This project therefore develops new computational modeling methods, grounded in data-driven computational linguistics, aimed at improving the scientific understanding of how issues are framed by political elites, the media, and the public. This collaboration between political scientists and computer scientists has four goals: (a) developing novel methods for semi-automated frame discovery, whereby computational models guided by political scientists' expert knowledge speed up and augment their analytical process; (b) developing novel algorithms based on natural language processing for automatic frame analysis, producing measurably accurate results comparable with reliable human coders; (c) establishing the validity of these processes on well-understood cases; and (d) applying these methods to several current policy issues, using data across years and across traditional and social media streams. The resulting evolutionary framing data will help unpack the mechanisms of framing and help predict trends in public opinion and policy.