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Framing is a central concept in political communication and a powerful political tool. Thus, it is hugely important to understand: a) what frames are used to define specific issues, b) what general patterns are evidenced by the evolution of frames over time, and c) how frames diffuse, or spread, across policy areas and venues. These tasks also pose a serious challenge, thanks to the volume of text data, the dynamic nature of language, and the variance in applicable frames across issues (e.g., the ‘innocence’ frame of the death penalty debate is not used in discussing smoking bans). We describe a project that advances framing research methodology in two ways. First, we present a unified coding scheme for content analysis across issues, whereby issue-specific frames (e.g., innocence) are nested within high-level dimensions (or frame types) that cross-cut issues (e.g., fairness). We call this the “Policy Frames Codebook” with an eye toward doing for frame categorization across issues what the Policy Agendas Codebook has done for issue categorization across agendas. Second, we validate the policy frames coding scheme by applying it to news coverage of three issues—smoking, immigration, and same-sex marriage—in the United States over a twenty-two year period. This pilot dataset is the first cut from a larger data collection effort that will eventually span news coverage of five policy issues over several decades. Using this data, our long-term aim is to identify and assess empirical patterns in which frames tend to get selected across policy debates in the United States, how frames within policy debates tend to evolve, and the conditions under which frames spread from one issue to the next and/or across policy venues (e.g., between states, or from the media to Congress). Toward this aim, we employ strategies heavily informed by existing work in natural language processing, but tailored to the specific needs and professional sensibilities of framing and policy scholars.
Boydstun et al. (Fri,) studied this question.