Strategic Emotional Rhetoric in Political Social Media: A Comprehensive Analysis of Donald Trump’s Tweeting Patterns and Propagandistic Techniques in the Digital Age

This study employs Python-based analysis to investigate how Donald Trump strategically leveraged propagandistic techniques and emotional rhetoric on Twitter (2009–2019) to shape public perception and advance his political agenda. Analyzing 43,913 original tweets through sentiment analysis, linguistic patterns, and temporal dynamics, we reveal Trump’s systematic use of name-calling (“fake”, “illegal”), glittering generalities (“greatness”), and plain folks appeals to cultivate a combative, relatable persona. Quantitative results show his preference for emotional punctuation (exclamation marks: 12,458), negative vocabulary (529 pejorative terms), and algorithm-optimized timing (e.g., breakfast hours) to amplify engagement. Temporal tweet patterns correlate with political milestones like his 2016 victory, illustrating digital rhetoric’s weaponization to bypass media gatekeepers. Applying promotional arts theory, we expose how emotional manipulation, algorithms, and propagandistic framing interact in political communication. Findings highlight Twitter as a battleground for symbolic reality, where simplified language, repetition, and adversarial narratives fuel polarization and in-group loyalty. This research advances political communication scholarship by elucidating how digital platforms enable leaders to exploit cognitive biases, emotional contagion, and algorithmic mechanics to redefine democratic discourse in the post-truth era.

Authors

Publication Details

Journal
Studies in Linguistics and Literature
Published
2025-04-08
DOI
https://doi.org/10.22158/sll.v9n2p48
Primary Topic
Rhetoric and Communication Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Strategic Emotional Rhetoric in Political Social Media: A Comprehensive Analysis of Donald Trump’s Tweeting Patterns and Propagandistic Techniques in the Digital Age

Yiping Li, Yuejing Zhai
Studies in Linguistics and Literature
Rhetoric and Communication Studies
article

Strategic Emotional Rhetoric in Political Social Media: A Comprehensive Analysis of Donald Trump’s Tweeting Patterns and Propagandistic Techniques in the Digital Age

Yiping Li, Yuejing Zhai
article en

Abstract

This study employs Python-based analysis to investigate how Donald Trump strategically leveraged propagandistic techniques and emotional rhetoric on Twitter (2009–2019) to shape public perception and advance his political agenda. Analyzing 43,913 original tweets through sentiment analysis, linguistic patterns, and temporal dynamics, we reveal Trump’s systematic use of name-calling (“fake”, “illegal”), glittering generalities (“greatness”), and plain folks appeals to cultivate a combative, relatable persona. Quantitative results show his preference for emotional punctuation (exclamation marks: 12,458), negative vocabulary (529 pejorative terms), and algorithm-optimized timing (e.g., breakfast hours) to amplify engagement. Temporal tweet patterns correlate with political milestones like his 2016 victory, illustrating digital rhetoric’s weaponization to bypass media gatekeepers. Applying promotional arts theory, we expose how emotional manipulation, algorithms, and propagandistic framing interact in political communication. Findings highlight Twitter as a battleground for symbolic reality, where simplified language, repetition, and adversarial narratives fuel polarization and in-group loyalty. This research advances political communication scholarship by elucidating how digital platforms enable leaders to exploit cognitive biases, emotional contagion, and algorithmic mechanics to redefine democratic discourse in the post-truth era.

Studies in Linguistics and LiteratureVol. 9(2)
Openalex Percentile: Top 92%
Rhetoric and Communication Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.