How Students Evaluate Fake News and AI-Generated Content on Social Media: Insights from Hong Kong Post-Secondary Students

Social media has become a primary news source for post-secondary students in Hong Kong; however, there is substantial disinformation and misinformation on these platforms. This study offers an initial qualitative window into how Hong Kong post-secondary students identify and respond to online disinformation and misinformation on social media. The qualitative interviews were conducted based on the traditional “Content–Appearance–Motivation” (CAM) framework. The findings show that students actively draw on common-sense reasoning and CAM-related cues. The study proposes a provisional Contextualized Dual-Loop Verification Model in which traditional CAM assessment is embedded within a broader loop of platform literacy, technical authenticity awareness, perceived risk and efficacy, and metacognitive regulation, highlighting the need for journalism and media education to move beyond conventional information literacy toward AI-era verification competencies. Future large-scale and cross-cultural studies are needed to test and refine this model.