Advocating for a community-centred model for responding to potential information harms

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.