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This study analyzes the relationship between state-level variables and Twitter discourse on genetically modified organisms (GMOs). Using geographically identified tweets related to GMOs, we examined how the sentiments expressed about GMOs related to education levels, news coverage, proportion of rural and urban counties, state-level political ideology, amount of GMO-related legislation introduced, and agricultural dependence of each U.S. state. State-level characteristics predominantly did not predict the sentiment of the discourse. Instead, the topics of tweets predicted the majority of variance in tweet sentiment at the state level. The topics that tweets within a state focused on were related to state-level characteristics in some cases.
In May 2016, the National Academies of Sciences, Engineering, and Medicine (NASEM) released the report “Genetically Engineered Crops: Experiences and Prospects,” summarizing scientific consensus on genetically engineered crops and their implications. NASEM reports aim to give the public and policymakers information on socially relevant science issues. Their impact, however, is not well understood. This analysis combines national pre- and post-report survey data with a large-scale content analysis of Twitter discussion to examine the report’s effect on public perceptions of genetically modified organisms (GMOs). We find that the report’s release corresponded with reduced negativity in Twitter discourse and increased ambivalence in public risk and benefit perceptions of GMOs, mirroring the NASEM report’s conclusions. Surprisingly, this change was most likely for individuals least trusting of scientific studies or university scientists. Our findings indicate that NASEM consensus reports can help shape public discourse, even in, or perhaps because of, the complex information landscape of traditional and social media.
This paper presents a method for using dimensional reduction in the analysis of political content. We draw inspiration from latent semantic analysis (LSA) theory, which posits that factor analysis can successfully model human language. We suggest that the factor analysis of word frequencies generated from any political text—for example, open-ended survey responses—provides adequate content analysis categories and can substitute for more commonly practiced techniques. The method proceeds in three steps: data preparation, exploratory factor analyses, and hypothesis testing. This method may produce other benefits by allowing the data to speak more clearly in the development of coding dictionaries while avoiding the problems of inferential circularity common in other data-driven approaches. We demonstrate the method using responses collected in the execution of an experimental design dealing with the topic of partial-birth abortion and assess the demonstration by presenting a human coding of the same material.
Many Americans hold distorted views of elected officials and, as our study shows, the blame is due partly to our ideological biases and partly to mass media. Analyzing a nationally representative online survey, we corroborate recent research that found that one in five Americans still believe president Barack Obama is a Muslim and that almost seven in ten mistakenly think Sarah Palin, and not Saturday Night Live’s Tina Fey, was the first to say “I can see Russia from my house.” Although race, political ideology, and “born-again” or evangelical Christian status were the primary drivers of misperceptions about Obama’s faith, media use had a more crucial role in predicting the more widespread misperception about Palin. Misattribution of the Fey quote to Palin was greatest among heavy viewers of traditional news media and late-night TV comedy, which is suggestive of the “lamestream media” effect often espoused by prominent Republican figures.
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