Evidence map›Paper›PMID 41843606›Full record

ArticlePloS one2026

Identity interweaving, act boundaries, illusion and reality interweaving: A study of visual narratives of scientists and citizen scientists through AI.

Yi He, Xiaoxia Jian, Weifeng Zhang, Xin Jin

Abstract read
In one paragraph

Article in PloS one, 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.

Yi HeSchool of Journalism and Media, Chongqing Normal University, Chongqing, China.ORCID https://orcid.org/0000-0003-4511-5461
Xiaoxia JianSchool of Journalism and Media, Chongqing Normal University, Chongqing, China.
Weifeng ZhangSchool of Journalism and Communication, Beijing Normal University, Beijing, China.ORCID https://orcid.org/0009-0000-5170-8306
Xin JinSchool of Journalism and Media, Chongqing Normal University, Chongqing, China.ORCID https://orcid.org/0000-0001-5735-2769

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rise of generative artificial intelligence (AI) is transforming human-computer interaction, reshaping communication methods and altering public perceptions of science. This shift challenges traditional scientific authority, especially as citizen science gains prominence. While research on scientific rhetoric has focused on qualitative analyses in media, little attention has been given to how AI influences visual rhetorical narratives in science. This study employs computer visual analysis and quantitative rhetorical difference analysis to explore the intersection of AI and science through Visual Narrative Theory.It investigates the rhetorical differences between scientists and citizen scientists across three dimensions: narrate, act, and resonate. Findings reveal that both groups embody a mixed rhetoric of authority and proximity in the narrative dimension. In the act dimension, AI depicts scientists in professional roles while showing citizen scientists in practical roles, portraying scientists as "flowers in the greenhouse". In the resonate dimension, scientists' narratives often feature surreal elements, while citizen scientists present more everyday narratives.This analysis, utilizing computer vision and quantitative methods, offers a fresh perspective on the image of science in the AI era and suggests strategies for enhancing science communication and building trust in science using generative AI.

Indexed as

Artificial IntelligenceCitizen ScienceNarrationResearch PersonnelCommunicationGenerative Artificial IntelligenceHumans

Identifiers

PMID41843606
PMCPMC12994824

What OpenQuestion holds

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LicenceCC BY
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.