We present BioSpark, a system for analogical innovation designed to act as a creativity partner in reducing the cognitive effort in finding, mapping, and creatively adapting diverse inspirations. While prior approaches have focused on initial stages of finding inspirations, BioSpark uses LLMs embedded in a familiar, visual, Pinterest-like interface to go beyond inspiration to supporting users in identifying the key solution mechanisms, transferring them to the problem domain, considering tradeoffs, and elaborating on details and characteristics. To accomplish this BioSpark introduces several novel contributions, including a tree-of-life enabled approach for generating relevant and diverse inspirations, as well as AI-powered cards including 'Sparks' for analogical transfer; 'Trade-offs' for considering pros and cons; and 'Q&A' for deeper elaboration. We evaluated BioSpark through workshops with professional designers and a controlled user study, finding that using BioSpark led to a greater number of generated ideas; those ideas being rated higher in creative quality; and more diversity in terms of biological inspirations used than a control condition. Our results suggest new avenues for creativity support tools embedding AI in familiar interaction paradigms for designer workflows.

With recent advancements in the capabilities of Text-to-Image (T2I) AI models, product designers have begun experimenting with them in their work. However, T2I models struggle to interpret abstract language and the current user experience of T2I tools can induce design fixation rather than a more iterative, exploratory process. To address these challenges, we developed Inkspire, a sketch-driven tool that supports designers in prototyping product design concepts with analogical inspirations and a complete sketch-to-design-to-sketch feedback loop. To inform the design of Inkspire, we conducted an exchange session with designers and distilled design goals for improving T2I interactions. In a within-subjects study comparing Inkspire to ControlNet, we found that Inkspire supported designers with more inspiration and exploration of design ideas, and improved aspects of the co-creative process by allowing designers to effectively grasp the current state of the AI to guide it towards novel design intentions.

Biologically Inspired Design (BID), or Biomimicry, is a problem-solving methodology that applies analogies from nature to solve engineering challenges. For example, Speedo engineers designed swimsuits based on shark skin. Finding relevant biological solutions for real-world problems poses significant challenges, both due to the limited biological knowledge engineers and designers typically possess and to the limited BID resources. Existing BID datasets are hand-curated and small, and scaling them up requires costly human annotations. In this paper, we introduce BARcode (Biological Analogy Retriever), a search engine for automatically mining bio-inspirations from the web at scale. Using advances in natural language understanding and data programming, BARcode identifies potential inspirations for engineering challenges. Our experiments demonstrate that BARcode can retrieve inspirations that are valuable to engineers and designers tackling real-world problems, as well as recover famous historical BID examples. We release data and code; we view BARcode as a step towards addressing the challenges that have historically hindered the practical application of BID to engineering innovation.

Sensemaking in unfamiliar domains can be challenging, demanding considerable user effort to compare different options with respect to various criteria. Prior research and our formative study found that people would benefit from reading an overview of an information space upfront, including the criteria others previously found useful. However, existing sensemaking tools struggle with the “cold-start” problem — it not only requires significant input from previous users to generate and share these overviews, but such overviews may also turn out to be biased and incomplete. In this work, we introduce a novel system, Selenite, which leverages Large Language Models (LLMs) as reasoning machines and knowledge retrievers to automatically produce a comprehensive overview of options and criteria to jumpstart users’ sensemaking processes. Subsequently, Selenite also adapts as people use it, helping users find, read, and navigate unfamiliar information in a systematic yet personalized manner. Through three studies, we found that Selenite produced accurate and high-quality overviews reliably, significantly accelerated users’ information processing, and effectively improved their overall comprehension and sensemaking experience.

Efficiently reviewing scholarly literature and synthesizing prior art are crucial for scientific progress. Yet, the growing scale of publications and the burden of knowledge make synthesis of research threads more challenging than ever.While significant research has been devoted to helping scholars interact with individual papers, building research threads scattered across multiple papers remains a challenge.Most top-down synthesis (and LLMs) make it difficult to personalize and iterate on the output, while bottom-up synthesis is costly in time and effort.Here, we explore a new design space of mixed-initiative workflows.In doing so we develop a novel computational pipeline, Synergi, that ties together user input of relevant seed threads with citation graphs and LLMs, to expand and structure them, respectively.Synergiallows scholars to start with an entire threads-and-subthreads structure generated from papers relevant to their interests, and to iterate and customize on it as they wish. In our evaluation, we find that Synergi helps scholars efficiently make sense of relevant threads, broaden their perspectives, and increases their curiosity. We discuss future design implications for thread-based, mixed-initiative scholarly synthesis support tools.

Large repositories of products, patents and scientific papers offer an opportunity for building systems that scour millions of ideas and help users discover inspirations. However, idea descriptions are typically in the form of unstructured text, lacking key structure that is required for supporting creative innovation interactions. Prior work has explored idea representations that were either limited in expressivity, required significant manual effort from users, or dependent on curated knowledge bases with poor coverage. We explore a novel representation that automatically breaks up products into fine-grained functional aspects capturing the purposes and mechanisms of ideas, and use it to support important creative innovation interactions: functional search for ideas, and exploration of the design space around a focal problem by viewing related problem perspectives pooled from across many products. In user studies, our approach boosts the quality of creative search and inspirations, substantially outperforming strong baselines by 50-60%.

The ever-increasing pace of scientific publication necessitates methods for quickly identifying relevant papers. While neural recommenders trained on user interests can help, they still result in long, monotonous lists of suggested papers. To improve the discovery experience we introduce multiple new methods for augmenting recommendations with textual relevance messages that highlight knowledge-graph connections between recommended papers and a user’s publication and interaction history. We explore associations mediated by author entities and those using citations alone. In a large-scale, real-world study, we show how our approach significantly increases engagement—and future engagement when mediated by authors—without introducing bias towards highly-cited authors. To expand message coverage for users with less publication or interaction history, we develop a novel method that highlights connections with proxy authors of interest to users and evaluate it in a controlled lab study. Finally, we synthesize design implications for future graph-based messages.

The success of Wikipedia and other user-generated content communities has been driven by the openness of recruiting volunteers globally, but this openness has also led to a persistent lack of trust in its content. Despite several attempts at developing trust indicators to help readers more quickly and accurately assess the quality of content, challenges remain for practical deployment to general consumers. In this work we identify and address three key challenges: empirically determining which metrics from prior and existing community approaches most impact reader trust; 2) validating indicator placements and designs that are both compact yet noticed by readers; and 3) demonstrating that such indicators can not only lower trust but also increase perceived trust in the system when appropriate. By addressing these, we aim to provide a foundation for future tools that can practically increase trust in user generated content and the sociotechnical systems that generate and maintain them.

Developers perform online sensemaking on a daily basis, such as researching and choosing libraries and APIs. Prior research has introduced tools that help developers capture information from various sources and organize it into structures useful for subsequent decision-making. However, it remains a laborious process for developers to manually identify and clip content, maintaining its provenance and synthesizing it with other content. In this work, we introduce a new system called Crystalline that automatically collects and organizes information into tabular structures as the user searches and browses the web. It leverages natural language processing to automatically group similar criteria together to reduce clutter, and uses passive behavioral signals such as mouse movement and dwell time to infer what information to collect and how to visualize and prioritize it. Our user study suggests that developers are able to create comparison tables about 20% faster with a 60% reduction in operational cost without sacrificing the quality of the tables.

Reviewing the literature to understand relevant threads of past work is a critical part of research and vehicle for learning. However, as the scientific literature grows the challenges for users to find and make sense of the many different threads of research grow as well. Previous work has helped scholars to find and group papers with citation information or textual similarity using standalone tools or overview visualizations. Instead, in this work we explore a tool integrated into users’ reading process that helps them with leveraging authors’ existing summarization of threads, typically in introduction or related work sections, in order to situate their own work’s contributions. To explore this we developed a prototype that supports efficient extraction and organization of threads along with supporting evidence as scientists read research articles. The system then recommends further relevant articles based on user-created threads. We evaluate the system in a lab study and find that it helps scientists to follow and curate research threads without breaking out of their flow of reading, collect relevant papers and clips, and discover interesting new articles to further grow threads.

People spend a significant amount of time trying to make sense of the internet, collecting content from a variety of sources and organizing it to make decisions and achieve their goals. While humans are able to fluidly iterate on collecting and organizing information in their minds, existing tools and approaches introduce significant friction into the process. We introduce Fuse, a browser extension that externalizes users’ working memory by combining low-cost collection with lightweight organization of content in a compact card-based sidebar that is always available. Fuse helps users simultaneously extract key web content and structure it in a lightweight and visual way. We discuss how these affordances help users externalize more of their mental model into the system (e.g., saving, annotating, and structuring items) and support fast reviewing and resumption of task contexts. Our 22-month public deployment and follow-up interviews provide longitudinal insights into the structuring behaviors of real-world users conducting information foraging tasks.

Consumers conducting comparison shopping, researchers making sense of competitive space, and developers looking for code snippets online all face the challenge of capturing the information they find for later use without interrupting their current flow. In addition, during many learning and exploration tasks, people need to externalize their mental context, such as estimating how urgent a topic is to follow up on, or rating a piece of evidence as a “pro” or “con,” which helps scaffold subsequent deeper exploration. However, current approaches incur a high cost, often requiring users to select, copy, context switch, paste, and annotate information in a separate document without offering specific affordances that capture their mental context. In this work, we explore a new interaction technique called “wiggling,” which can be used to fluidly collect, organize, and rate information during early sensemaking stages with a single gesture. Wiggling involves rapid back-and-forth movements of a pointer or up-and-down scrolling on a smartphone, which can indicate the information to be collected and its valence, using a single, light-weight gesture that does not interfere with other interactions that are already available. Through implementation and user evaluation, we found that wiggling helped participants accurately collect information and encode their mental context with a 58% reduction in operational cost while being 24% faster compared to a common baseline.

Analogies have been central to creative problem-solving throughout the history of science and technology. As the number of scientific articles continues to increase exponentially, there is a growing opportunity for finding diverse solutions to existing problems. However, realizing this potential requires the development of a means for searching through a large corpus that goes beyond surface matches and simple keywords. Here we contribute the first end-to-end system for analogical search on scientific articles and evaluate its effectiveness with scientists’ own problems. Using a human-in-the-loop AI system as a probe we find that our system facilitates creative ideation, and that ideation success is mediated by an intermediate level of matching on the problem abstraction (i.e., high versus low). We also demonstrate a fully automated AI search engine that achieves a similar accuracy with the human-in-the-loop system. We conclude with design implications for enabling automated analogical inspiration engines to accelerate scientific innovation.

Tabs have become integral to browsing the Web yet have changed little since their introduction nearly 20 years ago. In contrast, the internet has gone through dramatic changes, with users increasingly moving from navigating to websites to exploring information across many sources to support online sensemaking. This paper investigates how tabs today are overloaded with a diverse set of functionalities and issues users face when managing them. We interviewed ten information workers asking about their tab management strategies and walk through each open tab on their work computers four times over two weeks. We uncovered competing pressures pushing for keeping tabs open (ranging from interaction to emotional costs) versus pushing for closing them (such as limited attention and resources). We then surveyed 103 participants to estimate the frequencies of these pressures at scale. Finally, we developed design implications for future browser interfaces that can better support managing these pressures.

Despite the increasing complexity and scale of people’s online activities, browser interfaces have stayed largely the same since tabs were introduced in major browsers nearly 20 years ago. The gap between simple tab-based browser interfaces and the complexity of users’ tasks can lead to serious adverse effects – commonly referred to as “tab overload.” This paper introduces a Chrome extension called Tabs.do, which explores bringing a task-centric approach to the browser, helping users to group their tabs into tasks and then organize, prioritize, and switch between those tasks fluidly. To lower the cost of importing, Tabs.do uses machine learning to make intelligent suggestions for grouping users’ open tabs into task bundles by exploiting behavioral and semantic features. We conducted a field deployment study where participants used Tabs.do with their real-life tasks in the wild, and showed that Tabs.do can decrease tab clutter, enabled users to create rich task structures with lightweight interactions, and allowed participants to context-switch among tasks more efficiently.

As the amount of information online continues to grow, a correspondingly important opportunity is for individuals to reuse knowledge which has been summarized by others rather than starting from scratch. However, appropriate reuse requires judging the relevance, trustworthiness, and thoroughness of others' knowledge in relation to an individual's goals and context. In this work, we explore augmenting judgements of the appropriateness of reusing knowledge in the domain of programming, specifically of reusing artifacts that result from other developers' searching and decision making. Through an analysis of prior research on sensemaking and trust, along with new interviews with developers, we synthesized a framework for reuse judgements. The interviews also validated that developers express a desire for help with judging whether to reuse an existing decision. From this framework, we developed a set of techniques for capturing the initial decision maker's behavior and visualizing signals calculated based on the behavior, to facilitate subsequent consumers' reuse decisions, instantiated in a prototype system called Strata. Results of a user study suggest that the system significantly improves the accuracy, depth, and speed of reusing decisions. These results have implications for systems involving user-generated content in which other users need to evaluate the relevance and trustworthiness of that content.

This joint panel between UIST and CSCW brings together leading researchers at the intersection of the conferences-systems researchers in collaborative and social computing-to engage in a discussion and retrospective. Pairs of panelists will represent each decade since the founding of the conferences, sharing a brief retrospective that surveys the most influential papers of that decade, the zeitgeist of the problems that were popular that decade and why, and what each decade's work has to say to the decades that came before and after. The panel is intended as a space to celebrate advances in the field, and reflect on the burdens and opportunities that it faces ahead.

While there is an enormous amount of information online for making decisions such as choosing a product, restaurant, or school, it can be costly for users to synthesize that information into confident decisions. Information for users' many different criteria needs to be gathered from many different sources into a structure where they can be compared and contrasted. The usefulness of each criterion for differentiating potential options can be opaque to users, and evidence such as reviews may be subjective and conflicting, requiring users to interpret each under their personal context. We introduce Mesh, which scaffolds users to iteratively build up a better understanding of both their criteria and options by evaluating evidence gathered across sources in the context of consumer decision-making. Mesh bridges the gap between decision support systems that typically have rigid structures and the fluid and dynamic process of exploratory search, changing the cost structure to provide increasing payoffs with greater user investment. Our lab and field deployment studies found evidence that Mesh significantly reduces the costs of gathering and evaluating evidence and scaffolds decision-making through personalized criteria enabling users to gain deeper insights from data.

Whether figuring out where to eat in an unfamiliar city or deciding which apartment to live in, consumer generated data (i.e. reviews and forum posts) are often an important influence in online decision making. To make sense of these rich repositories of diverse opinions, searchers need to sift through a large number of reviews to characterize each item based on aspects that they care about. We introduce a novel system, SearchLens, where searchers build up a collection of "Lenses" that reflect their different latent interests, and compose the Lenses to find relevant items across different contexts. Based on the Lenses, SearchLens generates personalized interfaces with visual explanations that promotes transparency and enables deeper exploration. While prior work found searchers may not wish to put in effort specifying their goals without immediate and sufficient benefits, results from a controlled lab study suggest that our approach incentivized participants to express their interests more richly than in a baseline condition, and a field study showed that participants found benefits in SearchLens while conducting their own tasks.

Developers spend a significant portion of their time searching for solutions and methods online. While numerous tools have been developed to support this exploratory process, in many cases the answers to developers' questions involve trade-offs among multiple valid options and not just a single solution. Through interviews, we discovered that developers express a desire for help with decision-making and understanding trade-offs. Through an analysis of Stack Overflow posts, we observed that many answers describe such trade-offs. These findings suggest that tools designed to help a developer capture information and make decisions about trade-offs can provide crucial benefits for both the developers and others who want to understand their design rationale. In this work, we probe this hypothesis with a prototype system named Unakite that collects, organizes, and keeps track of information about trade-offs and builds a comparison table, which can be saved as a design rationale for later use. Our evaluation results show that Unakite reduces the cost of capturing tradeoff-related information by 45%, and that the resulting comparison table speeds up a subsequent developer's ability to understand the trade-offs by about a factor of three.

Finding analogical inspirations in distant domains is a powerful way of solving problems. However, as the number of inspirations that could be matched and the dimensions on which that matching could occur grow, it becomes challenging for designers to find inspirations relevant to their needs. Furthermore, designers are often interested in exploring specific aspects of a product-- for example, one designer might be interested in improving the brewing capability of an outdoor coffee maker, while another might wish to optimize for portability. In this paper we introduce a novel system for targeting analogical search for specific needs. Specifically, we contribute an analogical search engine for expressing and abstracting specific design needs that returns more distant yet relevant inspirations than alternate approaches.

People engaged in complex searches such as planning a vacation or understanding their medical symptoms are often overwhelmed by opening and managing many tabs. These challenges are exacerbated as search moves to smartphones and mobile devices where screen real-estate is limited and tasks are frequently suspended, resumed, and interleaved. Rather than continue to utilize tab-based browsing for complex search, we introduce a new way of browsing through a scaffolded interface. The list of search results serves as a mutable workspace, where a user can track progress on a specific information query. The search query serves as a gateway into this workspace, accessed through a task-subtask hierarchy. We instantiate this in the Bento mobile search system and investigate its effectiveness in three studies. We find converging evidence that users were able to make progress on their complex searching tasks with this structure, and find it more organized and easier to revisit.

The availability of large idea repositories (e.g., patents) could significantly accelerate innovation and discovery by providing people inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy, real-world repositories remains a persistent challenge for both humans and computers. Previous approaches include costly hand-created databases that do not scale, or machine-learning similarity metrics that struggle to account for structural similarity, which is central to analogy. In this paper we explore the viability and value of learning simple structural representations. Our approach combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions. We demonstrate that these learned vectors allow us to find analogies with higher precision and recall than traditional methods. In an ideation experiment, analogies retrieved by our models significantly increased people's likelihood of generating creative ideas.

Programmers consistently engage in cognitively demanding tasks such as sense making and decision-making. During the information-foraging process, programmers are growing more reliant on resources available online since they contain masses of crowdsourced information and are easier to navigate. Content available in questions and answers on Stack Overflow presents a unique platform for studying the types of problems encountered in programming and possible solutions. In addition to classifying these questions, we introduce possible visual representations for organizing the gathered information and propose that such models may help reduce the cost of navigating, understanding and choosing solution alternatives.

Scientific discoveries are often driven by finding analogies in distant domains, but the growing number of papers makes it difficult to find relevant ideas in a single discipline, let alone distant analogies in other domains. To provide computational support for finding analogies across domains, we introduce SOLVENT, a mixed-initiative system where humans annotate aspects of research papers that denote their background (the high-level problems being addressed), purpose (the specific problems being addressed), mechanism (how they achieved their purpose), and findings (what they learned/achieved), and a computational model constructs a semantic representation from these annotations that can be used to find analogies among the research papers. We demonstrate that this system finds more analogies than baseline information-retrieval approaches; that annotators and annotations can generalize beyond domain; and that the resulting analogies found are useful to experts. These results demonstrate a novel path towards computationally supported knowledge sharing in research communities.

The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy, real-world repositories remains a persistent challenge for either human or automated methods. Previous approaches include costly hand-created databases that have high relational structure (e.g., predicate calculus representations) but are very sparse. Simpler machine-learning/information-retrieval similarity metrics can scale to large, natural-language datasets, but struggle to account for structural similarity, which is central to analogy. In this paper we explore the viability and value of learning simpler structural representations, specifically, "problem schemas", which specify the purpose of a product and the mechanisms by which it achieves that purpose. Our approach combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions. We demonstrate that these learned vectors allow us to find analogies with higher precision and recall than traditional information-retrieval methods. In an ideation experiment, analogies retrieved by our models significantly increased people's likelihood of generating creative ideas compared to analogies retrieved by traditional methods. Our results suggest a promising approach to enabling computational analogy at scale is to learn and leverage weaker structural representations.

The human mind remains an unparalleled engine of innovation, with its unique ability to make sense of complex information and find deep analogical connections driving progress in science and technology over the past millennia. The recent explosion of online information available in virtually every domain should present an opportunity for accelerating this engine; instead, it threatens to slow it as the information processing limits of individual minds are reached. In this talk I discuss our efforts towards building a universal knowledge accelerator: a system in which the sensemaking people engage in online is captured and made useful for others, leading to virtuous cycles of constantly improving information sources that in turn help people more effectively synthesize and innovate. Approximately 70 billion hours per year in the U.S. alone are spent on complex online sensemaking in domains ranging from scientific literature to health; capturing even a fraction of this could provide significant benefits. We discuss three integrated levels of research that are needed to realize this vision: at the individual level in understanding and capturing higher order cognition; at the computational level in developing new interaction systems and AI partners for human cognition; and at the social level in developing complex and creative crowdsourcing and social computing systems.

Crowdsourced clustering approaches present a promising way to harness deep semantic knowledge for clustering complex information. However, existing approaches have difficulties supporting the global context needed for workers to generate meaningful categories, and are costly because all items require human judgments. We introduce Alloy, a hybrid approach that combines the richness of human judgments with the power of machine algorithms. Alloy supports greater global context through a new "sample and search" crowd pattern which changes the crowd's task from classifying a fixed subset of items to actively sampling and querying the entire dataset. It also improves efficiency through a two phase process in which crowds provide examples to help a machine cluster the head of the distribution, then classify low-confidence examples in the tail. To accomplish this, Alloy introduces a modular "cast and gather" approach which leverages a machine learning backbone to stitch together different types of judgment tasks.

Crowdsourcing offers a powerful new paradigm for online work. However, real world tasks are often interdependent, requiring a big picture view of the difference pieces involved. Existing crowdsourcing approaches that support such tasks -- ranging from Wikipedia to flash teams -- are bottlenecked by relying on a small number of individuals to maintain the big picture. In this paper, we explore the idea that a computational system can scaffold an emerging interdependent, big picture view entirely through the small contributions of individuals, each of whom sees only a part of the whole. To investigate the viability, strengths, and weaknesses of this approach we instantiate the idea in a prototype system for accomplishing distributed information synthesis and evaluate its output across a variety of topics. We also contribute a set of design patterns that may be informative for other systems aimed at supporting big picture thinking in small pieces.

Friendsourcing consists of broadcasting questions and help requests to friends on social networking sites. Despite its potential value, friendsourcing requests often fall on deaf ears. One way to improve response rates and motivate friends to undertake more effortful tasks may be to offer extrinsic rewards, such as money or a gift, for responding to friendsourcing requests. However, past research suggests that these extrinsic rewards can have unintended consequences, including undermining intrinsic motivations and undercutting the relationship between people. To explore the effects of extrinsic reward on friends' response rate and perceived relationship, we conducted an experiment on a new friendsourcing platform - Mobilyzr. Results indicate that large extrinsic rewards increase friends' response rates without reducing the relationship strength between friends. Additionally, the extrinsic rewards allow requesters to explain away the failure of friendsourcing requests and thus preserve their perceptions of relationship ties with friends.

People are more creative at solving difficult design problems when they use relevant examples from outside of the problem's domain as inspirations. However, finding such “outside-the-box” inspirations is difficult, particularly in large idea repositories such as the web, because without guidance people select domains to search based on surface similarity to the problem's domain. In this paper, we demonstrate an approach in which non-experts identify domains that have the potential to yield useful and non-obvious inspirations for solutions. We report an empirical study demonstrating how crowds can generate domains of expertise and that showing people an abstract representation rather than the original problem helps them identify more distant domains. Crowd workers drawing inspirations from the distant domains produced more creative solutions to the original problem than did those who sought inspiration on their own, or drew inspiration from domains closer to or not sharing structural correspondence with the original problem.

Previous work has shown the promise of crowdsourcing analogical idea generation, where distributing the stages of analogical processing across many people can reduce fixation, identify inspirations from more diverse domains, and lead to more creative ideas. However, prior work has only considered problems with a single constraint, while many real-world problems involve multiple constraints. This paper contributes a systematic crowdsourcing approach for eliciting multiple constraints inherent in a problem and using those constraints to find inspirations useful in solving it. To do so we identify methods to elicit useful constraints at different levels of abstraction, and empirical results that identify how the level of abstraction influences creative idea generation. Our results show that crowds find the most useful inspirations when the problem domain is represented abstractly and constraints are represented more concretely.

Online communities, much like companies in the business world, often need to transfer 'best practices' internally from one unit to another to improve their performance. Organizational scholars disagree about how much a recipient unit should modify a best practice when incorporating it. Some evidence indicates that modifying a practice that has been successful in one environment will introduce problems, undercut its effectiveness and harm the performance of the recipient unit. Other evidence, though, suggests that recipients need to adapt the practice to fit their local environment. The current research introduces a contingency perspective on practice transfer, holding that the value of modifications depends on when they are introduced and who introduces them. Empirical research on the transfer of a quality-improvement practice between projects within Wikipedia shows that modifications are more helpful if they are introduced after the receiving project has had experience with the imported practice. Furthermore, modifications are more effective if they are introduced by members who have experience in a variety of other projects.

Patients researching medical diagnoses, scientist exploring new fields of literature, and students learning about new domains are all faced with the challenge of capturing information they find for later use. However, saving information is challenging on mobile devices, where the small screen and font sizes combined with the inaccuracy of finger based touch screens makes it time consuming and stressful for people to select and save text for future use. Furthermore, beyond the challenge of simply selecting a region of bounded text on a mobile device, in many learning and data exploration tasks the boundaries of what text may be relevant and useful later are themselves uncertain for the user. In contrast to previous approaches which focused on speeding up the selection process by making the identification of hard boundaries faster, we introduce the idea of intentionally supporting uncertain input in the context of saving information during complex reading and information exploration. We embody this idea in a system that uses force touch and fuzzy bounding boxes along with posthoc expandable context to support identifying and saving information in an intentionally uncertain way on mobile devices. In a two part user study we find that this approach reduced selection time and was preferred by participants over the default system text selection method.

Low-cost genetic sequencing, coupled with novel social media platforms and visualization techniques, present a new frontier for scientific participation, whereby people can learn, share, and act on data embedded within their own bodies. Our study of 23andMe, a popular genetic testing service, reveals how users make sense of and contextualize their genetic results, critique and evaluate the underlying research, and reflect on the broader implications of genetic testing. We frame user groups as citizen science publicsgroups that coalesce around scientific issues and work towards resolving shared concerns. Our findings show that personal genetics serves as a site for public engagement with science, whereby communities of biological citizens creatively interpret, debate, and act on professional research. We conclude with design trajectories at the intersection of genetics and creativity support tools: platforms for aggregating hybrid knowledge; tools for creative reflection on professional science; and strategies for supporting collaborations across communities.

Learning about a new area of knowledge is challenging for novices partly because they are not yet aware of which topics are most important. The Internet contains a wealth of information for learning the underlying structure of a domain, but relevant sources often have diverse structures and emphases, making it hard to discern what is widely considered essential knowledge vs. what is idiosyncratic. Crowdsourcing offers a potential solution because humans are skilled at evaluating high-level structure, but most crowd micro-tasks provide limited context and time. To address these challenges, we present Crowdlines, a system that uses crowdsourcing to help people synthesize diverse online information. Crowdworkers make connections across sources to produce a rich outline that surfaces diverse perspectives within important topics. We evaluate Crowdlines with two experiments. The first experiment shows that a high context, low structure interface helps crowdworkers perform faster, higher quality synthesis, while the second experiment shows that a tournament-style (parallelized) crowd workflow produces faster, higher quality, more diverse outlines than a linear (serial/iterative) workflow.

Distributed online groups have great potential for generating interdependent and complex products like encyclopedia articles or product design. However, coordinating multiple group members to work together effectively while minimizing process losses remains an open challenge. We conducted an experiment comparing the effectiveness of two coordination strategies (simultaneous vs. sequential work) on a complex creative task as the number of group members increased. Our results indicate that, contrary to prior work, a sequential work structure was more effective than a simultaneous work structure as the size of the group increased. A mediation analysis suggests that social processes such as territoriality partially accounts for these results. A follow up experiment giving workers specific roles mitigated the detrimental effects of the simultaneous work structure. These results have implications for small group theory and crowdsourcing research.

Over the last several years there has been an explosion of powerful, affordable, multi-touch devices. This provides an outstanding opportunity for novel data visualization techniques that leverage new interaction methods and minimize their barriers to entry. In this paper we describe an approach for multivariate data visualization that uses physics-based affordances that are easy to intuit, constraints that are easy to apply and visualize, and a consistent view as data is manipulated in order to promote data exploration and interrogation. We provide a framework for exploring this problem space, and an example proof of concept system called Kinetica. We describe the results of a user study that suggest users of Kinetica were able to explore multiple dimensions of data at once, identify outliers, and discover trends with minimal training.

Seeking solutions from one domain to solve problems in another is an effective process of innovation. This process of analogy searching is difficult for both humans and machines. In this paper, we present a novel approach for re-presenting a problem in terms of its abstract structure, and then allowing people to use this structural representation to find analogies. We propose a crowdsourcing process that helps people navigate a large dataset to find analogies. Through two experiments, we show the benefits of using abstract structural representations to search for ideas that are analogous to a source problem, and that these analogies result in better solutions than alternative approaches. This work provides a useful method for finding analogies, and can streamline innovation for both novices and professional designers.

Harnessing crowds can be a powerful mechanism for increasing innovation. However, current approaches to crowd innovation rely on large numbers of contributors generating ideas independently in an unstructured way. We introduce a new approach called distributed analogical idea generation, which aims to make idea generation more effective and less reliant on chance. Drawing from the literature in cognitive science on analogy and schema induction, our approach decomposes the creative process in a structured way amenable to using crowds. In three experiments we show that distributed analogical idea generation leads to better ideas than example-based approaches, and investigate the conditions under which crowds generate good schemas and ideas. Our results have implications for improving creativity and building systems for distributed crowd innovation.

If the people belong to multiple online communities, their joint membership can influence the survival of each of the communities to which they belong. Communities with many joint memberships may struggle to get enough of their members' time and attention, but find it easy to import best practices from other communities. In this paper, we study the effects of membership overlap on the survival of online communities. By analyzing the historical data of 5673 Wikia communities, we find that higher levels of membership overlap are positively associated with higher survival rates of online communities. Furthermore, we find that it is beneficial for young communities to have shared members who play a central role in other mature communities. Our contributions are two-fold. Theoretically, by examining the impact of membership overlap on the survival of online communities we identified an important mechanism underlying the success of online communities. Practically, our findings may guide community creators on how to effectively manage their members, and tool designers on how to support this task.

Analysts synthesize complex, qualitative data to uncover themes and concepts, but the process is time-consuming, cognitively taxing, and automated techniques show mixed success. Crowdsourcing could help this process through on-demand harnessing of flexible and powerful human cognition, but incurs other challenges including limited attention and expertise. Further, text data can be complex, high-dimensional, and ill-structured. We address two major challenges unsolved in prior crowd clustering work: scaffolding expertise for novice crowd workers, and creating consistent and accurate categories when each worker only sees a small portion of the data. To address these challenges we present an empirical study of a two-stage approach to enable crowds to create an accurate and useful overview of a dataset: A) we draw on cognitive theory to assess how re-representing data can shorten and focus the data on salient dimensions; and B) introduce an iterative clustering approach that provides workers a global overview of data. We demonstrate a classification-plus-context approach elicits the most accurate categories at the most useful level of abstraction.

People spend an enormous amount of time searching for complex information online; for example, consumers researching new purchases or patients learning about their conditions. As they search, people build up rich mental schemas about their target domains; which, if effectively shared, could accelerate learning for others with similar interests. In this paper we introduce a novel approach for integrating the schemas individuals develop as they gather information online and surfacing them for others with similar interests. Through a controlled experiment we show that having access to others' schemas while foraging for information helps new users to induce more useful, prototypical, and better-structured schemas than gathering information alone.

The Internet has the potential to accelerate scientific problem solving by engaging a global pool of contributors. Existing approaches focus on broadcasting problems to many independent solvers. We investigate other approaches that may be advantageous by examining a community for mathematical problem solving -- MathOverflow -- in which contributors communicate and collaborate to solve new mathematical 'micro-problems' online. We contribute a simple taxonomy of collaborative acts derived from a process-level examination of collaborations and a quantitative analysis relating collaborative acts to solution quality. Our results indicate a diversity of ways in which mathematicians are reaching a solution, including by iteratively advancing a solution. A better understanding of such collaborative strategies can inform the design of tools to support distributed collaboration on complex problems.

A significant challenge for crowdsourcing has been increasing worker engagement and output quality. We explore the effects of social, learning, and financial strategies, and their combinations, on increasing worker retention across tasks and change in the quality of worker output. Through three experiments, we show that 1) using these strategies together increased workers' engagement and the quality of their work; 2) a social strategy was most effective for increasing engagement; 3) a learning strategy was most effective in improving quality. The findings of this paper provide strategies for harnessing the crowd to perform complex tasks, as well as insight into crowd workers' motivation.

In modern crowdsourcing markets, requesters face the challenge of training and managing large transient workforces. Requesters can hire peer workers to review others' work, but the value may be marginal, especially if the reviewers lack requisite knowledge. Our research explores if and how workers learn and improve their performance in a task domain by serving as peer reviewers. Further, we investigate whether peer reviewing may be more effective in teams where the reviewers can reach consensus through discussion. An online between-subjects experiment compares the trade-offs of reviewing versus producing work using three different organization strategies: working individually, working as an interactive team, and aggregating individuals into nominal groups. The results show that workers who review others' work perform better on subsequent tasks than workers who just produce. We also find that interactive reviewer teams outperform individual reviewers on all quality measures. However, aggregating individual reviewers into nominal groups produces better quality assessments than interactive teams, except in task domains where discussion helps overcome individual misconceptions.

People spend an enormous amount of time searching for and saving information online. Existing tools capture only a small portion of the cognitive processing a user engages in while making sense of a new domain. In this paper we introduce a novel interface for capturing online information in a structured but lightweight way. We use this interface as a platform to experimentally characterize the costs and benefits of structuring information during the sensemaking process. Our results contribute empirical knowledge relevant to theories of information seeking and sensemaking, and practical implications for the development of tools to capture and share online information.

One of the most significant challenges for many online communities is increasing members' contributions over time. Prior studies on peer feedback in online communities have suggested its impact on contribution, but have been limited by their correlational nature. In this paper, we conducted a field experiment on Wikipedia to test the effects of different feedback types (positive feedback, negative feedback, directive feedback, and social feedback) on members' contribution. Our results characterize the effects of different feedback types, and suggest trade-offs in the effects of feedback between the focal task and general motivation, as well as differences in how newcomers and experienced editors respond to peer feedback. This research provides insights into the mechanisms underlying peer feedback in online communities and practical guidance to design more effective peer feedback systems.

Paid crowd work offers remarkable opportunities for improving productivity, social mobility, and the global economy by engaging a geographically distributed workforce to complete complex tasks on demand and at scale. But it is also possible that crowd work will fail to achieve its potential, focusing on assembly-line piecework. Can we foresee a future crowd workplace in which we would want our children to participate? This paper frames the major challenges that stand in the way of this goal. Drawing on theory from organizational behavior and distributed computing, as well as direct feedback from workers, we outline a framework that will enable crowd work that is complex, collaborative, and sustainable. The framework lays out research challenges in twelve major areas: workflow, task assignment, hierarchy, real-time response, synchronous collaboration, quality control, crowds guiding AIs, AIs guiding crowds, platforms, job design, reputation, and motivation.

Large-scale collaboration systems often separate their content from the deliberation around how that content was produced. Surfacing this deliberation may engender trust in the content generation process if the deliberation process appears fair, well-reasoned, and thorough. Alternatively, it could encourage doubts about content quality, especially if the process appears messy or biased. In this paper we report the results of an experiment where we found that surfacing deliberation generally led to decreases in perceptions of quality for the article under consideration, especially - but not only - if the discussion revealed conflict. The effect size depends on the type of editors' interactions. Finally, this decrease in actual article quality rating was accompanied by self-reported improved perceptions of the article and Wikipedia overall.

We examine the possibility of distributed sensemaking: improving a user's sensemaking by leveraging previous users' work without those users directly collaborating or even knowing one another. We asked users to engage in sensemaking by organizing and annotating web search results into "knowledge maps," either with or without previous users' maps to work from. We also recorded gaze patterns as users examined others' knowledge maps. Our findings show the conditions under which distributed sensemaking can improve sensemaking quality; that a user's sensemaking process is readily apparent to a subsequent user via a knowledge map; and that the organization of content was more useful to subsequent users than the content itself, especially when those users had differing goals. We discuss the role distributed sensemaking can play in schema induction by helping users make a mental model of an information space and make recommendations for new tool and system development.

We report on a study of the English edition of Wikipedia in which we used a mixed methods approach to understand how nested organizational structures called WikiProjects support collaboration. We first conducted two rounds of interviews with a total of 20 Wikipedians to understand how WikiProjects function and what it's like to participate in them from the perspective of Wikipedia editors. We then used a quantitative approach to further explore interpretations that arose from the qualitative data. Our analysis of these data together demonstrates how WikiProjects not only help Wikipedians coordinate tasks and produce articles, but also support community members and small groups of editors in important ways such as: providing a place to find collaborators, socialize and network; protecting editors' work; and structuring opportunities to contribute.

Though toolkits exist to create complex crowdsourced workflows, there is limited support for management of those workflows. Managing crowd workers and tasks requires significant iteration and experimentation on task instructions, rewards, and flows. We present CrowdWeaver, a system to visually manage complex crowd work. The system supports the creation and reuse of crowdsourcing and computational tasks into integrated task flows, manages the flow of data between tasks, and allows tracking and notification of task progress, with support for real-time modification. We describe the system and demonstrate its utility through case studies and user feedback.

Wikipedia's remarkable success in aggregating millions of contributions can pose a challenge for current editors, whose hard work may be reverted unless they understand and follow established norms, policies, and decisions and avoid contentious or proscribed terms. We present a machine learning model for predicting whether a contribution will be reverted based on word level features. Unlike previous models relying on editor-level characteristics, our model can make accurate predictions based only on the words a contribution changes. A key advantage of the model is that it can provide feedback on not only whether a contribution is likely to be rejected, but also the particular words that are likely to be controversial, enabling new forms of intelligent interfaces and visualizations. We examine the performance of the model across a variety of Wikipedia articles.

Traditional research on leadership in online communities has consistently focused on the small set of people occupying leadership roles. In this paper, we use a model of shared leadership, which posits that leadership behaviors come from members at all levels, not simply from people in high-level leadership positions. Although every member can exhibit some leadership behavior, different types of leadership behavior performed by different types of leaders may not be equally effective. This paper investigates how distinct types of leadership behaviors (transactional, aversive, directive and person-focused) and the legitimacy of the people who deliver them (people in formal leadership positions or not) influence the contributions that other participants make in the context of Wikipedia. After using propensity score matching to control for potential pre-existing differences among those who were and were not targets of leadership behaviors, we found that 1) leadership behaviors performed by members at all levels significantly influenced other members' motivation; 2) transactional leadership and person-focused leadership were effective in motivating others to contribute more, whereas aversive leadership decreased other contributors' motivations; and 3) legitimate leaders were in general more influential than regular peer leaders. We discuss the theoretical and practical implication of our work.

A challenge for many online production communities is to direct their members to accomplish tasks that are important to the group, even when these tasks may not match individual members' interests. Here we investigate how combining group identification and direction setting can motivate volunteers in online communities to accomplish tasks important to the success of the group as a whole. We hypothesize that group identity, the perception of belonging to a group, triggers in-group favoritism; and direction setting (including explicit direction from group goals and implicit direction from role models) focuses people's group-oriented motivation towards the group's important tasks. We tested our hypotheses in the context of Wikipedia's Collaborations of the Week (COTW), a group goal setting mechanism and a social event within Wikiprojects. Results demonstrate that 1) publicizing important group goals via COTW can have a strong motivating influence on editors who have voluntarily identified themselves as group members compared to those who have not self-identified; 2) the effects of goals spill over to non-goal related tasks; and 3) editors exposed to group role models in COTW are more likely to perform similarly to the models on group-relevant citizenship behaviors. Finally, we discuss design and managerial implications based on our findings.

Crowdsourcing has become a powerful paradigm for accomplishing work quickly and at scale, but involves significant challenges in quality control. Researchers have developed algorithmic quality control approaches based on either worker outputs (such as gold standards or worker agreement) or worker behavior (such as task fingerprinting), but each approach has serious limitations, especially for complex or creative work. Human evaluation addresses these limitations but does not scale well with increasing numbers of workers. We present CrowdScape, a system that supports the human evaluation of complex crowd work through interactive visualization and mixed initiative machine learning. The system combines information about worker behavior with worker outputs, helping users to better understand and harness the crowd. We describe the system and discuss its utility through grounded case studies. We explore other contexts where CrowdScape's visualizations might be useful, such as in user studies.

Extracting useful knowledge from large network datasets has become a fundamental challenge in many domains, from scientific literature to social networks and the web. We introduce Apolo, a system that uses a mixed-initiative approach - combining visualization, rich user interaction and machine learning - to guide the user to incrementally and interactively explore large network data and make sense of it. Apolo engages the user in bottom-up sensemaking to gradually build up an understanding over time by starting small, rather than starting big and drilling down. Apolo also helps users find relevant information by specifying exemplars, and then using a machine learning method called Belief Propagation to infer which other nodes may be of interest. We evaluated Apolo with twelve participants in a between-subjects study, with the task being to find relevant new papers to update an existing survey paper. Using expert judges, participants using Apolo found significantly more relevant papers. Subjective feedback of Apolo was also very positive.

Although science is becoming increasingly collaborative, there are remarkably few success stories of online collaborations between professional scientists that actually result in real discoveries. A notable exception is the Polymath Project, a group of mathematicians who collaborate online to solve open mathematics problems. We provide an in-depth descriptive history of Polymath, using data analysis and visualization to elucidate the principles that led to its success, and the difficulties that must be addressed before the project can be scaled up. We find that although a small percentage of users created most of the content, almost all users nevertheless contributed some content that was highly influential to the task at hand. We also find that leadership played an important role in the success of the project. Based on our analysis, we present a set of design suggestions for how future collaborative mathematics sites can encourage and foster newcomer participation.

In this paper, we introduce a method to measure shared leadership in Wikipedia as a step in developing a new model of online leadership. We show that editors with varying degrees of engagement and from peripheral as well as central roles all act like leaders, but that core and peripheral editors show different profiles of leadership behavior. Specifically, we developed machine learning models to automatically identify four types of leadership behaviors from 4 million messages sent between Wikipedia editors. We found strong evidence of shared leadership in Wikipedia, with editors in peripheral roles producing a large proportion of leadership behaviors.

We present APOLO, a system that uses a mixed-initiative approach to help people interactively explore and make sense of large network datasets. It combines visualization, rich user interaction and machine learning to engage the user in bottom-up sensemaking to gradually build up an understanding over time by starting small, rather than starting big and drilling down. APOLO helps users find relevant information by specifying exemplars, and then using a machine learning method called Belief Propagation to infer which other nodes may be of interest. We demonstrate APOLO's usage and benefits using a Google Scholar citation graph, consisting of 83,000 articles (nodes) and 150,000 citations relationships. A demo video of APOLO is available at http://www.cs.cmu.edu/~dchau/apolo/apolo.mp4.

Micro-task markets such as Amazon's Mechanical Turk represent a new paradigm for accomplishing work, in which employers can tap into a large population of workers around the globe to accomplish tasks in a fraction of the time and money of more traditional methods. However, such markets have been primarily used for simple, independent tasks, such as labeling an image or judging the relevance of a search result. Here we present a general purpose framework for accomplishing complex and interdependent tasks using micro-task markets. We describe our framework, a web-based prototype, and case studies on article writing, decision making, and science journalism that demonstrate the benefits and limitations of the approach.

Detecting and correcting low quality submissions in crowdsourcing tasks is an important challenge. Prior work has primarily focused on worker outcomes or reputation, using approaches such as agreement across workers or with a gold standard to evaluate quality. We propose an alternative and complementary technique that focuses on the way workers work rather than the products they produce. Our technique captures behavioral traces from online crowd workers and uses them to predict outcome measures such quality, errors, and the likelihood of cheating. We evaluate the effectiveness of the approach across three contexts including classification, generation, and comprehension tasks. The results indicate that we can build predictive models of task performance based on behavioral traces alone, and that these models generalize to related tasks. Finally, we discuss limitations and extensions of the approach.

In a world of widespread information access, information can overwhelm collaborators, even with visualizations to help. We extend prior work to study the effect of shared information on collaboration. We analyzed the success and discussion process of remote pairs trying to identify a serial killer in multiple crime cases. Each partner had half of the evidence, or each partner had all the available evidence. Pairs also used one of three tools: spreadsheet only (control condition), unshared visualizations, or shared visualization. Visualizations improved analysis over the control condition but this improvement depended on how much evidence each partner had. When each partner possessed all the evidence with visualizations, discussion flagged and pairs showed evidence of more confirmation bias. They discussed fewer hypotheses and persisted on the wrong hypothesis. We discuss the possible reasons for this phenomenon and implications for design of remote collaboration systems to incorporate awareness of intermediate processes important to collaborative success.

Online production groups have the potential to transform the way that knowledge is produced and disseminated. One of the most widely used forms of online production is the wiki, which has been used in domains ranging from science to education to enterprise. We examined the development of and interactions between coordination and conflict in a sample of 6811 wiki production groups. We investigated the influence of four coordination mechanisms: intra-article communication, inter-user communication, concentration of workgroup structure, and policy and procedures. We also examined the growth of conflict, finding the density of users in an information space to be a significant predictor. Finally, we analyzed the effectiveness of the four coordination mechanisms on managing conflict, finding differences in how each scaled to large numbers of contributors. Our results suggest that coordination mechanisms effective for managing conflict are not always the same as those effective for managing task quality, and that designers must take into account the social benefits of coordination mechanisms in addition to their production benefits.

This paper examines the location traces of 489 users of a location sharing social network for relationships between the users' mobility patterns and structural properties of their underlying social network. We introduce a novel set of location-based features for analyzing the social context of a geographic region, including location entropy, which measures the diversity of unique visitors of a location. Using these features, we provide a model for predicting friendship between two users by analyzing their location trails. Our model achieves significant gains over simpler models based only on direct properties of the co-location histories, such as the number of co-locations. We also show a positive relationship between the entropy of the locations the user visits and the number of social ties that user has in the network. We discuss how the offline mobility of users can have implications for both researchers and designers of online social networks.

Wikipedia is an online encyclopedia which has undergone tremendous growth. However, this same growth has made it difficult to characterize its content and coverage. In this paper we develop measures to map Wikipedia using its socially annotated, hierarchical category structure. We introduce a mapping technique that takes advantage of socially-annotated hierarchical categories while dealing with the inconsistencies and noise inherent in the distributed way that they are generated. The technique is demonstrated through two applications: mapping the distribution of topics in Wikipedia and how they have changed over time; and mapping the degree of conflict found in each topic area. We also discuss the utility of the approach for other applications and datasets involving collaboratively annotated category hierarchies.

The success of Wikipedia has demonstrated the power of peer production in knowledge building. However, unlike many other examples of collective intelligence, tasks in Wikipedia can be deeply interdependent and may incur high coordination costs among editors. Increasing the number of editors increases the resources available to the system, but it also raises the costs of coordination. This suggests that the dependencies of tasks in Wikipedia may determine whether they benefit from increasing the number of editors involved. Specifically, we hypothesize that adding editors may benefit low-coordination tasks but have negative consequences for tasks requiring a high degree of coordination. Furthermore, concentrating the work to reduce coordination dependencies should enable more efficient work by many editors. Analyses of both article ratings and article review comments provide support for both hypotheses. These results suggest ways to better harness the efforts of many editors in social collaborative systems involving high coordination tasks.

User studies are important for many aspects of the design process and involve techniques ranging from informal surveys to rigorous laboratory studies. However, the costs involved in engaging users often requires practitioners to trade off between sample size, time requirements, and monetary costs. Micro-task markets, such as Amazon's Mechanical Turk, offer a potential paradigm for engaging a large number of users for low time and monetary costs. Here we investigate the utility of a micro-task market for collecting user measurements, and discuss design considerations for developing remote micro user evaluation tasks. Although micro-task markets have great potential for rapidly collecting user measurements at low costs, we found that special care is needed in formulating tasks in order to harness the capabilities of the approach.

Wikis are collaborative systems in which virtually anyone can edit anything. Although wikis have become highly popular in many domains, their mutable nature often leads them to be distrusted as a reliable source of information. Here we describe a social dynamic analysis tool called WikiDashboard which aims to improve social transparency and accountability on Wikipedia articles. Early reactions from users suggest that the increased transparency afforded by the tool can improve the interpretation, communication, and trustworthiness of Wikipedia articles.

Wikipedia's success is often attributed to the large numbers of contributors who improve the accuracy, completeness and clarity of articles while reducing bias. However, because of the coordination needed to write an article collaboratively, adding contributors is costly. We examined how the number of editors in Wikipedia and the coordination methods they use affect article quality. We distinguish between explicit coordination, in which editors plan the article through communication, and implicit coordination, in which a subset of editors structure the work by doing the majority of it. Adding more editors to an article improved article quality only when they used appropriate coordination techniques and was harmful when they did not. Implicit coordination through concentrating the work was more helpful when many editors contributed, but explicit coordination through communication was not. Both types of coordination improved quality more when an article was in a formative stage. These results demonstrate the critical importance of coordination in effectively harnessing the "wisdom of the crowd" in online production environments.

Wikipedia has become one of the most important information resources on the Web by promoting peer collaboration and enabling virtually anyone to edit anything. However, this mutability also leads many to distrust it as a reliable source of information. Although there have been many attempts at developing metrics to help users judge the trustworthiness of content, it is unknown how much impact such measures can have on a system that is perceived as inherently unstable. Here we examine whether a visualization that exposes hidden article information can impact readers' perceptions of trustworthiness in a wiki environment. Our results suggest that surfacing information relevant to the stability of the article and the patterns of editor behavior can have a significant impact on users' trust across a variety of page types.

Wikipedia, a wiki-based encyclopedia, has become one of the most successful experiments in collaborative knowledge building on the Internet. As Wikipedia continues to grow, the potential for conflict and the need for coordination increase as well. This article examines the growth of such non-direct work and describes the development of tools to characterize conflict and coordination costs in Wikipedia. The results may inform the design of new collaborative knowledge systems.