Bangladeshi news coverage of 2026 measles outbreak grew more negative
Threat Amplified, Blame Restrained: LLM-Assisted Media Framing Analysis of the 2026 Bangladesh Measles Outbreak
Social and Information NetworksComputers and Society
Summary
News reports about the 2026 measles outbreak in Bangladesh became increasingly negative as the crisis worsened, tracking the rising number of deaths rather than new cases. The media focused more on the threat of the outbreak than on blaming specific people or groups, mostly attributing problems to broader system issues. The researchers used a large language model to analyze hundreds of headlines and verified the method with human coders. Their approach offers a way to study how media frame health crises in low- and middle-income countries.
What this means in practice
- •For public health communication teams: Monitor media sentiment in real-time during outbreaks to adjust public messaging and build trust.
- •For journalism editors: Analyze headline framing trends to guide more balanced and responsible reporting during health emergencies.
Authors
Shahan Ahmed
Abstract
How news media frame and emotionally code a public health emergency shapes public risk perception and trust, yet outbreak-coverage dynamics remain understudied for low- and middle-income countries (LMICs). We examine sentiment and stance in English-language Bangladeshi coverage of the 2026 measles outbreak -- the country's most severe in two decades, with over 97,000 suspected cases and 600 deaths across 61 of 64 districts, unfolding after the 2024 change of government and a 2024-2025 vaccine stockout. Using the Internet Archive, we build a reproducible corpus of 403 headlines from seven national outlets (396 in-window in 2026), label them for binary sentiment and four-way stance via a large language model under a locked codebook, and validate against a two-coder human-adjudicated gold standard (n=153; Cohen's kappa=0.89 stance, 0.75 sentiment). Aligned to the DGHS epidemic curve, coverage grew significantly more negative (56% to 88% negative; Cochran-Armitage z=4.12, p<.001) and risk-amplification framing intensified (44% to 84%; z=4.15, p<.001). Media negativity lagged incidence, tracking cumulative mortality. Contrary to the political backdrop, blame remained a minority frame (~9% overall) and was overwhelmingly systemic (32 of 37, 86%) rather than directed at named actors. The pipeline offers a scalable, transparent method for LMIC outbreak-media analysis; Bangladeshi coverage amplified threat far more than it assigned political blame.