From the White House with anger: Conversational features in President Trump's official communication

Reyes, Antonio; Ross, Andrew S.

doi:10.1016/j.langcom.2020.12.003

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…Sounds familiar? Commonly regarded as an object of comparison of Putin's political inclination (Hauser 2018), the former American president, Donald J. Trump, and his team also appear to have employed a deliberate or idiosyncratic campaigning style and rhetoric (Mercieca 2020;Reyes and Ross 2021). More importantly, Putin, on the one hand, was dubbed as an iron-fist father figure with an inevitably authoritarian inclination under the political scheme of Russia (Gorham 2005), with some political scientists defining his political strategies as "Putinism" (Fish 2017).…”

Section: Introductionmentioning

confidence: 99%

Fellow or foe? A quantitative thematic exploration into Putin’s and Trump’s stylometric features

Wang¹,

Zeng²

2023

Glottometrics

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Thematic concentration, a quantitative linguistic method, can reflect the speech style of a particular person. It may, to some degree, reflect the degree of a speaker’s intention to communicate certain themes. There has been limited empirical research on the similarity between Trump and Putin with respect to their linguistic features. Thus, the present study aims to compare Putin’s and Trump’s stylometric features and political themes based on thematic concentration with a corpus of Putin’s, Medvedev’s, Trump’s, and Obama’s speeches. Results show that 1) Both Putin’s and Trump’s speeches’ thematic concentration values are significantly or marginally significantly different from their precedents’. 2) Two leaders pay great attention to the concept of nationalism. 3) Thematic words of their speeches were slightly different across periods, reflecting the influence of external factors on the choice of language. The results of the present study may shed light on the stylometric studies of Putin and Trump.

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Section: Introductionmentioning

confidence: 99%

Fellow or foe? A quantitative thematic exploration into Putin’s and Trump’s stylometric features

Wang¹,

Zeng²

2023

Glottometrics

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“…He often uttered hate speeches towards his opponent, for instance, 'crooked Hillary' (Book et al, 2020). On another occasion, he called his opponent motherf****r, son of a b***h which led him to impeachment in re-elected as President (Reyes & Ross, 2021). Therefore, many researchers have investigated his political speeches to interpret his implied meaning and his political purposes in delivering his speeches (Book et al, 2020;Elder, 2020;Reyes and Ross, 2021;Stoner, 2021;Alenisi, & Abusaleek, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…On another occasion, he called his opponent motherf****r, son of a b***h which led him to impeachment in re-elected as President (Reyes & Ross, 2021). Therefore, many researchers have investigated his political speeches to interpret his implied meaning and his political purposes in delivering his speeches (Book et al, 2020;Elder, 2020;Reyes and Ross, 2021;Stoner, 2021;Alenisi, & Abusaleek, 2022). Despite many studies investigating Donald Trump's political speeches, very little research has attempted to show how President Trump's speeches has affected the teaching material of English.…”

Section: Introductionmentioning

confidence: 99%

Engaging Students’ Writing Competency on Metadiscourse Markers Using Donald Trump Presidential Speeches

Suswati¹,

Malik²

2023

SAGA

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This study aimed to investigate the implication of Donald Trump Presidential speeches as the teaching material of Metadiscourse Markers (MM) in writing class. It employed a qualitative study of English students after the pandemic situation. The research population consisted of 30 second-year students (male and female) from one selected class of Contextual Written Language Skills (CWLS) in the English Education Department of State University of Medan, Indonesia. The CWLS students’ writing ability was assessed using a questionnaire and Donald Trump Presidential speech transcripts taken from millercenter.org. The study referred to Hyland’s metadiscourse taxonomy (2005) and Lee’s contextual factors in writing (2016). The findings revealed that there was a distinct improvement in CWLS students’ writing ability between males and females on MM. This study also found that methodological variables such as teaching material and instructional design were some of the influencing factors in the learning process of MM using Donald Trump Presidential speeches.

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“…Such studies are now covered by dedicated volumes ( Kamps, 2021 ; Schier, 2022 ) as well as a wealth of scholarly works, including large-scale social network analysis and mining ( Zheng et al, 2021 ), analyses of media responses ( Christenson, Kreps & Kriner, 2021 ), the interplay of tweets and media ( Morales, Schultz & Landreville, 2021 ), or the (absence of) impact between his tweets and stock returns ( Machus, Mestel & Theissen, 2022 ). Related studies also include analyses about comments from political figures about Trump ( Milford, 2021 ; Alexandre, Jai-sung Yoo & Murthy, 2021 ) and vice versa ( Brown Crosby, 2022 ), analyses of his letter during the first impeachment ( Reyes & Ross, 2021 ), and detailed examinations about how Trump’s message was perceived by specific groups such as White extremists ( Long, 2022 ). The impeachment and handling of COVID-19 have received particular attention on Twitter ( Dejard et al, 2021 ; Cervi, García & Marín-Lladó, 2021 ), as key moments of his presidency.…”

Section: Introductionmentioning

confidence: 99%

(Re)shaping online narratives: when bots promote the message of President Trump during his first impeachment

Galgoczy

Phatak

Vinson

et al. 2022

PeerJ Computer Science

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Influencing and framing debates on Twitter provides power to shape public opinion. Bots have become essential tools of ‘computational propaganda’ on social media such as Twitter, often contributing to a large fraction of the tweets regarding political events such as elections. Although analyses have been conducted regarding the first impeachment of former president Donald Trump, they have been focused on either a manual examination of relatively few tweets to emphasize rhetoric, or the use of Natural Language Processing (NLP) of a much larger corpus with respect to common metrics such as sentiment. In this paper, we complement existing analyses by examining the role of bots in the first impeachment with respect to three questions as follows. (Q1) Are bots actively involved in the debate? (Q2) Do bots target one political affiliation more than another? (Q3) Which sources are used by bots to support their arguments? Our methods start with collecting over 13M tweets on six key dates, from October 6th 2019 to January 21st 2020. We used machine learning to evaluate the sentiment of the tweets (via BERT) and whether it originates from a bot. We then examined these sentiments with respect to a balanced sample of Democrats and Republicans directly relevant to the impeachment, such as House Speaker Nancy Pelosi, senator Mitch McConnell, and (then former Vice President) Joe Biden. The content of posts from bots was further analyzed with respect to the sources used (with bias ratings from AllSides and Ad Fontes) and themes. Our first finding is that bots have played a significant role in contributing to the overall negative tone of the debate (Q1). Bots were targeting Democrats more than Republicans (Q2), as evidenced both by a difference in ratio (bots had more negative-to-positive tweets on Democrats than Republicans) and in composition (use of derogatory nicknames). Finally, the sources provided by bots were almost twice as likely to be from the right than the left, with a noticeable use of hyper-partisan right and most extreme right sources (Q3). Bots were thus purposely used to promote a misleading version of events. Overall, this suggests an intentional use of bots as part of a strategy, thus providing further confirmation that computational propaganda is involved in defining political events in the United States. As any empirical analysis, our work has several limitations. For example, Trump’s rhetoric on Twitter has previously been characterized by an overly negative tone, thus tweets detected as negative may be echoing his message rather than acting against him. Previous works show that this possibility is limited, and its existence would only strengthen our conclusions. As our analysis is based on NLP, we focus on processing a large volume of tweets rather than manually reading all of them, thus future studies may complement our approach by using qualitative methods to assess the specific arguments used by bots.

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From the White House with anger: Conversational features in President Trump's official communication

Cited by 6 publications

References 26 publications

Fellow or foe? A quantitative thematic exploration into Putin’s and Trump’s stylometric features

Fellow or foe? A quantitative thematic exploration into Putin’s and Trump’s stylometric features

Engaging Students’ Writing Competency on Metadiscourse Markers Using Donald Trump Presidential Speeches

(Re)shaping online narratives: when bots promote the message of President Trump during his first impeachment

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