The Spread of Fake News on Social Media: A Comparative Study of Kazakhstani and Chinese Content

Abstract

This study analyzes seven representative fake news incidents related to public safety rumors and crisis-related misinformation in Kazakhstan and China to examine the overall logic of how misinformation is generated and disseminated on social media. Rather than treating it as an isolated phenomenon, the study approaches fake news as a structurally embedded process shaped by information vacuums, trust within familiar networks, algorithmic amplification of emotional content, and users' instinctive responses to uncertainty. Using a qualitative research design and content analysis, this study draws on texts, screenshots, and short videos from platforms including Telegram, WhatsApp, Weibo, and Douyin to trace how fake news evolves through cross-platform transmission and reprocessing. The findings show that, despite differences in platform ecosystems, fake news follows a consistent multi-stage dissemination pattern. It originates in private spaces then gains amplification on semi public platforms, undergoes emotional rewriting within short video environments and also exploits delays in official information release to keep spreading. Through continuous reprocessing misinformation gradually gets features of authenticity, making timely correction more harder. Based on these findings this study proposes a transnational governance approach which emphasizes improving platform transparency, timely institutional communication and also users judgement under conditions of uncertainty. It argues that combating fake news requires not only limiting its spread but also stabilizing the digital environment by reducing information vacuums, improving platform alert systems and making users easier access to reliable information. This study helps better understand how misinformation works in digital media environments and also gives some practical ideas for responding to fake news across platforms institutions and user communities.