Abstract As digitally enabled information systems play an increasingly central role in culture and economics, their negative consequences have become apparent. This guest editorial addresses the urgent need for information scientists to take a more deliberate stance in designing and guiding the evolution of these systems. We propose a framework for conceptualizing “healthier information ecosystems” by drawing on theories from complex systems and ecological research, grounded in a value‐oriented approach. The article reviews key concepts from systems science, complex systems, and ecology, with a focus on ecosystem and adaptation research. These perspectives offer analytical approaches for decomposing information ecosystems and provide a foundation for understanding “health” in the context of evolving, open systems. Unlike natural ecosystems, information ecosystems must be evaluated according to human values; thus, we articulate a set of values as a starting point for defining health in this context. By introducing insights from beyond the field of information systems, we aim to instigate scholarly dialog, connect prior work in new ways, and reveal new opportunities for research and intervention. This connective and argumentative contribution is intended to guide future research and identify solutions to the proliferating problems in our current information ecosystems.

The dynamics of charitable donor co-attendance networks can help fundraisers assess and improve fundraising outcomes. To improve understanding of donor-giving patterns, this study examines a large, multi-year network describing the co-attendance of donors at charitable fundraising events. We analyze the dynamics of co-attendance networks based on their topological structure, shift in node characteristics, and various network properties. Among other results, we observe a 76% increase in giving value for donors that showed increased centrality rank over nonoverlapped snapshots. In the data we examined, 19.14% of the donors whose giving increased and 16.24% of donors that remained in the same giving range exhibited increased co-attendance with high-capacity donors, whereas none of the donors that shifted to a lower class exhibited increased co-attendance with high-capacity donors over the periods, potentially illustrating a positive peer effect on donors. Some similarity was also observed in the giving characteristics of donors who co-attend events, with a 0.211 assortativity coefficient for the giving class of donors as a characteristic of donors when considering network dynamics using a rolling window size of 3 years. This is followed by analyzing the group-level similarities that reveal an interlinked clique of communities with diverse sizes. Our results show that large communities have a higher fraction of wealthy donors.

There is growing concern about misinformation and the role online media plays in social polarization. Analyzing belief dynamics is one way to enhance our understanding of these problems. Existing analytical tools, such as sur-vey research or stance detection, lack the power to corre-late contextual factors with population-level changes in belief dynamics. In this exploratory study, I present the Belief Landscape Framework, which uses data about people’s professed beliefs in an online setting to measure belief dynamics with more temporal granularity than previous methods. I apply the approach to conversations about climate change on Twitter and provide initial validation by comparing the method’s output to a set of hypotheses drawn from the literature on dynamic systems. My analysis indicates that the method is relatively robust to different parameter settings, and results suggest that 1) there are many stable configurations of belief on the polarizing issue of climate change and 2) that people move in predictable ways around these points. The method paves the way for more powerful tools that can be used to understand how the modern digital media eco-system impacts collective belief dynamics and what role misinformation plays in that process.

While many technology-based approaches to support people living with HIV target specific clinical goals, recent work has begun to consider how to design support in the context of HIV stigma. Here, we consider two challenges; the first, and central challenge is how to work with a small group of stakeholders to design for the much larger, but hard to access, population of HIV-positive individuals. Addressing the first challenge, we introduce the Narrative Tapestry design process, which is our main contribution, and helps create a generative third space wherein stakeholders draw on their cultural knowledge to reflect on common experiences of living with HIV. The second challenge is how to design a platform that is less likely to disintegrate as people transition through phases of living with a stigmatized identity. Applying the Narrative Tapestry process led us to insights that both demonstrate the value of the design process and partially address this second challenge. We find that social support can be a critical lifeline following an HIV diagnosis, but that when people have normalized their identity, this need can give way to a desire to address stigma directly. We propose combining social support tools with a set of features that enable people to work as change-agents to address stigma in their local communities. We argue that this type of platform would help to retain senior members who can serve both as community caretakers as well as role models for newcomers.

Online social support communities can significantly improve health outcomes for individuals living with disease. Although they are well studied in the literature, little research examines how sociotechnical design changes influence the sustainability of support communities for different medical conditions. We compare the impact of a single design change on 49 disease‐specific health support forums hosted on the WebMD platform, a popular online health information service. A statistical analysis showcases changes in posting patterns before and after the design intervention; a subsequent interpretive examination of forum content reveals how the design change affected members' perceived affordances of the platform. Our findings suggest that, despite differences between communities, the design change triggered a common set of cascading effects: it made it difficult for core users to create and maintain relationships, that led them to ultimately leave the site, and, in turn, reduced the activity drawing newcomers to the platform. Using these findings, we argue that the design of sustainable and robust online communities must account for systemic, sociotechnical dynamics.

Social support can significantly improve health outcomes for individuals living with disease, and online forums have emerged as an important vehicle for social support. Whereas research has focused on the delivery and use of social support, little is known about how these communities are sustained. We describe one sociotechnical mechanism that enables sustainable communities to provide social support to a large number of people. We focus upon thirteen disease-specific discussion forums hosted by the WebMD online health community. In these forums, small, densely connected cores of members who maintain strong relationships generate the majority of support for others. Through content analysis we find they provide informational support to a large number of more itinerant members, but provide one another with community support. Based on these observations, we describe a sociotechnical mechanism of online support that is distinct from non-support oriented communities, and has implications for the design of self-sustaining online support systems.

In this article, we present an analysis of communication transcripts from computer-mediated teams that illustrates how different kinds of decision support impact collaborative knowledge construction. Our analysis introduces an algorithmic technique called Topic Evolution Analysis (TEvA), which tracks clusters of words in conversation, and illustrates how these clusters change and merge over time. This analysis is combined with measurements of group dynamics to distinguish between teams using different kinds of decision support.

Group decision support systems (GDSS) hold significant potential for improving decision making, but they have not been broadly adopted. One reason for this is that these platforms introduce representational work for users that is distinct from a more familiar deliberative interaction but they offer uncertain payoff. This article presents a study with a platform that addresses this problem by leveraging the argumentative structure of deliberative conversation to drive a decision support algorithm. The platform uses argument visualization to mediate the collaborators' conversation. The study demonstrates that the platform addresses a known deficiency in human information pooling called the common knowledge phenomenon.

We describe an approach to building adaptive groupware systems. This approach encompasses a methodology that reduces the complexity of inferring user intent by identifying a domain-specific interface language that both supports the user's maintenance of common ground, and can be used to drive an adaptive component.Our approach can be framed as follows: 1) Users of same-time different-place collaborative systems must exchange certain types coordination specific information; 2) We can facilitate the exchange and management of this information by introducing special purpose interface components, which we call Coordinating Representations, that structure these communications; 3) Information that is collected through these interface components is particularly well suited to driving intent inferencing procedures; and 4) Intent inference can be used to drive adaptive components that support the collaborative activity.We discuss empirical results from two experiments that validate this methodology.

The analysis of network traffic and customer usage patterns is critical for the network operation, capacity planning and targeted marketing of the cellular industry. This data analysis task presents both an opportunity and a challenge for data warehousing technology because of the huge amount of wireless calling data and the dynamic nature of the usage reports. We have developed a data-warehousing system to address the issues of both the performance and the flexibility of the usage analysis reports. Our system collects traffic data from a wireless network and provides the following functionalities: basic reporting, dynamic modeling, customer profiling and usage forecasting. This paper focuses on the usage analysis reporting for the traffic by an individual customer or a group of customers on a subset of a network or the network as a whole. Based on the traffic usage, it describes three different usage forecasting models for capacity planning. A combined application of data warehousing and statistical analysis to a customer calling plan selection is also presented.