater, OK 74078, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The public is paying increased attention to the environmental impacts associated with cattle production systems in the US. Thus, cattle producers are increasingly exploring ways to communicate their efforts to reduce those environmental and climate impacts. This study uses online and social media data from 2018 to 2022 to examine public interest in and sentiment about beef and dairy cattle production when discussed within the context of greenhouse gas (GHG) emissions. Variation in mentions and net sentiment over the reporting period is quantified and analyzed. Over the period of data collection, which was interrupted by the COVID-19 pandemic, we find that consumers became increasingly aware of cattle production's role in GHG emissions and the relationship between emissions and climate change. We also find that consumers have become increasingly interested in emissions mitigation strategies and sustainable practices employed in the production of beef and dairy products.

Indexed as

big datacarbonmarketsonline datapublic perceptionssocial media

Identifiers

PMID41164457
PMCPMC12559853

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.