A critical discourse analysis of narrative discrepancies in Pakistani news channel headlines

This case study investigates how the same tweet is treated linguistically with different narratives. Taking the case of the tweet of Maryam Nawaz, a Pakistani female politician, we analyze through different linguistic interpretations the selected news channels’ headlines that picked up her tweet on...

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Main Authors: Awais Rubbani, Karizza P. Bravo-Sotelo, Manal
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:Cogent Arts & Humanities
Subjects:
Online Access:https://www.tandfonline.com/doi/10.1080/23311983.2024.2380115
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author Awais Rubbani
Karizza P. Bravo-Sotelo
Manal
author_facet Awais Rubbani
Karizza P. Bravo-Sotelo
Manal
author_sort Awais Rubbani
collection DOAJ
description This case study investigates how the same tweet is treated linguistically with different narratives. Taking the case of the tweet of Maryam Nawaz, a Pakistani female politician, we analyze through different linguistic interpretations the selected news channels’ headlines that picked up her tweet on the death of a local journalist, Arshad Sharif. Following earlier studies on critical discourse analysis (CDA) of news headlines, the present study investigated how language is used to portray Maryam’s tweet about Arshad Sharif’s death: (1) how is Maryam Nawaz linguistically represented by Pakistani news channels’ headlines, and (2) how are the news channels’ headlines foregrounded/backgrounded to portray the image of Maryam Nawaz. Using a qualitative approach, we gathered data from 10 news headlines from leading Pakistani news channels and used Fairclough’s three-dimensional model for the qualitative analysis of linguistic choices. We also employed Goffman’s theory to analyze foregrounding and backgrounding of news channels’ headlines. Findings suggest that Maryam Nawaz is portrayed negatively with specific linguistic choices, like ‘apology’, ‘indifference’, ‘to shame humanity’, ‘outrage’, ‘backlash’, and ‘inappropriate tweet’. The analysis also reveals that Maryam Nawaz is foregrounded in most news headlines, while Arshad Sharif’s death is backgrounded, demonstrating the prominence given to Maryam Nawaz and the criticisms against her. Such negative linguistic representations in electronic and print media outlets can shape the portrayal of female politicians in Pakistan and have implications for media representation of female public figures, which can be relevant in contexts where media outlets can reframe narratives using linguistic resources to suit certain agendas.
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spelling doaj-art-1601a9dd8030455aa37c150ec40cd8852025-08-20T02:37:49ZengTaylor & Francis GroupCogent Arts & Humanities2331-19832024-12-0111110.1080/23311983.2024.2380115A critical discourse analysis of narrative discrepancies in Pakistani news channel headlinesAwais Rubbani0Karizza P. Bravo-Sotelo1Manal2Faculty of Arts and Social Sciences, Universiti Brunei Darussalam, Gadong, BruneiFaculty of Arts and Social Sciences, Universiti Brunei Darussalam, Gadong, BruneiDepartment of English & Literary Studies, University of Management & Technology, Lahore, PakistanThis case study investigates how the same tweet is treated linguistically with different narratives. Taking the case of the tweet of Maryam Nawaz, a Pakistani female politician, we analyze through different linguistic interpretations the selected news channels’ headlines that picked up her tweet on the death of a local journalist, Arshad Sharif. Following earlier studies on critical discourse analysis (CDA) of news headlines, the present study investigated how language is used to portray Maryam’s tweet about Arshad Sharif’s death: (1) how is Maryam Nawaz linguistically represented by Pakistani news channels’ headlines, and (2) how are the news channels’ headlines foregrounded/backgrounded to portray the image of Maryam Nawaz. Using a qualitative approach, we gathered data from 10 news headlines from leading Pakistani news channels and used Fairclough’s three-dimensional model for the qualitative analysis of linguistic choices. We also employed Goffman’s theory to analyze foregrounding and backgrounding of news channels’ headlines. Findings suggest that Maryam Nawaz is portrayed negatively with specific linguistic choices, like ‘apology’, ‘indifference’, ‘to shame humanity’, ‘outrage’, ‘backlash’, and ‘inappropriate tweet’. The analysis also reveals that Maryam Nawaz is foregrounded in most news headlines, while Arshad Sharif’s death is backgrounded, demonstrating the prominence given to Maryam Nawaz and the criticisms against her. Such negative linguistic representations in electronic and print media outlets can shape the portrayal of female politicians in Pakistan and have implications for media representation of female public figures, which can be relevant in contexts where media outlets can reframe narratives using linguistic resources to suit certain agendas.https://www.tandfonline.com/doi/10.1080/23311983.2024.2380115Linguistic representationnews headlinestextual analysismedia framingforegroundingbackgrounding
spellingShingle Awais Rubbani
Karizza P. Bravo-Sotelo
Manal
A critical discourse analysis of narrative discrepancies in Pakistani news channel headlines
Cogent Arts & Humanities
Linguistic representation
news headlines
textual analysis
media framing
foregrounding
backgrounding
title A critical discourse analysis of narrative discrepancies in Pakistani news channel headlines
title_full A critical discourse analysis of narrative discrepancies in Pakistani news channel headlines
title_fullStr A critical discourse analysis of narrative discrepancies in Pakistani news channel headlines
title_full_unstemmed A critical discourse analysis of narrative discrepancies in Pakistani news channel headlines
title_short A critical discourse analysis of narrative discrepancies in Pakistani news channel headlines
title_sort critical discourse analysis of narrative discrepancies in pakistani news channel headlines
topic Linguistic representation
news headlines
textual analysis
media framing
foregrounding
backgrounding
url https://www.tandfonline.com/doi/10.1080/23311983.2024.2380115
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