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Framing is a sophisticated form of discourse in which the speaker tries to induce a cognitive bias through consistent linkage between a topic and a specific context (frame). We build on political science and communication theory and use probabilistic topic models combined with time series regression analysis (autoregressive distributed-lag models) to gain insights about the language dynamics in the political processes. Processing four years of public statements issued by members of the US Congress, our results provide a glimpse into the complex dynamic processes of framing, attention shifts and agenda setting, commonly known as ‘spin’. We further provide new evidence for the divergence in party discipline in US politics.