Speaking Different Languages or Reading from the Same Script? Word Usage of Democratic and Republican Politicians

Neiman, Jayme L.; Gonzalez, Frank J.; Wilkinson, Kevin W.; Smith, Kevin B.; Hibbing, John R.

doi:10.1080/10584609.2014.969465

Cited by 28 publications

(19 citation statements)

References 34 publications

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“…They initially applied this dictionary to examining sermons spoken in liberal and conservative religious denominations (Graham et al 2009). In subsequent work using the Moral Foundations Dictionary and focusing on political elites, Neiman et al (2016) concluded that the rhetoric of politicians generally fails to follow the script anticipated by MFT. Lipsitz (2018), however, found, with some nuances, support for MFT in her analysis of the differences between Democratic and Republican campaign advertisements.…”

Section: Of 25mentioning

confidence: 99%

“…In applying this research to actual political systems, an important question is whether elite political actors-politicians themselves-exhibit left/right differences in intuitions and behaviors that are similar to those among the general public. Unfortunately, researchers are constrained in their ability to assess the political intuitions of elites, given the relatively scripted nature of politicians' behavior and speech on the public stage, along with a lack of access to elites in their more private or unguarded moments (but for attempts, see Neiman et al 2016;Lipsitz 2018;Jones et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…The results show differences between Trump and his fellow Republican candidates, with Trump most notable for his low overall use of words associated with moral foundations. This study, in combination with the relatively few others of its type (Motyl 2012;Clifford and Jerit 2013;Sagi and Dehghani 2014;Neiman et al 2016;Lipsitz 2018), should help scholars explore both the potential and the pitfalls of using automated text analysis to provide indications of left/right differences in cognition among political elites.…”

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confidence: 98%

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Moral Foundations in the 2015-16 U.S. Presidential Primary Debates: The Positive and Negative Moral Vocabulary of Partisan Elites

Lewis

2019

Social Sciences

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Moral foundations theory (MFT) suggests that individuals on the political left draw upon moral intuitions relating primarily to care and fairness, whereas conservatives are more motivated than liberals by authority, ingroup, and purity concerns. The theory of conservatism as motivated social cognition (CMSC) suggests that conservatives are more attuned than liberals to threat and to negative stimuli. Because evidence for both accounts rests on studies of mass publics, however, it remains unclear whether political elites of the left and right exhibit these inclinations. Thus, this analysis uses the 2015-16 United States presidential primary season as an occasion to explore partisan differences in candidates’ moral rhetoric. The analysis focuses on verbal responses to questions posed during party primary debates, a setting that is largely unscripted and thus potentially subject to intuitive influences. The Moral Foundations Dictionary is employed to analyze how frequently candidates used words representing various moral foundations, distinguishing between positive and negative references to each. Consistent with CMSC, the Republican candidates were more likely to use negative-valence moral terminology, describing violations of moral foundations. The direction of some partisan differences contradicts the expectations of MFT. Donald Trump, a novice candidate, was an exception to the typical Republican pattern, making markedly lower overall use of moral-foundations vocabulary.

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Section: Of 25mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 98%

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Moral Foundations in the 2015-16 U.S. Presidential Primary Debates: The Positive and Negative Moral Vocabulary of Partisan Elites

Lewis

2019

Social Sciences

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“…LIWC has been used to explore subjects like interracial feedback (Harber et al, 2019), self and other focus (Barasch & Berger, 2014), and racial disparities in police language (Voigt et al, 2017). Other word count-based dictionaries have been used to measure things like warmth and competence (Nicolas et al, 2021), gender stereotypes (Gaucher et al, 2011), references to nature (Kesebir & Kesebir, 2017), and liberal and conservative language (Neiman et al, 2016).…”

Section: Unlocking the Potential Of Textmentioning

confidence: 99%

Using natural language processing to understand people and culture.

Berger¹,

Packard²

2022

American Psychologist

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Language can provide important insights into people, and culture more generally. Further, the digitization of information has made more and more textual data available. But by itself, all that data are just that: data. Realizing its potential requires turning that data into insight. We suggest that automated text analysis can help. Recent advances have provided novel and increasingly accessible ways to extract insight from text. While some psychologists may be familiar with dictionary methods, fewer may be aware of approaches like topic modeling, word embeddings, and more advanced neural network language models. This article provides an overview of natural language processing and how it can be used to deepen understanding of people and culture. We outline the dual role of language (i.e., reflecting things about producers and impacting audiences), review some useful text analysis methods, and discuss how these approaches can help unlock a range of interesting questions.

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“…Fulgoni et al, (2016) examined the differences in word usage in partisan news articles and found that liberals and conservatives frame political issues in partisan sources according to their endorsement of moral foundations as MFT claimed, except for purity foundation. However, Neiman et al, (2016) found that the rhetoric of Democratic and Republican political elites does not strongly meet the predictions of MFT (but see Lipsitz, 2018). Later studies, benefiting from the richness of online and big data, examined the relationship between morality and ideology, especially on social media.…”

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confidence: 99%

A tale of two hashtags: An examination of moral content of pro‐ and anti‐government tweets in Turkey

Bayrak

Alper

2021

Euro J Social Psych

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Amidst the 2018 presidential campaign, Turkish President Recep Tayyip Erdogan said "If one day our nation says tamam 1 (enough), then we will step aside" in his parliamentary speech. After Erdoğan's speech, there was a burst of comments on social media, especially on Twitter (Pierpoint et al., 2018). Opponents of Erdogan, including the members of parliament from opposition parties, reacted to Erdogan's words by tweeting using the "#tamam" hashtag, which was used to advocate that it is enough and it is time for Erdogan to step aside. As a reaction to this, Erdogan's supporters, including some of the ministers and deputies, responded by starting the hashtag "#devam" (which means "continue") to show their support for Erdogan's government. These two hashtags were used almost 2 million times in one day and they both ended up on Twitter's worldwide trending topic list (Pierpoint et al., 2018). In this study, the pro-and antigovernment tweets with the "#tamam" and "#devam" hashtags were analyzed in terms of their moral content. We examined the content of 186,554 tweets (86,554 pro-and 100,000 anti-government) within the framework of the Moral Foundations Theory (MFT;

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Speaking Different Languages or Reading from the Same Script? Word Usage of Democratic and Republican Politicians

Cited by 28 publications

References 34 publications

Moral Foundations in the 2015-16 U.S. Presidential Primary Debates: The Positive and Negative Moral Vocabulary of Partisan Elites

Moral Foundations in the 2015-16 U.S. Presidential Primary Debates: The Positive and Negative Moral Vocabulary of Partisan Elites

Using natural language processing to understand people and culture.

A tale of two hashtags: An examination of moral content of pro‐ and anti‐government tweets in Turkey

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