Hearts or Minds? Persuasive Messages on Climate Change

What kinds of appeals do the public find persuasive for global causes? Are arguments that appeal to so-called rational self-interest more persuasive than those that appeal to morality? Are mixed messages that combine appeals of self-interest with morality more successful than streamlined single themed messages? The causal mechanisms by which transnational advocacy movements are able to generate political support for their campaigns are poorly specified in the literature in international relations and public opinion. This paper explores the relative persuasiveness of advocacy appeals for the issue of climate change. Using an experimental design, this paper reports the results of survey market research of a diverse sample of 360 subjects, each of whom was assigned to one of four conditions, a control condition with no message appeal, an economic self-interest appeal, a secular moral appeal, and a mixed appeal combining self interest and morality. Subjects were then asked a series of questions about their willingness to support advocacy efforts, including such actions as writing a letter to the member of Congress, signing a petition, and joining an organization. We hypothesized that for issues like climate change for which the costs of action are higher and for which there is a more direct cost to individuals or the country, arguments based on economic self-interest are more likely to be persuasive than moral appeals. Where the direct risks or costs to individuals or the country are lower (like the global AIDS crisis), moral messages are more likely to have appeal. For especially religious subjects, however, we hypothesize that moral arguments may be as if not more persuasive even on issues like climate change where the direct costs to the individual or country are likely to be higher.LBJ School of Public Affair

Structural Topic Models for Open-Ended Survey Responses

Collection and especially analysis of open-ended survey responses are relatively rare in the discipline and when conducted are almost exclusively done through human coding. We present an alternative, semiautomated approach, the structural topic model (STM) (Roberts, Stewart, and Airoldi 2013; Roberts et al. 2013), that draws on recent developments in machine learning based analysis of textual data. A crucial contribution of the method is that it incorporates information about the document, such as the author's gender, political affiliation, and treatment assignment (if an experimental study). This article focuses on how the STM is helpful for survey researchers and experimentalists. The STM makes analyzing open-ended responses easier, more revealing, and capable of being used to estimate treatment effects. We illustrate these innovations with analysis of text from surveys and experiments

Replication data for: Hearts or Minds? Identifying Persuasive Messages on Climate Change

This includes the do file, raw data, and the data ma

CCES 2014, Team Module of University of Texas at Austin (UTA)

This dataverse contains the data and supporting documents for the CCES 2014 University of Texas at Austin. This project was supported by the National Science Foundation, Grant Number SES-1430505

Hearts or minds? Identifying persuasive messages on climate change

This article sheds light on what kinds of appeals persuade the US public on climate change. Using an experimental design, we assign a diverse sample of 330 participants to one of four conditions: an economic self-interest appeal, a moral appeal, a mixed appeal combining self-interest and morality and a control condition with no persuasive appeal. 1 Participants were then asked a series of questions about their willingness to support advocacy efforts, including such actions as writing a letter to Congress, signing a petition and joining an organization. We hypothesized that for issues like climate change where it is expensive to address the problem, arguments based on self-interest are more likely to be persuasive than moral appeals. Our experiment yielded some surprising results. Knowledge was an important moderator of people’s attitudes on climate change in response to the persuasive messages. We found that among respondents who were more knowledgeable about climate change that the economic frame was most the persuasive in terms of a subject’s willingness to take actions to support the cause. However, among low knowledge respondents, the control condition without messaging yielded the most concern

Structural Topic Models for Open-Ended Survey Responses

Collection and especially analysis of open-ended survey responses are relatively rare in the discipline and when conducted are almost exclusively done through human coding. We present an alternative, semiautomated approach, the structura ltopic model (STM) (Roberts, Stewart, and Airoldi 2013; Roberts et al. 2013), that draws on recent developments in machine learning based analysis of textual data. A crucial contribution of the method is that it incorporates information about the document, such as the author'™s gender, political affiliation, and treatment assignment (if an experimental study). This article focuses on how the STM is helpful for survey researchers and experimentalists. The STM makes analyzing open-ended responses easier, more revealing, and capable of being used to estimate treatment effects. We illustrate these innovations with analysis of text from surveys and experiments