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.

It has been observed that real-world social networks often exhibit stratification along economic or other lines, with consequences for class mobility and access to opportunities. With the rise in human interaction data and extensive use of online social networks, the structure of social networks (representing connections between individuals) can be used for measuring stratification. However, although stratification has been studied extensively in the social sciences, there is no single, generally applicable metric for measuring the level of stratification in a network. In this work, we first propose the novel Stratification Assortativity (StA) metric, which measures the extent to which a network is stratified into different tiers. Then, we use the StA metric to perform an in-depth analysis of the stratification of five co-authorship networks. We examine the evolution of these networks over 50 years and show that these fields demonstrate an increasing level of stratification over time, and, correspondingly, the trajectory of a researcher's career is increasingly correlated with her entry point into the network.

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.

Recent research on conspiracy theories labels conspiracism as a distinct and deficient epistemic process. However, the tendency to pathologize conspiracism obscures the fact that it is a diverse and dynamic collective sensemaking process, transacted in public on the web. Here, we adopt a narrative framework to introduce a new analytical approach for examining online conspiracism. Narrative plays an important role because it is central to human cognition as well as being domain agnostic, and so can serve as a bridge between conspiracism and other modes of knowledge production. To illustrate the utility of our approach, we use it to analyze conspiracy theories identified in conversations across three different anti-vaccination discussion forums. Our approach enables us to capture more abstract categories without hiding the underlying diversity of the raw data. We find that there are dominant narrative themes across sites, but that there is also a tremendous amount of diversity within these themes. Our initial observations raise the possibility that different communities play different roles in the collective construction of conspiracy theories online. This offers one potential route for understanding not only cross-sectional differentiation, but the longitudinal dynamics of the narrative in future work. In particular, we are interested to examine how activity within the framework of the narrative shifts in response to news events and social media platforms’ nascent efforts to control different types of misinformation. Such analysis will help us to better understand how collectively constructed conspiracy narratives adapt in a shifting media ecosystem.

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.

Incorporating relevant stakeholder input into conservation decision making is fundamentally challenging yet critical for understanding both the status of, and human pressures on, natural resources. Collective intelligence (CI), defined as the ability of a group to accomplish difficult tasks more effectively than individuals, is a growing area of investigation, with implications for improving ecological decision making. However, many questions remain about the ways in which emerging internet technologies can be used to applyCIto natural resource management. We examined how synchronous social-swarming technologies and asynchronous "wisdom of crowds" techniques can be used as potential conservation tools for estimating the status of natural resources exploited by humans. Using an example from a recreational fishery, we show that theCIof a group of anglers can be harnessed through cyber-enabled technologies. We demonstrate how such approaches - as compared against empirical data - could provide surprisingly accurate estimates that align with formal scientific estimates. Finally, we offer a practical approach for using resource stakeholders to assist in managing ecosystems, especially in data-poor situations.

Sustainable management of natural resources requires adequate scientific knowledge about complex relationships between human and natural systems. Such understanding is difficult to achieve in many contexts due to data scarcity and knowledge limitations. We explore the potential of harnessing the collective intelligence of resource stakeholders to overcome this challenge. Using a fisheries example, we show that by aggregating the system knowledge held by stakeholders through graphical mental models, a crowd of diverse resource users produces a system model of social–ecological relationships that is comparable to the best scientific understanding. We show that the averaged model from a crowd of diverse resource users outperforms those of more homogeneous groups. Importantly, however, we find that the averaged model from a larger sample of individuals can perform worse than one constructed from a smaller sample. However, when averaging mental models within stakeholder-specific subgroups and subsequently aggregating across subgroup models, the effect is reversed. Our work identifies an inexpensive, yet robust way to develop scientific understanding of complex social–ecological systems by leveraging the collective wisdom of non-scientist stakeholders.

Worldwide, interest in research on methods to define access to healthy food at the local level has grown, given its central connection to carrying out a healthy lifestyle. Within this research domain, papers have examined the spatial element of food access, or individual perceptions about the food environment. To date, however, no studies have provided a method for linking a validated, objective measure of the food environment with qualitative data on how people access healthy food in their community. In this study, we present a methodology for linking scores from a modified Nutrition Environment Measures Survey in Stores (conducted at every store in our study site of Flint, Michigan) with perceptions of the acceptability of food stores and shopping locations drawn from seven focus groups (n = 53). Spatial analysis revealed distinct patterns in visiting and avoidance of certain store types. Chain stores tended to be rated more highly, while stores in neighborhoods with more African-American or poor residents were rated less favorably and avoided more frequently. Notably, many people avoided shopping in their own neighborhoods; participants traveled an average of 3.38 miles to shop for groceries, and 60% bypassed their nearest grocery store when shopping. The utility of our work is threefold. First, we provide a methodology for linking perceived and objective definitions of food access among a small sample that could be replicated in cities across the globe. Second, we show links between perceptions of food access and objectively measured food store scores to uncover inequalities in access in our sample to illustrate potential connections. Third, we advocate for the use of such data in informing the development of a platforms that aim to make the process of accessing healthy food easier via non-food retail based interventions. Future work can replicate our methods to both uncover patterns in distinct food environments and aid in advocacy around how to best intervene in the food environment in various locales.

Online tech support communities have become valuable channels for users to seek and provide solutions to specific problems. From the resource exchange perspective, the sustainability of a social system is contingent upon the size of its members as well as their communication activities. To further extend the resource-based model, the current research identifies a variety of social roles in a large tech support Q&A forum and examines longitudinal changes in the community's structure based on the identification. Moreover, this study also investigates the relationship between the community's functionality and its traffic. Results suggest that the proportion of unsolved questions negatively impacts the number of future incoming questions and the outcome of a given question is not only dependent on users' interactions within the discussion, but also on the community activities preceding the question. These observations can help community managers to improve system design and task allocation.

Online health support forums utilize straightforward online discussion designs to create a sociotechnical space where people can seek social support from others. The advice generated in these forums exists as an archival resource for future health information seekers. The present study uses mixed methods to investigate how invisible social processes lead advice to be adapted over time by forum members. Drawing on the construct of ‘reification’ from the communities of practice (COP) literature, we operationalize the reification of advice (RoA) as a process by which advice is developed across multiple discussion threads, and construct an algorithmic procedure to extract posts that trace this process. We evaluate our algorithm with crowd-workers, and perform an inductive, qualitative analysis to identify different modes of advice reification. We suggest that RoA could be used as the basis of a mid-level theory that treats online support communities and bundles of advice trajectories embedded in a shifting sociotechnical context. In our closing analysis, we propose that our approach might be a first step in an algorithmic procedure for assessing advice quality, drawing on the idea that reified advice may be considered a product of the collective intelligence of an online health support community.

The participants in a joint activity must work hard to maintain coordination. For complicated and/or novel activities, even more talk is needed to proceed. Over time, for recurrent cooperative behaviors, the participants will organize their talk as a means of organizing their actions. The main work of this talk is to explore the ramifications of, and methodology for, introducing coordinating representations into same-time/different-place computer-mediated cooperative activities. Groupware facilitates communication, coordination, and collaboration of group effort. Building a groupware system requires a detailed analysis of the work environment in which it will be deployed and extensive work on designing both the interface as it presents itself to the individual user and the mediated interaction among the users. Where a shared virtual whiteboard is an external media that can be used to support all kinds of social interaction, a coordinating representation is one kind of content realized in an external media.

Misinformation has found a new natural habitat in the digital age. Thousands of forums, blogs, and alternative news sources amplify fake news and inaccurate information to such a degree that it impacts our collective intelligence. Researchers and policy makers are troubled by misinformation because it is presumed to energize or even carry false narratives that can motivate poor decision-making and dangerous behaviors. Yet, while a growing body of research has focused on how viral misinformation spreads, little work has examined how false narratives are in fact constructed. In this study, we move beyond contagion inspired approaches to examine how people construct a false narrative. We apply prior work in cognitive science on narrative understanding to illustrate how the narrative changes over time and in response to social dynamics, and examine how forum participants draw upon a diverse set of online sources to substantiate the narrative. We find that the narrative is based primarily on reinterpretations of conventional and scholarly sources, and then used to provide an alternate account of unfolding events. We conclude that the link between misinformation, conventional knowledge, and false narratives is more complex than is often presumed, and advocate for a more direct study of this relationship.

Participating in online health communities for informational support can benefit patients in various ways. For the online communities to be sustainable and effective for their participants, membership retention and commitment are important. This study explores how informational support requesting and providing by users holding different social roles (core user and periphery user) are related with participants' retention in the community. We first crawled six years of data in the WebMD fibromyalgia forum with around 200,000 posts and 10,000 users. Then a supervised machine learning model is trained and validated to automatically identify the requesting and providing informational support posts exchanged between the members in the community. Lastly, survival analysis was employed to quantify how the informational support requesting and providing by different social roles predicts the member's continued participation in the online community. The results reveal the different influencing mechanism of requesting and providing support from different social roles on the patients' decision to stay in the community. The findings can aid in the design of better support mechanisms to enhance member commitment in online health communities.

Misinformation has found a new natural habitat in the digital age. Thousands of forums, blogs, and alternative news sources amplify inaccurate information to such a degree that it impacts our collective intelligence. Widespread misinformation is troubling, not just because it is wrong, but also because it can persist in the face of attempts to correct it, and thus becomes part of a larger culture of community-based pseudoknowledge (PK). Prior work has focused on the motivations and psychology of those who create and maintain PK but has neglected inspection of the dynamics of collective PK production itself. In this exploratory case study, we illustrate how the active participation of multiple collaborators adapts PK over time through a process we liken to participatory storytelling. We argue that the Internet provides a uniquely well-suited environment for evolving PK that is "more fit", in that it is more engaging, easier to defend, and possibly easier to spread.

Acute socio-environmental crises often expose systemic problems that are linked by failures in management, environmental, or social systems. If recovery efforts are to address these systemic problems, these issues and the concerns of those impacted by the crisis need to be clearly articulated, rationally represented, and communicated to those responsible for the recovery. Although participatory approaches to crisis recovery often use environmental modeling, explicit ways in which stakeholders' narratives and experiences can be translated into computer-based models for scenario analysis are not readily available to modelers or decision-makers. We present an approach to translating community narratives about crisis events using a free Fuzzy Cognitive Mapping software called Mental Modeler (www.mentalmodeler.org). We applied this process to the recent water crisis in Flint, Michigan, and demonstrate how participatory modeling can give communities a way to structure their thoughts, develop recovery actions, and communicate with those in charge of crisis recovery efforts.

The impacts of the Flint Water Crisis (FWC) present municipal and state officials, emergency responders, community organizations, and residents with considerable uncertainties about how to reorganize and respond in the wake of tragedy. In addition to the collapse of infrastructure and governance systems, the community is experiencing a collapse of its communication and knowledge-sharing networks, specifically between those directly impacted by the crisis and those involved in the emergency response. In this article, we summarize what we learned from a community engagement process that took place in the winter and spring of 2016 after widespread acknowledgment of the FWC and review the ( 1) results of five ''participatory modeling'' workshops with residents carried out in the city of Flint and ( 2) results of a follow-up cultural consensus survey administered to Flint residents and FWC responders engaged in the recovery to evaluate the degree of agreement among actors about the dynamics of the FWC. The modeling exercise revealed that Flint residents perceive that long-term racial and economic marginalization and political disenfranchisement led to the FWC. Cultural consensus data indicate that nonresidents are less likely to share this view about the causes of the crisis; however, there was more agreement between Flint residents and nonresidents around the consequences of, and solutions to, the FWC. Agreement around potential solutions is encouraging, but if recovery efforts fail to address Flint residents' underlying concerns about long-term marginalization and disenfranchisement, there is a risk of further erosion of trust and communication between residents, state officials, and emergency responders.

Including stakeholders in environmental model building and analysis is an increasingly popular approach to understanding ecological change. This is because stakeholders often hold valuable knowledge about socio-environmental dynamics and collaborative forms of modeling produce important boundary objects used to collectively reason about environmental problems. Although the number of participatory modeling (PM) case studies and the number of researchers adopting these approaches has grown in recent years, the lack of standardized reporting and limited reproducibility have prevented PM's establishment and advancement as a cohesive field of study. We suggest a four-dimensional framework (4P) that includes reporting on dimensions of (1) the Purpose for selecting a PM approach (the why); (2) the Process by which the public was involved in model building or evaluation (the how); (3) the Partnerships formed (the who); and (4) the Products that resulted from these efforts (the what). We highlight four case studies that use common PM software-based approaches (fuzzy cognitive mapping, agent-based modeling, system dynamics, and participatory geospatial modeling) to understand human-environment interactions and the consequences of ecological changes, including bushmeat hunting in Tanzania and Cameroon, agricultural production and deforestation in Zambia, and groundwater management in India. We demonstrate how standardizing communication about PM case studies can lead to innovation and new insights about model-based reasoning in support of ecological policy development. We suggest that our 4P framework and reporting approach provides a way for new hypotheses to be identified and tested in the growing field of PM.