Political Discourse Analysis of Donald Trump’s Rhetoric: A Linguistic Study of Cognition and Discursivity
Bibliographic record
Abstract
To elucidate the deliberate use of Islamophobia in the political sphere, this study carefully examines Donald Trump's presidential campaign speeches using political discourse analysis (PDA). Trump’s Islamophobic position in political discourse has sparked a global discussion. His divisive political language during the 2016 elections contributed to an overall rise in incidents showing hatred for Muslims in the America by painting a poor picture of the Muslim world. The present study employs the theoretical framework of Van Dijk (1998) to examine the socio-political contexts of the discourse: participants’ insight (their goals, relevant knowledge and their belief system), group organization, power dynamics, as well as favourable and unfavourable perceptions of “us” against “them”. The purpose of the work is to highlight the processes by which Islamophobia is created, propagated, and normalised in public discourse. The nature of the current investigation involves heterogeneous techniques. It analyzes the text using PDA methods. Additionally, it computes word frequency to determine the proportion of positive to negative terms in Donald Trump's political speech. The analysis reveals that political leaders use language as a tool to serve their own political ends. This study focusses on understanding the complex interplay between language, power, and ideology in contemporary political communication. By shedding light on the pervasive influence of Islamophobia as a political strategy, it underscores the imperative of critically engaging with political discourse to challenge hegemonic narratives, and to foster all-inclusive and democratic societies. Political leaders frequently employ language as a weapon to further their agendas. This research work highlights the necessity of critically interacting with political discourse to challenge dominant narratives and promote inclusive and democratic societies by bringing to light the pervasive influence of Islamophobia as a political strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".