Corpus-Based Multimodal Analysis of Pakistan–India War Memes Conflict 2025 (PIWMC)

Corpus-Based Multimodal Analysis of Pakistan–India War Memes Conflict 2025 (PIWMC)

Authors

  • Saba Irfan Minhaj University Lahore

DOI:

https://doi.org/10.58932/MULK0015

Keywords:

social media, geopolitical conflict, Pakistan-India conflict, multimodal analysis, digital battlefield, satire and humor.

Abstract

Social media has become a veritable vanguard to raise awareness, activism, and shape public opinion, but how users employ linguistic devices to create awareness and amplify children's concerns has hitherto been less studied. This paper examines the use of interrogatives as a pragmatic strategy in X interactions about children, with the aim of identifying the predominant forms of interrogative use, the discourse issues they index, and their pragmatic functions within the affordance of the asynchronous discourse context. Data for the study were part of a larger project corpus of 285,015 tweets with 2,819,696 tokens collected between July 2023 and July 2024 using the Apify Twitter Scraper. The study adopted a mixed method of corpus-based methodologies and qualitative discourse analysis (QDA) as data were coded and sorted using Laurence Anthony’s AntConc by identifying 22,299 interrogative sentences and subjected to pragma-discourse analysis using aspects of Mey’s Pragmatic Act Theory. Findings reveal two main forms of interrogative in the discourse: open and closed interrogatives. These interrogative forms characterised security, indoctrination, and sex-related discourse issues through SSK, REF, REL, MPH, to express communicative functions such as condemning, indicting, and accusing. The study concludes that some X users strategically use question style to convey their emotionally driven intentions while expressing their opinions on children-related issues.

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Published

2026-06-29

How to Cite

Irfan, S. (2026). Corpus-Based Multimodal Analysis of Pakistan–India War Memes Conflict 2025 (PIWMC). Journal of Advanced Corpus-Oriented Research, 2(1), 120–144. https://doi.org/10.58932/MULK0015

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