We introduce a formal framework for recognizing manipulation in human-agent interactions, where one agent gradually influences another's beliefs. To this end, we extend Quantitative Bipolar Argumentation Frameworks (QBAFs) by incorporating agents' beliefs about arguments, attacks, and supports, forming QBAF with Belief (QBAFB). By defining axioms of belief change and integrating QBAFB into dialogue games, we establish conditions for manipulation-belief change, concealment, and intent-where strategies are shaped by (dis)honesty. The framework generates belief state trajectories, serving as explanations for manipulation.