How Does Deep Synthesis Content Erode Trust in Social Media? — An Analysis from the Dimensions of Platform, Information, and Users Based on a Survey Experiment
Year: 2025
Type: article
Abstract: Deep-synthesis technology—an artificial intelligence technique used primarily for audiovisual content—has been misused in ways that are systematically deconstructing the trust ecosystem of social media. This study innovatively unpacks social media trust into three dimensions (platform trust, information trust, and user trust) and, using a survey-experiment design (n = 522), examines the differentiated erosive effects of deep-synthesis content within social media platforms. Taking Douyin (the Chinese version of TikTok) as the context, we employ five categories of deep-synthesis videos as stimuli and control for topic-related confounds. The findings show that deep-synthesis content exerts a negative impact on social media trust, with information trust being the most severely damaged, followed by platform trust; user/interpersonal trust exhibits a lagging and comparatively weak effect. These results reveal differentiated pathways through which technological alienation disrupts trust mechanisms and provide a theoretical basis for platform governance and user-level cognitive interventions.
Authors: Xinhui Wu, Sheng Lin
Institution: Hangzhou Dianzi University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4414145680