A digital communication model based on audience trust in the era of social media algorithms

Abstract

This study aims to formulate a model of digital communication based on audience trust in the era of social media algorithms, by examining how audiences interpret message credibility, communicator authenticity, content visibility, and the information verification process within a personalized digital environment. This study employs a qualitative approach using an interpretive phenomenological design. Data were collected through semi-structured interviews, non-participatory digital observation, and digital documentation from 15–25 purposively selected informants—active social media users with experience consuming, evaluating, or responding to algorithmically recommended content. Data analysis was conducted thematically to identify patterns of meaning emerging from participants’ experiences. The research findings indicate that audience trust is formed through five main components: source credibility, value alignment, communication transparency, algorithmic awareness, and participatory verification. The audience is not entirely passive in receiving content that appears on their timeline but negotiates trust through personal experiences, reading comments, comparing sources, and assessing the authenticity of communicators. This study concludes that trust in digital communication is relational-algorithmic because it is shaped by interactions between communicators, audiences, digital communities, and platform logic. The implications of this research underscore the importance of transparent, ethical, and contextual communication strategies, as well as the strengthening of digital literacy and algorithmic literacy. Further research is recommended to compare audience experiences across platforms, age groups, and types of issues.