Evidence map›Paper›PMID 42422518›Full record

ArticleFrontiers in psychiatry2026

Psychological decoupling in responses to AI: emotional reactivity and behavioral discourse in digital environments.

Naciye Güliz Ugur, Ismail Bayram

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Naciye Güliz UgurDepartment of Management Information Systems, Sakarya University, Sakarya, Türkiye.
Ismail BayramDepartment of Management Information Systems, Sakarya University, Sakarya, Türkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Artificial intelligence (AI) has become increasingly visible in digitally mediated environments, raising important questions about how users express emotional reactions to perceived AI presence. While prior research has frequently examined AI acceptance, trust, and behavioral outcomes, less is known about how different forms of AI perception are reflected in large-scale user-generated discourse. This study investigates the relationship between multidimensional AI perception, emotional expression, trust-related discourse, and purchase-related discourse in naturalistic online environments. Methods: A large-scale dataset consisting of 2,326,781 cleaned YouTube comments collected from 8,947 videos and 5,176 channels was analyzed. AI perception was operationalized through six categories: automation perception, robotic communication style, scriptedness, lack of authenticity, perceived artificiality, and uncanny valley perception. The analytical pipeline combined LLM-assisted weak supervision, transformer-based classification, manual validation, sentiment analysis, and statistical association tests to examine emotional expression and discourse patterns across AI perception categories. Results: AI perception was highly prevalent throughout the dataset and was strongly associated with negative emotional expression. Comments containing AI perception cues exhibited substantially higher levels of negative sentiment than comments without such cues. Emotional expression varied meaningfully across AI perception categories, with robotic communication style, lack of authenticity, automation perception, and uncanny valley perception showing the highest levels of negative expression. In contrast, trust-related discourse and purchase-related discourse remained rare across the corpus and displayed only limited differentiation across AI perception conditions. These findings indicate that emotional expression is considerably more visible and differentiated than explicit behavioral discourse in AI-related user comments. Discussion: The findings support a pattern of expressive decoupling, whereby perceived AI presence is strongly reflected in emotional expression while explicit trust-related and purchase-related discourse remains sparse. Rather than indicating actual behavioral outcomes, the results suggest that AI-related perceptions primarily manifest as discursive emotional reactions within AI-saturated digital environments. The study contributes to digital mental health and human-AI interaction research by demonstrating how large-scale user-generated discourse can reveal psychologically meaningful patterns of emotional expression associated with perceived AI presence.

Indexed as

artificial intelligence perceptiondigital mental healthemotional expressionexpressive decouplinghuman–AI interactionsentiment analysissocial media discourseuser-generated discourse

Identifiers

PMID42422518
PMCPMC13343222

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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.