Quantitative content analysis of climate change coverage in the newspapers Egemen Qazaqstan and the New York Times

Herald of journalism, 2025

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DOI 10.26577/hj20257846
Data confidence 70 medium
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Abstract

Kazakhstan has already warmed by about +2.9 °C. Central Asia is heating faster than the global average. In this context, clear and explanatory climate journalism is vital for shaping public understanding and informing new policy design. This research article therefore, examines how often climate topics appeared in Egemen Qazaqstan and The New York Times from 2015 to 2025, and how that frequency changed year by year. The study’s scholarly contribution is to provide, for the first time in Kazakhstan, a stable, long-term quantitative dataset in this field. Its practical value lies in offering comparable indicators that editors and policymakers can use for communication planning. Methodologically, data were collected in Python using expanded climate-related keywords in Kazakh and English and were grouped by year. Descriptive and quantitative comparisons were conducted for each annual indicator. This design enables tracking of long-term dynamics. The main results reveal both a clear gap in publication volumes and a shared upward trend. Over the decade, Egemen Qazaqstan published 1,843 climate-related articles, while The New York Times published 63,408. Attention increased in both outlets. The Kazakhstani newspaper showed small, event-driven increments and then a sharp rise in recent years (peaking in 2024), whereas the U.S. outlet maintained a high and steady baseline. This suggests not merely coverage triggered by news events but an established editorial practice. The study’s value is in providing a decade-long quantitative climate dataset for Kazakh-language journalism and a foundation for future coding by frames, sources, and topics. Practically, newsrooms can plan a permanent “climate/environment” section, strengthen channels with scientists, meteorologists, and relevant authorities, and increase explanatory, solutions-oriented reporting grounded in local examples.