Conspiratorial discourse is increasingly embedded within digital communication ecosystems, yet its structure and spread remain difficult to study. This work analyzes conspiratorial narratives in Singapore-based Telegram groups, showing that such content is woven into everyday discussions rather than confined to isolated echo chambers. We propose a two-stage computational framework. First, we fine-tune RoBERTa-large to classify messages as conspiratorial or not, achieving an F1-score of 0.866 on 2,000 expert-labeled messages. Second, we build a signed belief graph in which nodes represent messages and edge signs reflect alignment in belief labels, weighted by textual similarity. We introduce a Signed Belief Graph Neural Network (SiBeGNN) that uses a Sign Disentanglement Loss to learn embeddings that separate ideological alignment from stylistic features. Using hierarchical clustering on these embeddings, we identify seven narrative archetypes across 553,648 messages: legal topics, medical concerns, media discussions, finance, contradictions in authority, group moderation, and general chat. SiBeGNN yields stronger clustering quality (cDBI = 8.38) than baseline methods (13.60 to 67.27), supported by 88 percent inter-rater agreement in expert evaluations. Our analysis shows that conspiratorial messages appear not only in clusters focused on skepticism or distrust, but also within routine discussions of finance, law, and everyday matters. These findings challenge common assumptions about online radicalization by demonstrating that conspiratorial discourse operates within ordinary social interaction. The proposed framework advances computational methods for belief-driven discourse analysis and offers applications for stance detection, political communication studies, and content moderation policy.

翻译：阴谋论话语日益嵌入数字通信生态系统，但其结构与传播机制仍难以研究。本文分析了新加坡Telegram群组中的阴谋论叙事，发现此类内容被编织进日常讨论而非局限于孤立的回音室。我们提出了一个两阶段计算框架：首先，我们微调RoBERTa-large模型以识别消息是否包含阴谋论内容，在2000条专家标注消息上达到0.866的F1分数；其次，我们构建带符号的信念图谱，其中节点代表消息，边符号反映信念标签的一致性，边权由文本相似度加权。我们提出带符号信念图神经网络（SiBeGNN），采用符号解缠损失学习能够区分意识形态对齐与风格特征的嵌入表示。通过对553,648条消息的嵌入进行层次聚类，我们识别出七种叙事原型：法律议题、医疗关切、媒体讨论、金融话题、权威矛盾、群组管理及日常聊天。SiBeGNN的聚类质量（cDBI = 8.38）显著优于基线方法（13.60至67.27），专家评估者间一致性达88%。分析表明，阴谋论消息不仅出现在聚焦怀疑或不信任的集群中，也存在于金融、法律及日常事务的常规讨论中。这些发现通过证明阴谋论话语在普通社会互动中运作，挑战了关于网络激进化的常见假设。本框架推动了信念驱动话语分析的计算方法，为立场检测、政治传播研究和内容审核政策提供了应用前景。