Kinship as a Political Weapon: A Critical Discourse Analysis of Gendered Verbal Attacks on Female Relatives of Politicians in South Asia Digital Politics

Zenodo Qualitative Research Repository & Dataset Overview

Dataset Title:

Kinship as a Political Weapon: A Critical Discourse Analysis of Gendered Verbal Attacks on Female Relatives of Politicians in South Asia Digital Politics

Lead Author: Major Muhammad Naveed Ashraf
Co-Author / Supervisor: Dr. Ayesha Zafar
Institution: Department of English, University of Sialkot, Punjab, Pakistan

AUTHOR QUALIFICATIONS & RESEARCH CONTEXT 
This dataset is published upon completion of advanced graduate research (MS/MPhil in English Language and Literature) specializing in Discourse Analysis, Media Linguistics, and Gendered Political Communication. 

Related scholarly publications by the author include:
1. Research Article: "Linguistic Strategies in Strategic Communication: Analyzing the Role of Language in Hybrid Warfare" — Published in Pakistan Army Journal (PAJ).
2. Book Chapter / Monograph: "Resistance and Resilience: Gender, Media, and Counter-Publics in South Asian Digital Spaces."

DATASET CONTENTS & PLOS ONE COMPLIANCE
This repository contains the minimal, anonymized qualitative dataset supporting the research article submitted to PLOS ONE (Manuscript ID: PONE-D-26-23500). It includes:
1. Three-country qualitative corpus distribution tables (Pakistan, India, and Bangladesh, 2020–2024; N = 4,310 dataset units).
2. Feminist Critical Discourse Analysis (FCDA) micro-linguistic analytical frameworks across five dimensions (Lexis, Presupposition, Modality, Naming Practices, and Transitivity).
3. Thematic categorization matrix covering the four primary attack repertoires: Moral Policing, Nepotism Framing, Nationalist Labeling, and Sexualized Disinformation.

 ETHICAL & ANONYMIZATION STATEMENT 
In strict compliance with Association of Internet Researchers (AoIR) Ethical Guidelines (Version 2.0) and PLOS ONE Data Availability Policies:
- All raw social media account screenshots, user handles (@usernames), profile photographs, avatars, and platform interfaces have been completely excluded.
- All textual data are presented exclusively as de-identified, bracketed qualitative extracts to ensure full participant privacy and prevent re-identification.

License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Contact for Access / Inquiries: dr.ayeshazafar@uskt.edu.pk