This study conducts an analysis of Donald J. Trump’s 2018 speech at the United Nations General Assembly through the lens of Van Dijk’s (2005) Socio-Cognitive Model, with a particular emphasis on political and critical discourse analysis. It investigates the mechanisms by which Trump defends his positions and persuades his audience, notably through the use of hyperbole and numerical exaggerations to underscore his accomplishments. The research posits that political speech has a notable impact on influencing public ideology. The linguistic analysis reveals variations in Trump’s communication strategies, highlighting his use of boastful and exaggerated statements to promote his achievements. The study employs Van Dijk’s Ideological Square along with a socio-cognitive approach as its theoretical and analytical frameworks, focusing on four key strategies. The findings suggest that Trump prioritizes exaggeration over logical argumentation and frequently engages in aggressive rhetoric against nations that challenge America’s superpower status.

Corporate climate disclosures have come to the fore of measuring environmental responsibility, but worries about greenwashing of exaggeration or parts of the environmental performance of exaggerating or overselling environmental performance remain. This paper fulfills this crucial gap in establishing the validity of such revelations by offering the machine learning method of identifying possible greenwashing. It is probable that the mixed-methods design has been used, where the textual analysis of the composed corporate sustainability reports and supervised learning algorithms trained on labeled examples of misleading statements are supplemented. Through the implementation of natural language processing and classification algorithms, the model will recognise patterns that are suggestive of a lack or even exaggeration of commitment with regard to climate pledges. The findings can be used to illustrate industry-related patterns and important language indications linked to greenwashing.