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Seeing the Message but Not the Machine: Digital Skepticism and AI Discernment in Online Information Environments.

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Bibliographic Details
Title: Seeing the Message but Not the Machine: Digital Skepticism and AI Discernment in Online Information Environments.
Authors: Phothong, Lersak1 (AUTHOR), Sukprasert, Anupong1 (AUTHOR) anupong.s@acc.msu.ac.th, Shutimarrungson, Nattakarn1 (AUTHOR), Obthong, Mehtabhorn1 (AUTHOR)
Source: Information. Mar2026, Vol. 17 Issue 3, p295. 31p.
Subject Terms: *Artificial intelligence, *Internet forums, *Electronic information resources, *Social media, Skepticism
Abstract: Artificial intelligence (AI) increasingly mediates how information is generated, ranked, and circulated in digital environments. However, it remains unclear under what conditions users explicitly articulate recognition of AI involvement in routine news-related discourse. This study examines how digital skepticism and AI-related discernment are expressed in naturally occurring social media discussions. Using an exploratory observational design, 6065 user-generated comments from 305 news-related Reddit threads were analyzed through a rule-based framework distinguishing general skepticism, structural suspicion, and explicit AI-related discernment. Within the sampled corpus, generalized digital skepticism is proportionally more visible than explicit attribution to AI-generated or synthetically produced content. Explicit AI-related attribution is unevenly distributed across discourse contexts, appearing more frequently in technology-oriented communities and remaining limited in mainstream news-related discussions. Differences across score-based visibility contexts do not correspond to a consistently higher representation of explicit AI attribution. These findings indicate a distributional difference between generalized skepticism and publicly articulated recognition of AI mediation. Rather than measuring levels of awareness, the results illuminate the contextual and linguistic conditions under which AI involvement becomes explicitly named in public interaction. By focusing on observable discourse rather than self-reported attitudes, the study provides a corpus-bound account of when AI mediation becomes discursively articulated in algorithmically mediated environments. [ABSTRACT FROM AUTHOR]
Database: Library, Information Science & Technology Abstracts
Description
Abstract:Artificial intelligence (AI) increasingly mediates how information is generated, ranked, and circulated in digital environments. However, it remains unclear under what conditions users explicitly articulate recognition of AI involvement in routine news-related discourse. This study examines how digital skepticism and AI-related discernment are expressed in naturally occurring social media discussions. Using an exploratory observational design, 6065 user-generated comments from 305 news-related Reddit threads were analyzed through a rule-based framework distinguishing general skepticism, structural suspicion, and explicit AI-related discernment. Within the sampled corpus, generalized digital skepticism is proportionally more visible than explicit attribution to AI-generated or synthetically produced content. Explicit AI-related attribution is unevenly distributed across discourse contexts, appearing more frequently in technology-oriented communities and remaining limited in mainstream news-related discussions. Differences across score-based visibility contexts do not correspond to a consistently higher representation of explicit AI attribution. These findings indicate a distributional difference between generalized skepticism and publicly articulated recognition of AI mediation. Rather than measuring levels of awareness, the results illuminate the contextual and linguistic conditions under which AI involvement becomes explicitly named in public interaction. By focusing on observable discourse rather than self-reported attitudes, the study provides a corpus-bound account of when AI mediation becomes discursively articulated in algorithmically mediated environments. [ABSTRACT FROM AUTHOR]
ISSN:20782489
DOI:10.3390/info17030295