Political stance detection in understudied socio-political contexts presents a persistent challenge for language models because dynamic contexts and indirect relationships between political entities complicate the accurate alignment of opinions. To address this, we introduce GRASP-ChoQ, an approach that combines structured knowledge graphs with chain-of-questions reasoning to break down interactions in political texts. We support this with BPDisC, a novel dataset of politically charged tweets from Bangladesh during and after the July 2024 protests, along with a knowledge graph that details political entities and events. By using the knowledge graph to provide context, GRASP-ChoQ moves away from making direct predictions and instead uses intermediate reasoning steps. Experiments indicate that our proposed method yields substantial improvements relative to baseline approaches. Notably, the DeepSeek R1 variant, when integrated with GRASP-ChoQ, achieved the highest performance, demonstrating a 40% higher F1 score over zero-shot detection. As a whole, through the proposed framework, the improvement of retrieval augmentation is occurring, which facilitates the adaptive analysis of low-resource discussion of politics.