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Political authenticity is connected to higher levels of political trust from electorates and can influence political outcomes, but it is often overlooked as a relevant factor for electoral behavior. To date, discussions of how authenticity appears and changes in politics typically remain at the theoretical level and are rarely comparative. This article develops a framework to identify and compare how authenticity is performed in political discourses over time and across settings by politicians. To demonstrate the usefulness of the framework, this article investigates authenticity performances in 21,496 political texts of electoral debates, interviews, campaigns, and official speeches by presidents and presidential candidates in Brazil and the United States (US) since 1988. The findings indicate that authenticity is generally performed with greater frequency by presidents and presidential candidates in Brazil than in the US, though authenticity performances are not more prevalent during election years in either country.
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This article investigates how the Amazon has been constructed as a problem in Brazilian presidential speeches since 1985. We develop a framework that accounts for how important transnational actors, as presidents, construct policy objects as particular problems depending on where and when they participate in politics. We create a dataset containing 6240 official speeches by all Brazilian presidents since 1985. We train a supervised machine learning algorithim to classify how Amazon related sections within speeches construct the Amazon as a problem. We find that presidents often construct the Amazon as an environmental problem when speaking far away from the region, whereas they usually construct it as problems of economic integration or social development when in the Amazon.
Awarded Best Paper in Amazonian Studies at the Latin American Studies Association
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Urgency Analysis: Ranking Political Priorities (with Jael Tan and James Hollway)
How urgent is climate change for our leaders and where does it lie in their list of priorities? When speaking to their electorates and canvassing support, politicians employ various terms to signal the urgency of different problems and the priority of specific policies. We introduce a new text analytic tool, urgency analysis, to capture the urgency of political priorities in discourse. Urgency analysis combines natural language processing and survey validated dictionaries to provide an interpretable measure of the urgency of priorities in political texts. To demonstrate the usefulness of urgency analysis, we compare the urgency of climate change priorities by speaker and over time. Using data on US presidential and UK prime ministerial political speeches on climate change between 2009 and 2019, we find that climate change appears less urgent over time in political discourses, especially when compared employment, immigration, and health. We conclude by discussing extensions to urgency analysis and its potential applications. Urgency analysis is implemented for R with the poldis package, making it an easy, free, and accessible tool for researchers interested in analyzing political discourses.
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Managing imprecise dates in R with messydates (with James Hollway)
Dates are often messy. Whether historical (or ancient), future, or even recent, we sometimes only know approximately when an event occurred, that it happened within a particular period, or sources offer multiple competing dates. Although researchers generally recognize this messiness, many feel expected to force artificial precision or unfortunate imprecision on temporal data to proceed with analysis. However, this can create inferential issues when timing or sequence is important. This paper introduces the messydates R package that assists researchers with this problem by retaining and working with various kinds of date imprecision.
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Is peace good for the environment? A comparative approach to post-conflict environmental outcomes (with Stefano Jud and Quynh Nguyen)
In the past decades, most conflicts took place in areas considered biodiversity hotspots. There is a growing literature that investigates the relationship between conflict and the environment; however, these studies typically remain at the country level and are rarely comparative. The aggregation of isolated case studies can be problematic since they rely on different definitions and operationalizations of conflicts, diverse measures of environmental degradation, and work with different geopolitical levels. We propose investigating the relationship between conflict, peace, and the environment systematically by considering land-use change patterns in conflict and post-conflict zones over time. We work with a high-resolution 30 by 30 square meters global landcover time-series dataset and a georeferenced conflict events dataset to create similar sized peace and conflict areas for several countries that have experienced violent conflicts since the year 2000. These areas serve as treatment and control for inferential models that investigate how conflicts progress and end and their impact on land-use changes in their aftermath.