In the rapidly evolving landscape of information ecosystems, the proliferation of harmful speech—including hate speech and misinformation—has become a critical challenge with profound societal implications. Contemporary digital platforms, augmented by algorithmic targeting and structural impediments such as paywalls, are reshaping how information is disseminated and consumed. These changes are not merely technological but deeply sociopolitical, affecting diverse communities in manifold ways. As a result, the prevailing approach among practitioners—primarily relying on ‘social listening’ to detect information harms—is increasingly being recognized as inadequate. Experts argue for more robust, integrative models that blend online data with offline realities, fostering responsive and context-aware interventions to mitigate damage.
The limitations of social listening reside in its reactive nature and narrow scope, often capturing only fragments of the digital discourse without grounding in broader sociocultural contexts. Traditional social listening tools analyze content through algorithmic filters, trending hashtags, or keyword monitoring, but these techniques fall short when confronting subtle or emergent forms of dangerous speech. For instance, misinformation campaigns frequently evolve into sophisticated narratives tailored to exploit community-specific vulnerabilities, often escaping surface-level detection. Meanwhile, hate speech may manifest in coded language or dog whistles, eluding automated surveillance boundaries. This fractured detection landscape not only undermines timely intervention but also risks exacerbating mistrust in digital platforms and public institutions.
Recognizing the complexities inherent in today’s information ecosystems, researchers Wardle and Scales have proposed an innovative framework termed the Community-Centered Exploration, Engagement, and Evaluation (CE3) system. This model is inspired by integrated epidemiological surveillance methodologies, which effectively combine multiple data streams — both quantitative and qualitative — to trace and respond to public health threats. The CE3 system seeks to transpose these principles into the information realm by embedding community voices at the core of harm detection and mitigation processes. This integration promises a deeper understanding of how information harms manifest and propagate within specific social contexts, leveraging localized knowledge that algorithms alone cannot capture.
At the heart of the CE3 system lies the imperative to fuse online and offline data ecosystems, breaking down the artificial silos often created by purely digital monitoring. Integrating offline indicators—such as community reports, ground-level ethnographic observations, and local media analysis—with continuous online monitoring allows for a more nuanced, timely picture of information dynamics. This holistic data incorporation not only enhances the sensitivity of detection but also informs more culturally aligned mitigation strategies, reducing the risk of alienating affected populations. The approach mirrors public health frameworks that combine laboratory data, clinical reports, and social determinants to effectively track disease outbreaks and tailor responses.
One of the critical affordances of digital platforms contributing to information harms is algorithmic targeting. Algorithms optimize content delivery for engagement but often amplify polarizing or sensational content, intensifying divisions and confusion. This ‘attention economy’ logic, built into social media architectures, creates fertile ground for misinformation and hateful narratives to flourish unchecked. Additionally, structural barriers like paywalls restrict access to authoritative information, disproportionately affecting marginalized groups and deepening informational inequities. The CE3 model emphasizes community empowerment to contest these structural limitations, promoting transparency and advocating for democratized access to credible information sources.
Moreover, the CE3 framework foregrounds the participatory role of communities not only in detection but also in response. By centering local knowledge and agency, it challenges the top-down paradigms that have often characterized efforts to address information harms. Community actors become co-creators of surveillance and intervention strategies, tailoring efforts to their unique sociopolitical landscapes. Such involvement fosters trust and legitimacy, which are essential in environments where public skepticism towards both digital platforms and institutional authorities has become widespread. This is particularly pertinent in domains such as public health, election security, and climate communication, where misinformation can have far-reaching consequences.
Public distrust, fueled by repeated failures to effectively counter misinformation and hate speech, represents a significant hurdle that the CE3 model aims to surmount. Traditional models, reliant on reactive detection and platform-driven moderation, have sometimes been perceived as either intrusive or insufficiently responsive, thereby propagating societal cynicism and disengagement. By contrast, a community-centered approach promises transparency, accountability, and culturally sensitive mechanisms that prioritize affected populations’ lived experiences. This recalibration is essential for rebuilding confidence in public discourse and scientific communication, especially in an era marked by the ‘infodemic’ — an overwhelming abundance of information, both accurate and misleading.
Technical implementation of the CE3 system necessitates advanced data integration capabilities, combining automated natural language processing tools with human-in-the-loop analysis. Machine learning classifiers must be calibrated to accommodate community-specific dialects, terminologies, and communication norms to effectively identify nuanced dangerous speech. In parallel, participatory data collection methods, including community reporting platforms and localized surveys, enrich algorithmic insights with qualitative depth. Such hybrid approaches mitigate biases inherent in purely automated systems and ensure a more equitable representation of diverse voices. Furthermore, secure data governance frameworks underpinning the system safeguard privacy while fostering collaborative data sharing among stakeholders.
Importantly, the CE3 model also advances beyond detection to include community-driven evaluation metrics that assess the effectiveness of interventions. Standardized measures designed with community input evaluate changes in information environments, trust levels, and behavioral outcomes over time. This iterative feedback loop ensures that mitigation strategies are adaptive and contextually relevant, maximizing impact while minimizing unintended consequences. Embedding evaluation within the model institutionalizes continuous learning, an attribute currently lacking in many reactive social listening methodologies.
Integrating lessons from epidemic surveillance is particularly illuminating given parallels between misinformation outbreaks and viral pathogens. Both phenomena exhibit rapid spread, mutation, and community-specific vulnerabilities. Epidemiological frameworks employ sentinel networks, cross-sectoral partnerships, and early warning indicators to preempt health crises. These principles translate effectively into the information domain, where sentinel community reporters can provide early alerts about emerging narratives, and partnerships among civil society, academia, and technology providers facilitate coordinated responses. The CE3 approach thereby transforms information harm management into a proactive, systemic endeavor rather than a fragmented, post hoc reaction.
The scalability of the CE3 system is another pivotal consideration. Information harms manifest differently across geographic, cultural, and linguistic contexts, necessitating flexible frameworks adaptable to local conditions. Open-source tools and platform-agnostic architectures ensure that the model can be deployed in varied environments—from rural communities with limited digital infrastructure to urban centers with dense online activity. Importantly, capacity-building initiatives empower local stakeholders with the necessary skills and resources to actively participate in surveillance and response, fostering sustainable, community-led resilience against information harms.
In an era where digital ecosystems increasingly shape societal discourse and democracy itself, the imperative to rethink detection and intervention mechanisms has never been greater. Wardle and Scales’ blueprint for the Community-Centered Exploration, Engagement, and Evaluation system represents a significant paradigm shift. By centering community agency, integrating multi-modal data streams, and drawing on proven epidemiological approaches, the CE3 model offers a promising path toward mitigating the pernicious effects of dangerous speech. Its success hinges on collaborative innovation and a commitment to inclusive, context-aware solutions that bridge the online and offline worlds.
Ultimately, addressing the complex landscape of contemporary information harms demands more than technological fixes; it requires systemic change rooted in social trust and mutual engagement. This new model invites policymakers, platforms, researchers, and communities to reconceptualize their roles—not as isolated actors but as interconnected agents within an ecosystem. The CE3 framework embodies this integrative vision, aiming to restore faith in public information environments and safeguard the social fabric from the divisive consequences of misinformation and hate. As digital ecosystems continue to evolve, such adaptive, community-centered strategies will be essential to ensuring resilient and equitable information futures.
Subject of Research: Community-centered approaches to detecting and mitigating information harms in digital ecosystems, drawing on epidemiological surveillance frameworks.
Article Title: Advocating for a community-centred model for responding to potential information harms.
Article References:
Wardle, C., Scales, D. Advocating for a community-centred model for responding to potential information harms.
Nat Hum Behav (2025). https://doi.org/10.1038/s41562-025-02233-2
Image Credits: AI Generated
