Many Web platforms rely on user collaboration to generate high-quality content: Wiki, Q&A communities, etc. Understanding and modeling the different collaborative behaviors is therefore critical. However, collaboration patterns are difficult to capture when the relationships between users are not directly observable, since they need to be inferred from the user actions. In this work, we propose a solution to this problem by adopting a systemic view of collaboration. Rather than modeling the users as independent actors in the system, we capture their coordinated actions with embedding methods which can, in turn, identify shared objectives and predict future user actions. To validate our approach, we perform a study on a dataset comprising more than 16M user actions, recorded on the online collaborative sandbox Reddit r place. Participants had access to a drawing canvas where they could change the color of one pixel at every fixed time interval. Users were not grouped in teams nor were given any specific goals, yet they organized themselves into a cohesive social fabric and collaborated to the creation of a multitude of artworks. Our contribution in this paper is multi-fold: i) we perform an in-depth analysis of the Reddit r place collaborative sandbox, extracting insights about its evolution over time; ii) we propose a predictive method that captures the latent structure of the emergent collaborative efforts; and iii) we show that our method provides an interpretable representation of the social structure.
Networks of collaboration between scientists have been studied by Newman @cite_11 . The authors argue that simple unweighted networks are unable to capture the strength of collaboration ties and propose a method to model the strength of collaboration by relying on the number of co-authored papers. The same author has later studied various properties of such networks @cite_11 . @cite_7 have studied collaboration networks from an evolving and self-organizing perspective. Behavioral experiments on the ability to solve problems collaboratively have been conducted by Kearns @cite_26 . Online collaboration with different network topologies has been studied by Suri and Watts @cite_9 . The exploration-exploitation trade-off in a collaborative problem solving task has been discussed by Mason and Watts @cite_19 . Kittur and Kraut @cite_17 studied various types of collaboration taking place between Wikipedia editors and measured the impact on quality of the resulting articles.
@cite_26 @cite_7 @cite_9 @cite_17 @cite_19 @cite_11
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The popular h-index used to measure scientific output can be described in terms of a pool of evaluated objects (the papers), a quality function on the evaluated objects (the number of citations received by each paper) and a sentencing line crossing the origin, whose intersection with the graph of the quality function yields the index value (in the h-index this is a line with slope 1). Based on this abstraction, we present a new index, the c-index, in which the evaluated objects are the citations received by an author, a group of authors, a journal, etc., the quality function of a citation is the collaboration distance between the authors of the cited and the citing papers, and the sentencing line can take slopes between 0 and . As a result, the new index counts only those citations which are significant enough, where significance is proportional to collaboration distance. Several advantages of the new c-index with respect to previous proposals are discussed. Using data from computer databases of scientific papers in physics, biomedical research, and computer science, we have constructed networks of collaboration between scientists in each of these disciplines. In these networks two scientists are considered connected if they have coauthored one or more papers together. We have studied many statistical properties of our networks, including numbers of papers written by authors, numbers of authors per paper, numbers of collaborators that scientists have, typical distance through the network from one scientist to another, and a variety of measures of connectedness within a network, such as closeness and betweenness. We further argue that simple networks such as these cannot capture the variation in the strength of collaborative ties and propose a measure of this strength based on the number of papers coauthored by pairs of scientists, and the number of other scientists with whom they worked on those papers. Using a selection of our results, we suggest a variety of possible ways to answer the question, "Who is the best connected scientist?" We present insights from a bibliometric analysis and scientific paper publication mining of 551 papers in Requirements Engineering (RE) series of conference (11 years from 2005 to 2015). We study cross-disciplinary and interdisciplinary nature of RE re- search by analyzing the cited disciplines in the reference section of each paper. We apply topic modeling on a corpus consisting of 551 abstracts and extract topics as frequently co-occurring and connected terms. We use topic modeling to study the structure and composition of RE research and analyze popular topics in industry as well as research track. Co-authorship in papers is an indicator of collaboration and interaction between scientists as well as institutions and we analyze co-authorship data to in- vestigate university-industry collaboration, internal and external collaborations. We present results on the distribution of the num- ber of co-authors in each paper as well as distribution of authors across world regions. We present our analysis on the public or proprietary dataset as well as the domain of the dataset used in studies published in Requirements Engineering (RE) series of conferences. Scholars are increasingly citing electronic web references which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To webcite a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its instructions for authors accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) prospectively before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted citing articles (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. Purpose  The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach  ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets  a citation network based on an early paper on webometrics, and a selfcitation network based on the 19 most cited papers in the Journal of Documentation  using citation data taken from the Web of Knowledge database.Findings  ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ...