Stakeholders in the Same Bed with Different Dreams: Semantic Network Analysis of Issue Interpretation in Risk Policy Related to Mad Cow Disease
通过分析韩国官僚、公民、科学家和利益集团四类群体在疯牛病政策中的媒体关键词，用语义网络揭示他们如何解读风险议题，并检验议题注意周期的理论预测。
The “Risk Society” as Ulrich Beck (Beck, Ulrich. 1992. Risk society: Towards a new modernity. London, UK: Sage, Beck, Ulrich. 1999. World risk society. Cambridge, UK: Polity Press) has called modern society, has changed the policy dynamics of many high-risk policies. Policy conflicts occur due to both physically objective risks and socially constructed risks. Risk politicization means that issue expansion and attention cycles are likely to be related to the social construction of policy images. An interesting case in point concerns the risk perception about Bovine Spongiform Encephalopathy (BSE) among four groups of people in South Korea: bureaucrats, citizens, scientists, and interest groups. We develop expectations about how the four groups will frame the political issues based on their roles and concerns in the policy process. Downs’ (Downs, Anthony. 1972. Up and down with ecology: The issue-attention cycle. Public Interest 28:38–50) work on the issue-attention cycle suggests how issues rise and fall on the public agenda, and our work also directly tests his theses. This study uses semantic network analysis based on content analysis of key words in 6400 articles published in 2008 to construct semantic networks for the four stakeholder groups over four stages in the policy process. Through this unique data analysis, we can see the major issue concerns and intensity of each of the four groups in the four policy stages by assessing the key words each group uses in the media articles and the visualizations of their semantic associations. We do find distinct differences in what issues each of the four groups focus on and how the focus of substance changes in each of the four policy stages that clarify how these groups framed the BSE policy issues. Our results both confirm and raise doubts about some predictions from the issue-attention cycle. Citizens’ attention follows closely to Downs’ predictions, whereas the other stakeholders’ issue-attention cycles do not follow Downs’ predictions as well. Finally, our study explains and demonstrates the value of semantic network analysis as a powerful method for understanding issue framing in the policy process.