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co-authorship is included as keyword or extra keyword in 0 datasets, 0 tools and 15 publications.
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|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Breaking news on Wikipedia: Dynamics, structures, and roles in high-tempo collaboration||Brian C. Keegan||English||2012||The goal of my research is to evaluate how distributed virtual teams are able to use socio-technical systems like Wikipedia to self-organize and respond to complex tasks. I examine the roles Wikipedians adopt to synthesize content about breaking news events out of a noisy and complex information space. Using data from Wikipedia's revision histories as well as from other sources like IRC logs, I employ methods in content analysis, statistical network analysis, and trace ethnography to illuminate the multilevel processes which sustain these temporary collaborations as well as the dynamics of how they emerge and dissolve.||0||0|
|Do editors or articles drive collaboration? Multilevel statistical network analysis of wikipedia coauthorship||Brian Keegan
|English||2012||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.||0||1|
|Link prediction in a bipartite network using Wikipedia revision information||Chang Y.-J.
|Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012||English||2012||We consider the problem of link prediction in the bipartite network of Wikipedia. Bipartite stands for an important class in social networks, and many unipartite networks can be reinterpreted as bipartite networks when edges are modeled as vertices, such as co-authorship networks. While bipartite is the special case of general graphs, common link prediction function cannot predict the edge occurrence in bipartite graph without any specialization. In this paper, we formulate an undirected bipartite graph using the history revision information in Wikipedia. We adapt the topological features to the bipartite of Wikipedia, and apply a supervised learning approach to our link prediction formulation of the problem. We also compare the performance of link prediction model with different features.||0||0|
|The co-creation machine: Managing co-creative processes for the crowd||Debenham J.
|Lecture Notes in Computer Science||English||2012||Co-creative processes have spawned successes such as Wikipedia. They are also used to draw innovative ideas from consumers to producers, and from voters to government. This paper describes the initial stages of a collaboration between two Sydney-based universities to build a customisable co-creative process management system. The system has embedded intelligence that will make it easy and enjoyable to use. It will enable these powerful systems to be quickly deployed on the Internet to the benefit of the universities as well as industry and government. The innovation in the design of this project is that it is founded on normative multiagent systems that are an established technology for (business) process management but have yet to be deployed to support the co-creative process.||0||0|
|Co-authorship 2.0: Patterns of collaboration in Wikipedia||Tasso David Laniado||Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia||2011||The study of collaboration patterns in wikis can help shed light on the process of content creation by online communities. To turn a wiki's revision history into a collaboration network, we propose an algorithm that identifies as authors of a page the users who provided the most of its relevant content, measured in terms of quantity and of acceptance by the community. The scalability of this approach allows us to study the English Wikipedia community as a co-authorship network. We find evidence of the presence of a nucleus of very active contributors, who seem to spread over the whole wiki, and to interact preferentially with inexperienced users. The fundamental role played by this elite is witnessed by the growing centrality of sociometric stars in the network. Isolating the community active around a category, it is possible to study its specific dynamics and most influential authors.||0||3|
|Co-authorship 2.0: patterns of collaboration in Wikipedia||David Laniado
|Hypertext||English||2011||The study of collaboration patterns in wikis can help shed light on the process of content creation by online communities. To turn a wiki's revision history into a collaboration network, we propose an algorithm that identifies as authors of a page the users who provided the most of its relevant content, measured in terms of quantity and of acceptance by the community. The scalability of this approach allows us to study the English Wikipedia community as a co-authorship network. We find evidence of the presence of a nucleus of very active contributors, who seem to spread over the whole wiki, and to interact preferentially with inexperienced users. The fundamental role played by this elite is witnessed by the growing centrality of sociometric stars in the network. Isolating the community active around a category, it is possible to study its specific dynamics and most influential authors.||0||3|
|Information Quality in Wikipedia: The Effects of Group Composition and Task Conflict||Ofer Arazy
|J. Manage. Inf. Syst.||English||2011||0||2|
|Information quality in wikipedia: The effects of group composition and task conflict||Ofer Arazy
|Journal of Management Information Systems||English||2011||The success of Wikipedia demonstrates that self-organizing production communities can produce high-quality information-based products. Research on Wikipedia has proceeded largely atheoretically, focusing on (1) the diversity in members' knowledge bases as a determinant of Wikipedia's content quality, (2) the task-related conflicts that occur during the collaborative authoring process, and (3) the different roles members play in Wikipedia. We develop a theoretical model that explains how these three factors interact to determine the quality of Wikipedia articles. The results from the empirical study of 96 Wikipedia articles suggest that (1) diversity should be encouraged, as the creative abrasion that is generated when cognitively diverse members engage in task-related conflict leads to higher-quality articles, (2) task conflict should be managed, as conflict-notwithstanding its contribution to creative abrasion-can negatively affect group output, and (3) groups should maintain a balance of both administrative- and content-oriented members, as both contribute to the collaborative process. © 2011 M.E. Sharpe, Inc.||0||2|
|Co-creation of value in IT service processes using semantic MediaWiki||Schmidt R.
|Lecture Notes in Business Information Processing||English||2010||Enterprises are substituting their own IT-Systems by services provided by external providers. This provisioning of services may be done in an industrialized way, separating the service provider from the consumer. However, using industrialized services diminishes the capability to differentiate from competitors. To counter this, collaborative service processes based on the co-creation of value between service providers and prosumers are of huge importance. The approach presented shows how the co-creation of value in IT-service processes can profit from social software, using the example of the Semantic MediaWiki.||0||0|
|Writeslike.us: Linking people through OAI Metadata||Tonkin E.||ELPUB 2010 - Publishing in the Networked World: Transforming the Nature of Communication, 14th International Conference on Electronic Publishing||English||2010||Informal scholarly communication is an important aspect of discourse both within research communities and in dissemination and reuse of data and findings. Various tools exist that are designed to facilitate informal communication between researchers, such as social networking software, including those dedicated specifically for academics. Others make use of existing information sources, in particular structured information such as social network data (e.g. FOAF) or bibliographic data, in order to identify links between individuals; co-authorship, membership of the same organisation, attendance at the same conferences, and so forth. Writeslike.us is a prototype designed to support the aim of establishing informal links between researchers. It makes use of data harvested from OAI repositories as an initial resource. This raises problems less evident in the use of more consistently structured data. The information extracted is filtered using a variety of processes to identify and benefit from systematic features in the data. Following this, the record is analysed for subject, author name, and full text link or source; this is spidered to extract full text, where available, to which is applied a formal metadata extraction package, extracting several relevant features ranging from document format to author email address/citations. The process is supported using data from Wikipedia. Once available, this information may be explored using both graph and matrix-based approaches; we present a method based on spreading activation energy, and a similar mechanism based on cosine similarity metrics. A number of prototype interfaces/data access methods are described, along with relevant use cases, in this paper.||0||0|
|Wikinomics and its discontents: A critical analysis of Web 2.0 business manifestos||Van Dijck J.
|New Media and Society||English||2009||Collaborative culture', 'mass creativity' and 'co-creation' appear to be contagious buzzwords that are rapidly infecting economic and cultural discourse on Web 2.0. Allegedly, peer production models will replace opaque, top-down business models, yielding to transparent, democratic structures where power is in the shared hands of responsible companies and skilled, qualified users. Manifestos such as Wikinomics (Tapscott and Williams, 2006) and 'We-Think' (Leadbeater, 2007) argue collective culture to be the basis for digital commerce. This article analyzes the assumptions behind this Web 2.0 newspeak and unravels how business gurus try to argue the universal benefits of a democratized and collectivist digital space. They implicitly endorse a notion of public collectivism that functions entirely inside commodity culture. The logic of Wikinomics and 'We-Think' urgently begs for deconstruction, especially since it is increasingly steering mainstream cultural theory on digital culture.||0||0|
|Wikis as Digital Ecosystems: An Analysis Based on Authorship||Robert P. Biuk-Aghai
Libby Veng-Sam Tang
|Third IEEE International Conference on Digital Ecosystems and Technologies (DEST 2009), Istanbul, Turkey, 31 May - 3 June 2009||2009||Wikis, best represented by the popular and highly successfulWikipedia system, have established themselves as important componentsof a collaboration infrastructure. We suggest that the complex networkof user-contributors in volunteer-contributed wikis constitutes adigital ecosystem that bears all the characteristics typical of suchsystems. This paper presents an analysis supporting this notion basedon significance of authorship within the wiki. Our findings confirm thehypothesis that large volunteer-contributed wikis are digitalecosystems, and thus that the findings from the digital ecosystemsresearch stream are applicable to this type of system.||0||0|
|Wikis as digital ecosystems: An analysis based on authorship||Biuk-Aghai R.P.
|2009 3rd IEEE International Conference on Digital Ecosystems and Technologies, DEST '09||English||2009||Wikis, best represented by the popular and highly successful Wikipedia system, have established themselves as important components of a collaboration infrastructure. We suggest that the complex network of user-contributors In volunteer-contributed wikis constitutes a digital ecosystem that bears all the characteristics typical of such systems. This paper presents an analysis supporting this notion based on significance of authorship within the wiki. Our findings confirm the hypothesis that large volunteer-contributed wikis are digital ecosystems, and thus that the findings from the digital ecosystems research stream are applicable to this type of system.||0||0|
|A Method for Measuring Co-authorship Relationships in MediaWiki||Libby Veng-Sam Tang
Robert P. Biuk-Aghai
|WikiSym||English||2008||Collaborative writing through wikis has become increasingly popular in recent years. When users contribute to a wiki article they implicitly establish a co-authorship relationship. Discovering these relationships can be of value, for example in finding experts on a given topic. However, it is not trivial to determine the main co-authors for a given author among the potentially thousands who have contributed to a given author’s edit history. We have developed a method and algorithm for calculating a co-authorship degree for a given pair of authors. We have implemented this method as an extension for the MediaWiki system and demonstrate its performance which is satisfactory in the majority of cases. This paper also presents a method of determining an expertise group for a chosen topic.||5||2|
|Visualizing co-authorship networks in online Wikipedia||Biuk-Aghai R.P.||2006 International Symposium on Communications and Information Technologies, ISCIT||English||2006||The Wikipedia online user-contributed encyclopedia has rapidly become a highly popular and widely used online reference source. However, perceiving the complex relationships in the network of articles and other entities in Wikipedia is far from easy. We introduce the notion of using co-authorship of articles to determine relationship between articles, and present the WikiVis information visualization system which visualizes this and other types of relationships in the Wikipedia database in 3D graph form. A 3D star layout and a 3D nested cone tree layout are presented for displaying relationships between entities and between categories, respectively. A novel 3D pinboard layout is presented for displaying search results.||0||2|