Papers for

public health communication teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Health influencer videos assessed for credibility using linked arguments

GRID: Modeling Health Influencer Discourse as Argument Networks for Credibility Assessment

Abstract: Health-related discussions on social media are increasingly shaped by influencers who offer diverse, and often competing perspectives on the same issue. Yet, such content is typically evaluated in isolation, without considering the broader discourse in which it appears. We introduce GRID (Grounded Reasoning over Interconnected Discourse), a framework for evaluating influencer content through collective inference over the surrounding discourse. GRID represents each video as an argumentation graph connecting claims to its central message, or takeaway, links related claims and takeaways across videos, grounds claims in scientific evidence, and jointly infers credibility over the resulting network. We evaluate GRID on an end-to-end health-related content validation task using 569 influencer videos grounded in expert assessments. Our results show that accounting for the broader discourse improves influencer content validation. Finally, we use GRID-inferred credibility to demonstrate how argumentative strategies and moral framing differ between low- and high-credibility content.

Mon 19 OctComputation and Language
The gist
Social media health discussions often include influencers sharing different opinions on the same topic. The authors developed GRID, a system that connects claims made in influencer videos with scientific evidence and other related claims across videos to judge how trustworthy the content is. They tested GRID on hundreds of videos checked by experts and found that looking at the bigger conversation helps decide credibility better. They also used GRID to see how credible and less credible influencers use different argument styles.
Open → 2610.27149v1

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

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.

Wed 23 SeptSocial and Information NetworksComputers and Society
The gist
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.
Open → 2609.28362v1