Understanding ideology in social media requires models that learn from how information propagates, not just what it contains. This paper introduces a stochastic generative model that infers multidimensional ideological embeddings by fitting the flow of politically salient content across users. By capturing alignment through diffusion dynamics rather than labels, the model learns the structural complexity of political belief systems, offering a principled framework for representing ideology in networked communication.