Evidence map›Paper›PMID 39440021›Full record

ArticlePNAS nexus2024

Quantifying the uniqueness and divisiveness of presidential discourse.

Karen Zhou, Alexander A Meitus, Milo Chase, Grace Wang, Anne Mykland, William Howell, Chenhao Tan

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

7 authors.

Karen ZhouDepartment of Computer Science, The University of Chicago, 5730 S Ellis Ave, Chicago, IL 60637, USA.ORCID https://orcid.org/0000-0002-7147-0590
Alexander A MeitusDepartment of Computer Science, The University of Chicago, 5730 S Ellis Ave, Chicago, IL 60637, USA.
Milo ChaseDepartment of Computer Science, The University of Chicago, 5730 S Ellis Ave, Chicago, IL 60637, USA.ORCID https://orcid.org/0009-0004-1222-3604
Grace WangDepartment of Computer Science, The University of Chicago, 5730 S Ellis Ave, Chicago, IL 60637, USA.
Anne MyklandHarvard University, Massachusetts Hall, Cambridge, MA 02318, USA.
William HowellHarris School of Public Policy, The University of Chicago, 5730 S Ellis Ave, Chicago, IL 60637, USA.ORCID https://orcid.org/0000-0003-1733-2127
Chenhao TanDepartment of Computer Science, The University of Chicago, 5730 S Ellis Ave, Chicago, IL 60637, USA.ORCID https://orcid.org/0000-0002-3981-2116

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Do American presidents speak discernibly different from each other? If so, in what ways? And are these differences confined to any single medium of communication? To investigate these questions, this paper introduces a novel metric of uniqueness based on large language models, develops a new lexicon for divisive speech, and presents a framework for assessing the distinctive ways in which presidents speak about their political opponents. Applying these tools to a variety of corpora of presidential speeches, we find considerable evidence that Donald Trump's speech patterns diverge from those of all major party nominees for the presidency in recent history. Trump is significantly more distinctive than his fellow Republicans, whose uniqueness values appear closer to those of the Democrats. Contributing to these differences is Trump's employment of divisive and antagonistic language, particularly when targeting his political opponents. These differences hold across a variety of measurement strategies, arise on both the campaign trail and in official presidential addresses, and do not appear to be an artifact of secular changes in presidential communications.

Indexed as

divisive word lexiconDonald Trumplarge language modelspresidential speechuniqueness metrics

Identifiers

PMID39440021
PMCPMC11495212

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.