Evidence map›Paper›PMID 42539548›Full record

ArticleFrontiers in psychology2026

The mechanism of generative AI's construction of cultural identity: an empirical study based on Generation Z's social media behavior.

Jun Liang, Yixin Li, Zhileng Xiong

Abstract read
In one paragraph

Article in Frontiers in psychology, 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

3 authors.

Jun LiangSchool of Art, Huangshan University, Huangshan, China.
Yixin LiSchool of Literature and Digital Communication, Hunan University of Technology and Business, Changsha, China.
Zhileng XiongSchool of Literature and Digital Communication, Hunan University of Technology and Business, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative AI (AIGC) is fundamentally reshaping the landscape of digital content, yet its specific predictive relationship with the cultural identity of "digital natives" (Generation Z) remains underexplored compared to traditional User-Generated Content (UGC). While AIGC offers efficiency and aesthetic novelty, it lacks the inherent "human touch" of UGC. This study investigates this divergence by analyzing 11,628 comments from the Douyin platform. We employed a mixed-method approach combining semantic network analysis and structural equation modeling (SEM) to compare user responses across emotional, technological, and social dimensions. Our findings reveal a distinct "two-path" mechanism: while UGC fosters identity through a "Warm Path" of emotional resonance, AIGC is structurally associated with a significantly more robust identity profile via a "Cool Path" predicted by technical curiosity and exclusive topic circling. Crucially, we identify an "Authenticity Paradox": the lack of traditional human touch in AIGC does not alienate Generation Z; rather, the resulting "mindful friction"-quantified by a significant negative path effect between sentiment and identity

Indexed as

cultural identityGeneration Zgenerative AIsemantic network analysissocial mediastructural equation modelingtechnology acceptance

Identifiers

PMID42539548
PMCPMC13423852

What OpenQuestion holds

Textmetadata
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.