Social media platforms, particularly Telegram, play a pivotal role in shaping public perceptions and opinions on global and national issues. Unlike traditional news media, Telegram allows for the proliferation of user-generated content with minimal oversight, making it a significant venue for the spread of controversial and misinformative content. During the COVID-19 pandemic, Telegram's popularity surged in Singapore, a country with one of the highest rates of social media use globally. We leverage Singapore-based Telegram data to analyze information flows within groups focused on COVID-19 and climate change. Using k-means clustering, we identified distinct user archetypes, including Skeptic, Engaged Advocate, Observer, and Analyst, each contributing uniquely to the discourse. We developed a model to classify users into these clusters (Precision: Climate change: 0.99; COVID-19: 0.95). By identifying these user archetypes and examining their contributions to information dissemination, we sought to uncover patterns to inform effective strategies for combating misinformation and enhancing public discourse on pressing global issues.

翻译：社交媒体平台，特别是Telegram，在塑造公众对全球与国家议题的认知和观点方面发挥着关键作用。与传统新闻媒体不同，Telegram允许用户生成内容在极少监管的情况下大量传播，这使其成为争议性信息和误导性内容扩散的重要场所。在COVID-19大流行期间，Telegram在新加坡的使用率急剧上升——该国是全球社交媒体使用率最高的国家之一。我们利用基于新加坡的Telegram数据，分析了聚焦于COVID-19和气候变化的群组内部信息流动。通过k-means聚类方法，我们识别出四种不同的用户原型：怀疑者、积极参与的倡导者、观察者和分析者，每一类用户都在讨论中发挥着独特作用。我们开发了一个模型，可将用户分类至这些聚类（精确率：气候变化：0.99；COVID-19：0.95）。通过识别这些用户原型并考察其对信息传播的贡献，我们旨在揭示相关模式，从而为制定有效策略以应对错误信息、提升关于紧迫全球议题的公共讨论质量提供参考。