aceThe Impact of AI-Generated Content on Trust and Authenticity in Social Media Communication: A Study of Three Australian Case Studies
The robust growth of generative artificial intelligence (AI), especially large language models and image-generation systems, has greatly shaped communication practices on social media. In this context, AI-generated content has become widely adopted by businesses, media organizations, influencers, and individual users to efficiently create text, images, videos, and interactive content. While these technologies boost creativity and communication productivity, they also introduce ethical concerns regarding transpar…
AI-generated content disrupts how audiences judge authenticity, raising concerns over transparency, misinformation and public trust, as seen in three Australian controversies.
The study is a qualitative interpretive analysis of only three cases using secondary sources, so its conclusions are interpretive rather than measured and may not generalize.
Evidence
- Peer-reviewedCommunications in Humanities Research2026-10-20
How should this claim be treated?
Truvace Impact Record TRV-2026-2132, v1: “The Impact of AI-Generated Content on Trust and Authenticity in Social Media Communication: A Study of Three Australian Case Studies.” Truvace, 2026-10-21. /record/TRV-2026-2132 (accessed at citation time). sha256 2c69b70d6cd19492…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-2132 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.