Evidence map›Paper›PMID 39290438›Full record

ArticlePNAS nexus2024

National politics ignites more talk of morality and power than local politics.

Danica Dillion, Curtis Puryear, Longjiao Li, Andre Chiquito, Kurt Gray

Abstract read
In one paragraph

Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Danica DillionDepartment of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.ORCID https://orcid.org/0000-0002-8549-0058
Curtis PuryearDepartment of Management and Organizations, Kellogg School of Management, Northwestern University, Evanston, IL 60208, USA.ORCID https://orcid.org/0000-0001-6067-4654
Longjiao LiRotterdam School of Management, Erasmus University, Rotterdam, South Holland 3062 PA, Netherlands.ORCID https://orcid.org/0000-0002-5311-3573
Andre ChiquitoDepartment of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.ORCID https://orcid.org/0009-0008-0523-1699
Kurt GrayDepartment of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.ORCID https://orcid.org/0000-0001-5816-2676

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Politics and the media in the United States are increasingly nationalized, and this changes how we talk about politics. Instead of reading the local news and discussing local events, people are more often consuming national media and discussing national issues. Unlike local politics, which can rely on shared concrete knowledge about the region, national politics must coordinate large groups of people with little in common. To provide this coordination, we find that national-level political discussions rely upon different themes than local-level discussions, using more abstract, moralized, and power-centric language. The higher prevalence of abstract, moralized, and power-centric language in national vs. local politics was found in political speeches, politician Tweets, and Reddit discussions. These national-level linguistic features lead to broader engagement with political messages, but they also foster more anger and negativity. These findings suggest that the nationalization of politics and the media may contribute to rising partisan animosity.

Indexed as

languagemoralitypolitical polarizationpoliticspsychology

Identifiers

PMID39290438
PMCPMC11407281

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.