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 U.S. 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 U.S. politics.

The Hollywood Blacklist was based on a series of interviews conducted by the House Committee on Un-American Activities (HUAC), trying to identify members of the communist party. We use various NLP algorithms in order to automatically analyze a large corpus of interview transcripts and construct a network of the industry members and their "naming" relations. We further use algorithms for Sentiment Analysis in order to add a psychological dimension to the edges in the network. In particular, we test how different types of connections are manifested by different sentiment types and attitude of the interviewees. Analysis of the language used in the hearings can shed new light on the motivation and role of network members.