A Content Analysis Employing the FCET Model:

Framing Emerging Technologies in Selected Pakistani and International News Media

Authors

  • MUHAMMAD QASIM NIZAMANI University of Sindh, Jamshoro
  • Farheen Qasim Nizamani Department of Media & Communication Studies, University of Sindh, Jamshoro, Sindh, Pakistan https://orcid.org/0000-0001-7283-714X
  • Liaquat Ali Umrani Department of Media & Communication Studies, University of Sindh, Jamshoro, Sindh, Pakistan

Keywords:

News Framing, FCET model, Emerging Technologies, Pakistani News Media, Generative AI

Abstract

This research examines the quantitative content analysis of three news framing of emerging technologies, Big Data, Generative Artificial Intelligence (AI), and Augmented Reality (AR/XR) in Pakistani and international online news media outlets over two years (24-month period from 1 May 2024 to 1 May 2026). Developing on the Frame Categories for Emerging Technologies (FCET) model established by Graves-Sandriman (2026), a stratified purposive sample of N = 612 news items/ articles was coded in the line of four analytic classes: Conceptualization, Newness, User Experience, and Evaluation. The corpus was drawn from 11 Pakistani news platforms (both based on English and Urdu languages) and three international English language-based news platforms, segmented into four six-month temporal strata. Results discovered that FCET-4 (Evaluation) was the leading frame category across the complete sample (27.3%), with noteworthy variation by technology: Big Data news coverage highlighted Newness frames (28.9%), Generative AI shared evenly amid Conceptualization and Evaluation (25.5% each), and AR was most strongly rooted in Evaluation (30.4%). The IT Export Boost (n =59) and Regulatory Need (n = 8) frames overshadowed evaluative coverage, indicating Pakistan specific monetary and governance concerns. User Experience (FCET-3) was lacking 22.1% of completely coded articles, supporting Graves-Sandriman's (2026) research that user perception remains understated in emerging technology news media coverage. Largely, article valence was mixed in nature or unbiased in over 50% of the corpus, with Generative AI drawing the largest negative valence rate (27.9%). No statistically meaningful variance was noted in valence between Pakistani English language and Pakistani Urdu language news platforms. These results contribute to the growing body of knowledge on News Media coverage on emerging technology in the Global South and approach methodological replication and extension of the FCET model in a non-Western settings.

 

Keywords: news framing, FCET model, emerging technologies, Pakistani News media, content analysis, Big Data, Generative AI, Augmented Reality (AR), media framing

Published

2026-08-28