The Spiral of Silence theory posits that individuals who hold controversial or unpopular views often hesitate to express their opinions publicly. In modern online social networks, social bots have emerged as influential agents, mimicking human behavior and exerting a profound impact on public opinion dynamics. Consequently, the detection of social bots and the study of their effects on opinion formation have become increasingly critical research areas. In this paper, we propose Silent Spiral Sparse Graph Transformer (SSSGT), a novel and scalable method for detecting social bots and simulating opinion dynamics in complex social networks. Addressing key limitations of existing bots detection methods, SSSGT incorporates innovative graph sparsification techniques that reduce graph complexity while retaining essential structural properties. To further enhance the detection of social bots and simulate opinion dynamics, we propose Sparse Time Attention mechanism, enabling efficient tracking of temporal interactions in sparse graphs. Additionally, we present Hyper Kernel Attention layer for graph transformers, which improves computational efficiency without compromising performance. Extensive experiments conducted across multiple benchmark datasets demonstrate that SSSGT consistently achieves competitive results.