ReviewNature human behaviour2025
Advocating for a community-centred model for responding to potential information harms.
Review in Nature human behaviour, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- The role of AI in combating misinformation: leveraging text mining and social networking analysis.Scientific reports · 2026Article
- Impacts of Sexual and Reproductive Health and Rights Misinformation in Digital Spaces on Human Rights Protection and Promotion: Scoping Review.JMIR infodemiology · 2025Article
- Health promotion in the algorithmic age: recognizing the information environment as a determinant of health.Health promotion international · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Various characteristics of contemporary information ecosystems, including types of dangerous speech (hate speech and misinformation), affordances such as algorithmic targeting and structural barriers such as paywalls, are potentially causing harms to different communities. The current focus by practitioners on 'social listening' as the primary mechanism for detecting these harms is flawed, and we argue that mechanisms that effectively integrate and contextualize both offline and online data streams are required. We therefore outline a blueprint for a new model, the Community-Centered Exploration, Engagement, and Evaluation system. It draws on lessons learned from integrated epidemiological surveillance systems that merge multiple data streams. Such an approach can help detect and mitigate potential information harms, integrating community participation and response at its core. This community-driven model is designed to counteract the growing public distrust across a range of issues including public health, election integrity and climate.
Indexed as
Identifiers
40523957What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.