Sheeza Waqar

 Photo of Sheeza Waqar

Research topic

Speaking for the People: Imran Khan, Valence Populism, and the Discursive Framing of Corruption in Pakistan

My PhD research investigates how anti-corruption became one of the most influential political narratives in contemporary Pakistan through the speeches of former Prime Minister Imran Khan between 2013 and 2023. Rather than treating corruption simply as a policy issue, the study examines how Khan consistently presented corruption as a moral, political, and national concern and how this discourse evolved across different stages of his political career.

The research adopts a mixed qualitative and quantitative approach to provide a comprehensive analysis of Khan's anti-corruption rhetoric. The qualitative component employs Critical Discourse Analysis (CDA), supported by NVivo, to examine eight strategically selected speeches representing key political moments during his opposition, premiership, and post-premiership periods. The quantitative component analyses a corpus of 150 English-translated speeches using RStudio and the Quanteda package to identify patterns in language, trace changes in the prominence of anti-corruption discourse over time, and explore the distinctive vocabulary associated with different political phases.

The study examines how anti-corruption became a defining feature of Khan's political rhetoric, how he positioned himself in relation to political elites, and how his discourse adapted to changing political circumstances. By combining close qualitative interpretation with large-scale computational text analysis, the research provides a comprehensive account of the evolution of anti-corruption discourse in Pakistan over a decade.

This research contributes to broader debates on populism, political discourse, and democratic politics while demonstrating the value of integrating Critical Discourse Analysis with corpus linguistic methods to study contemporary political rhetoric.

Supervisory team

Principal supervisor: Professor Paul Kenny
Co-supervisor: Dr Daniel Casey

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