Display Omitted We model usage patterns of five different ontology-engineering projects.Users work in micro-workflows and specific user-roles can be identified.Class hierarchy influences users' edit behavior.Users edit ontologies top-down, breadth-first and prefer closely related classes.Users perform property-based workflows. Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the International Classification of Diseases, which is currently under active development by the World Health Organization contains nearly 50 , 000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, ontology-engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding the way these different stakeholders collaborate will enable us to improve editing environments that support such collaborations. In this paper, we uncover how large ontology-engineering projects, such as the International Classification of Diseases in its 11th revision, unfold by analyzing usage logs of five different biomedical ontology-engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users frequently change after specific given ones) that suggest that large collaborative ontology-engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, ontology editors, developers and contributors working on collaborative ontology-engineering projects and tools in the biomedical domain.
More recent research on collaborative authoring systems, such as Wikipedia, focuses on describing and defining not only the act of collaboration amongst strangers and uncertain situations that contribute to a digital good @cite_21 but also on antagonism and sabotage of said systems @cite_28 . It has also been discovered only recently that Wikipedia editors are slowly but steadily declining @cite_19 . Therefore have analyzed what impact reverts have on new editors of Wikipedia. showed that an increase in participation can be achieved by directly delegating specific tasks to contributors. As simple as this approach may appear, the identification of work (and thus specific tasks) is still a tedious and time-consuming process, which can only partly be automated due to its assigned complexity.
@cite_28 @cite_19 @cite_21
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Unlike traditional firms, open collaborative systems rely on volunteers to operate, and many communities struggle to maintain enough contributors to ensure the quality and quantity of content. However, Wikipedia has historically faced the exact opposite problem: too much participation, particularly from users who, knowingly or not, do not share the same norms as veteran Wikipedians. During its period of exponential growth, the Wikipedian community developed specialized socio-technical defense mechanisms to protect itself from the negatives of massive participation: spam, vandalism, falsehoods, and other damage. Yet recently, Wikipedia has faced a number of high-profile issues with recruiting and retaining new contributors. In this paper, we first illustrate and describe the various defense mechanisms at work in Wikipedia, which we hypothesize are inhibiting newcomer retention. Next, we present results from an experiment aimed at increasing both the quantity and quality of editors by altering various elements of these defense mechanisms, specifically pre-scripted warnings and notifications that are sent to new editors upon reverting or rejecting contributions. Using logistic regressions to model new user activity, we show which tactics work best for different populations of users based on their motivations when joining Wikipedia. In particular, we found that personalized messages in which Wikipedians identified themselves in active voice and took direct responsibility for rejecting an editor’s contributions were much more successful across a variety of outcome metrics than the current messages, which typically use an institutional and passive voice. Open collaboration systems, such as Wikipedia, need to maintain a pool of volunteer contributors to remain relevant. Wikipedia was created through a tremendous number of contributions by millions of contributors. However, recent research has shown that the number of active contributors in Wikipedia has been declining steadily for years and suggests that a sharp decline in the retention of newcomers is the cause. This article presents data that show how several changes the Wikipedia community made to manage quality and consistency in the face of a massive growth in participation have ironically crippled the very growth they were designed to manage. Specifically, the restrictiveness of the encyclopedia’s primary quality control mechanism and the algorithmic tools used to reject contributions are implicated as key causes of decreased newcomer retention. Furthermore, the community’s formal mechanisms for norm articulation are shown to have calcified against changes—especially changes proposed by newer editors. 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. Prior scholarship on Wikipedia's collaboration processes has examined the properties of either editors or articles, but not the interactions between both. We analyze the coauthorship network of Wikipedia articles about breaking news demanding intense coordination and compare the properties of these articles and the editors who contribute to them to articles about contemporary and historical events. Using p* ERGM methods to test a multi-level, multi-theoretical model, we identify how editors' attributes and editing patterns interact with articles' attributes and authorship history. Editors' attributes like prior experience have a stronger influence on collaboration patterns, but article attributes also play significant roles. Finally, we discuss the implications our findings and methods have for understanding the socio-material duality of collective intelligence systems beyond Wikipedia.