During community decision-making and civic collaboration, conflicts can escalate when people suspect misinformation. We introduce the concept of sense of misinformation as experiencing someone's language or behavior as misinformation when it is not, that is to say when no falsehood is involved. Misinformation and sense of misinformation feel similar and can have similar social consequences; but sense of misinformation rests upon a mistaken perception of someone else's information as false. Through a case study of a casino proposal in local community, we examine how sense of misinformation developed over time during a contentious civic process through key factors (i.e., miscoordination governance, miscommunication between local government and citizens, and conflict and the breakdown of civic discourse), undermining trust and community democracy. Distinguishing between misinformation and sense of misinformation presents a challenge, but it is important. We contribute a conceptual distinction to the misinformation literature by identifying this distinct phenomenon and discuss ways to help communities recognize and repair such misattributions. Finally, we discuss design approaches for mitigating sense of misinformation.

Water pollution remains a critical global challenge, threatening public health and aquatic ecosystems. Governmental efforts to monitor and manage water resources often face limitations due to constrained resources and socio-political priorities that may not prioritize sustainable solutions. Citizen science has emerged as a promising approach, engaging communities in scientific research to expand data collection capabilities and foster environmental stewardship. This paper explores the role of distributed collaboration infrastructure, exemplified by the Water Data Collaborative (WDC), in connecting and enhancing citizen-based water quality monitoring groups across North America. Through participatory design sessions with WDC users, the study identifies essential design features needed to support collaboration and address common challenges such as data standardization and resource sharing. Our findings emphasize the complexity of relationships among citizen science groups, government entities, and higher-order organizations, emphasizing the need for scalable, integrated solutions that avoid creating new silos within the ecosystem. This research contributes insights into how CSCW and HCI can facilitate effective citizen science practices and infrastructure design to advance sustainable water management.

Citizen science project leaders collecting field data in a hyperlocal community often face common socio-technical challenges, which can potentially be addressed by sharing innovations across different groups through peer-to-peer collaboration. However, most citizen science groups practice in isolation, and end up re-inventing the wheel when it comes to addressing these common challenges. This study seeks to investigate distributed collaboration between different water monitoring citizen science groups. We discovered a unique social network application called Water Reporter that mediated distributed collaboration by creating more visibility and transparency between groups using the app. We interviewed 8 citizen science project leaders who were users of this app, and 6 other citizen science project leaders to understand how distributed collaboration mediated by this app differed from collaborative practices of Non Water Reporter users. We found that distributed collaboration was an important goal for both user groups, however, the tasks that support these collaboration activities differed for the two user groups.

Abstract Access to clean water is a critical challenge and opportunity for community-level collaboration. People rely on local water sources, but awareness of water quality and participation in water management is often limited. Lack of community engagement can increase risks of water catastrophes, such as those in Flint, Michigan, and Cape Town, South Africa. We investigated water quality practices in a watershed system serving c.100 000 people in the United States. We identified a range of entities including government and nonprofit citizen groups that gather water quality data. Many of these data are accessible in principle to citizens. However, the data are scattered and diverse; information infrastructures are primitive and not integrated. Water quality data and data practices are hidden in plain sight. Based on fieldwork, we consider sociotechnical courses of action, drawing on best practices in human–computer interaction and community informatics, data and environmental systems management.