Joshua Introne

The Climate CoLab: Large scale model-based collaborative planning

The Climate CoLab is a system to help thousands of people around the world collectively develop plans for what humans should do about global climate change. This paper shows how the system combines three design elements (model-based planning, on-line debates, and electronic voting) in a synergistic way. The paper also reports early usage experience showing that: (a) the system is attracting a continuing stream of new and returning visitors from all over the world, and (b) the nascent community can use the platform to generate interesting and high quality plans to address climate change. These initial results indicate significant progress towards an important goal in developing a collective intelligence system—the formation of a large and diverse community collectively engaged in solving a single problem.

Using shared representations to improve coordination and intent inference

In groupware, users must communicate about their intentions and aintain common knowledge via communication channels that are explicitly designed into the system. Depending upon the task, generic communication tools like chat or a shared whiteboard may not be sufficient to support effective coordination. We have previously reported on a methodology that helps the designer develop task specific communication tools, called coordinating representations, for groupware systems. Coordinating representations lend structure and persistence to coordinating information. We have shown that coordinating representations are readily adopted by a user population, reduce coordination errors, and improve performance in a domain task. As we show in this article, coordinating representations present a unique opportunity to acquire user information in collaborative, user-adapted systems. Because coordinating representations support the exchange of coordinating information, they offer a window onto task and coordination-specific knowledge that is shared by users. Because they add structure to communication, the information that passes through them can be easily exploited by adaptive technology. This approach provides a simple technique for acquiring user knowledge in collaborative, user-adapted systems. We document our application of this approach to an existing groupware system. Several empirical results are provided. First, we show how information that is made available by a coordinating representation can be used to infer user intentions. We also show how this information can be used to mine free text chat for intent information, and show that this information further enhances intent inference. Empirical data shows that an automatic plan generation component, which is driven by information from a coordinating representation, reduces coordination errors and cognitive effort for its users. Finally, our methodology is summarized, and we present a framework for comparing our approach to other strategies for user knowledge acquisition in adaptive systems.

Solving Wicked Social Problems with Socio-computational Systems

Global climate change is one of the most challenging problems humanity has ever faced. Fortunately, a new way of solving large, complex problems has become possible in just the last decade or so. Examples like Wikipedia and Linux illustrate how the work of thousands of people can be combined in ways that would have been impossible only a few years ago. Inspired by systems like these, we developed the Climate CoLab—a global, on-line platform in which thousands of people around the world work together to create, analyze, and ultimately select detailed plans for what we humans can do about global climate change.The Climate CoLab has been operating since November 2009, and has an active community of thousands of users. In this article, we outline some of the challenges faced in developing the system, describe our current solutions to these problems, and report on our experiences.

Analyzing the flow of knowledge in computer mediated teams

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. Our analysis offers evidence that some kinds of decision support improve the apparent rationality of a team, but at the cost of collaborative knowledge construction. This result is not apparent when simply measuring team decision performance. We use this finding to motivate the utility and importance of the approach when assessing the impact of technology on collaborative knowledge processing.

A Sociotechnical Mechanism for Online Support Provision

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.

Wireless usage analysis for capacity planning and beyond: a data warehouse approach

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.

A data fusion approach to biosurveillance

A significant hurdle in biosurveillance is how to detect attacks with unknown bioagents. One reason for this is that there is a high degree of variance in medical reporting in such cases, masking statistical anomalies that might otherwise be apparent. In this article, we present an application that employs a novel two-level fusion architecture designed to contend with this problem. The lower-level fusion step detects and tracks the indication of an outbreak of some sort given a set of noisy patient records, based on an information retrieval technique called latent semantic analysis. The higher level fusion step then determines the type of outbreak, based on dynamic Bayesian networks that model cause-effect interrelationships among several sources of information such as terrorist activities, environment, diseases and symptoms. We have developed and demonstrated feasibility of the approach via simulated outbreak events provided by the BioWar simulation platform.

Taming a Menagerie of Heavy Tails with Skew Path Analysis

The discovery of stable, heavy-tailed distributions of activity on the web has inspired many researchers to search for simple mechanisms that can cut through the complexity of countless social interactions to yield powerful new theories about human behavior. A dominant mode of investigation involves fitting a mathematical model to an observed distribution, and then inferring the behaviors that generate the modeled distribution. Yet, distributions of activity are not always stable, and the process of fitting a mathematical model to empirical distributions can be highly uncertain, especially for smaller and highly variable datasets. In this paper, we introduce an approach called skew-path analysis, which measures how concentrated information production is along different dimensions in community-generated data. The approach scales from small to large datasets, and is suitable for investigating the dynamics of online behavior. We offer a preliminary demonstration of the approach by using it to analyze six years of data from an online health community, and show that the technique offers interesting insights into the dynamics of information production. In particular, we find evidence for two distinct point attractors within a subset of the forums analyzed, demonstrating the utility of the approach.

How People Weave Online Information Into Pseudoknowledge

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.

Leveraging a better interface language to simplify adaptation

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 users 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.