| Abstract: | Recent work has applied the Narrative Policy Framework (NPF) to examine narrative strategies in policy debates on social media platforms. We contribute to the literature by applying the NPF to fracking policy debates in New York using well-established Natural Language Processing tools, including sentiment analysis. We combine this computational approach with a qualitative hand-coding of pro- and antifracking Twitter influentials. This approach allows us to consider a much larger corpus of tweets over a much longer time frame than has been done thus far. We adapt and test NPF propositions related to the use of the devil/angel shift strategies before and after a major state-wide policy change, that is, a state-wide moratorium on high volume hydraulic fracturing or fracking. Overall, we find evidence for the use of the devil shift narrative strategy by the pro-fracking coalition aimed at the Governor prior to the moratorium. After the moratorium, the relative percentage of Tweets containing devil shift sentiments decreases as the pro-fracking coalition generally downshifts in its use of angel shift language without a corresponding increase in devil shift language, whereas, conversely, the anti-fracking coalition generally downshifts in its use of devil shift language without a general increase in angel shift language. When we shifted our analysis to Tweets containing fracking and the Governor, we found a similar postban decrease in devil shift language among anti-fracking users. Our findings offer lessons for using computational tools in the NPF as an approach to expand analytic ability and for the operationalization of concepts such as narrative strategies and policy entrepreneurs. |
