representation of president raisi’s helicopter crash in media: a now corpus-based discourse analysis

Following the crash of the iranian air force helicopter near the village of uzi on 19 may 2024, which caused the decease of the iranian president ebrahim raisi and seven other politicians and crew members, a vast number of news articles, commentaries, and papers were published worldwide, reflecting various reactions to the incident. this event serves as a boundary-making moment both in the surface structure—narrating an event—and in the deep structure—drawing an in-group/out-group divide with the president as the representative of a conservative discourse. the present study attempts to examine how ebrahim raisi’s helicopter crash has been represented in the now corpus (approximately 19.2 billion words). inspired by baker’s (2004) framework of corpus-assisted analysis, as well as laclau and mouffe’s discourse theory (2001) and van dijk’s ideological square (2009), the analysis of this event’s representation was conducted using the keynes parameter and semantic categorization based on a detailed reading of concordance lines. corpus-based categorization serves as a tool to articulate the semiotic system of the western worldview toward this incident. the data ultimately reveal a dual representation of the event. on one hand, it narrates reactions from various actors, including the islamic republic’s allies and enemies; on the other, at a deeper level, it guides the audience toward an other-making contemplation, producing three types of other-makings based on raisi’s different positions as a judge, president, and religious figure. this discourse revolves around a nodal point of binary representation—good versus evil—centered on the late president, with categories such as alliance, conspiracy theories, transformation from death to salvation, leadership aspirations, hardline stance, public (il)legitimacy, brutal judge, suppressor president, and unconfirmed cleric, which foreground the negative other more than the positive self.