Evidence map›Paper›PMID 41501075›Full record

ArticleNature communications2026

Historical reconstruction of human moralization with word association and text corpora.

Aida Ramezani, Jennifer E Stellar, Matthew Feinberg, Yang Xu

Abstract readHistorical Article
In one paragraph

Article in Nature communications, 2026. 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

4 authors.

Aida RamezaniDepartment of Computer Science, University of Toronto, Toronto, ON, Canada. armzn@cs.toronto.edu.ORCID http://orcid.org/0009-0009-4793-6221
Jennifer E StellarDepartment of Psychological & Brain Sciences, University of Toronto, Toronto, ON, Canada.
Matthew FeinbergRotman School of Management, University of Toronto, Toronto, ON, Canada.
Yang XuDepartment of Computer Science, University of Toronto, Toronto, ON, Canada.ORCID http://orcid.org/0000-0002-8825-7497

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Moralization, the process by which concepts and practices gain moral attributes, plays a pivotal role in shaping individual and social behaviour. However, research on how moralization unfolds over time remains limited. We present HistMoral, an open-access computational framework based on human word association, historical text corpora, and graph neural networks that enables scalable, retrospective analysis of moral trajectories of many different concepts. We apply our framework to analyze the moral time courses of over 20,000 concepts within the Corpus of Historical American English over the past 150 years, as well as within the New York Times annotated corpus from 1987 to 2007. Our findings provide robust evidence of moralization across diverse categories, from diseases to world leaders, and identify moralization around economic-political shifts of recent decades.

Indexed as

MoralsGraph Neural NetworksHistory, 20th CenturyHistory, 21st CenturyHumans

Identifiers

PMID41501075
PMCPMC13068938

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.