Narrative strategies for emerging disruptive technologies: A case study of Blockchain for Europe

Kanol, Direnç

doi:10.1111/polp.12492

Cited by 2 publications

(2 citation statements)

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“…In particular, data science tools, such as topic modeling, hold promise in helping researchers elucidate themes or narrative strategies among and between policy cohorts. In addition, expanding this technique to other policy contexts might uncover how different scenarios encourage distinct narrative strategies (Kanol, 2022).…”

Section: Discussion/conclusionmentioning

confidence: 99%

Fracking Twitter: Utilizing machine learning and natural language processing tools for identifying coalition and causal narratives

Pattison,

Cipolli,

Marichal

et al. 2023

Politics & Policy

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The Narrative Policy Framework (NPF) has provided policy scholars with a valuable method to gain empirical insight into the power of narratives in the policy process. However, a significant limitation of the NPF has been its ability to deploy this framework on large N datasets due to the labor‐intensive nature of collecting narrative data. In recent years, NPF scholars have turned to computational social science tools to address this challenge. This study builds upon this emerging body of literature and our previous work, which uses sentiment analysis, a natural language processing technique, to evaluate the use of the angel/devil shift across coalitions before and after a major policy change. We examined Tweets that included the terms “fracking” and “New York” before and after the introduction of a moratorium. While sentiment analysis allowed us to gain insight into the narrative structure of the fracking policy discourse space, the labor involved in hand‐coding Twitter users into neutral‐, pro‐, or anti‐fracking groups was onerous. This project aims to supplement our natural language processing method by employing supervised machine learning techniques to increase the universe of respondents. We hand‐coded 500 Twitter users into neutral‐, pro‐, or anti‐fracking groups and trained a much larger dataset using an extreme gradient boost algorithm to classify a broader corpus of Tweets. This enabled us to expand the number of Tweets used in the analyses. We then applied sentiment analysis on this newly classified larger dataset to reveal differences in the pro‐fracking and anti‐fracking advocacy coalitions. By using machine learning to classify pro and con Tweets, we gained the ability to achieve significantly greater insight into how these two subgroups employed different narrative and linguistic devices in their Twitter discussions about fracking.Related ArticlesMerry, Melissa K. 2022. “Trump's Tweets as Policy Narratives: Constructing the Immigration Issue via Social Media.” Politics & Policy 50(4): 752–72. https://doi.org/10.1111/polp.12487.Robles, Pedro, and Daniel J. Mallinson. 2023. “Catching Up with AI: Pushing toward a Cohesive Governance Framework.” Politics & Policy 51(3): 355–72. https://doi.org/10.1111/polp.12529.Shanahan, Elizabeth A., Mark K. McBeth, and Paul L. Hathaway. 2011. “Narrative Policy Framework: The Influence of Media Policy Narratives on Public Opinion.” Politics & Policy 39(3): 373–400. https://doi.org/10.1111/j.1747‐1346.2011.00295.x.

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Section: Discussion/conclusionmentioning

confidence: 99%

Fracking Twitter: Utilizing machine learning and natural language processing tools for identifying coalition and causal narratives

Pattison,

Cipolli,

Marichal

et al. 2023

Politics & Policy

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“…Focusing on trust in institutions, Aguado (2022) uses perceptions of President Trump to explore the effects of social media on beliefs of electoral legitimacy, adding to P&P's recent articles examining Trump and Twitter (see e.g., Garrett, 2020; Merry, 2022). Staying with social media, but using a Narrative Policy Framework lens, Kanol (2022) studies how interest groups advocating for emerging disruptive technologies—in this case, the Twitter messages by Blockchain for Europe—use narrative strategies to influence policy (cf. Merry, 2022; Boscarino, 2022; Brekken & Fenley, 2021).…”

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Note from the Editors

Norman¹,

Alemán²

2022

Politics & Policy

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In her comparative international study of local initiatives in three continents, Ruiz-Campillo's (2022) article contributes strongly to research on policy diffusion regarding climate action and sustainable development. Turning to issues of the EU democratic deficit, Ghibellini (2022) argues that, before attempting to enhance the bonding power of EU citizenship, the Union first needs to strengthen its still-weak political foundations in the wake of post-pandemic health care, economic, and populistic/ nationalistic challenges in the region.Our next two articles focus on Africa. Onyango and Ondiek (2022) compare COVID-19 policy responses in Kenya and South Africa and show how they led to increased open innovation strategies and enhanced government use of technology in both cases (cf. regarding China, Liu & Zhu, 2021). Iwuoha and others (2022) critically examine the problems involved in Nigeria's public-private partnership established to regulate its maritime ports. And finally, Falkenström and Svallfors (2022) study knowledge-driven management in Swedish health-care governance and the risks implicated in its implementation for professional learning (see also Svallfors et al., 2022).We hope you enjoy the excellent original research articles in this issue of P&P and, as always, we look forward to your continued article submissions at https://mc.manuscriptcentral.com/polpol.

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Narrative strategies for emerging disruptive technologies: A case study of Blockchain for Europe

Cited by 2 publications

References 84 publications

Fracking Twitter: Utilizing machine learning and natural language processing tools for identifying coalition and causal narratives

Fracking Twitter: Utilizing machine learning and natural language processing tools for identifying coalition and causal narratives

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