1048 lines
108 KiB
BibTeX
1048 lines
108 KiB
BibTeX
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@inproceedings{adler_content-driven_2007,
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title = {A Content-Driven Reputation System for the {{Wikipedia}}},
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booktitle = {Proceedings of the 16th {{International Conference}} on {{World Wide Web}}},
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author = {Adler, B. Thomas and {de Alfaro}, Luca},
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year = {2007},
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series = {{{WWW}} '07},
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pages = {261--270},
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publisher = {{ACM}},
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address = {{New York, NY, USA}},
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abstract = {We present a content-driven reputation system for Wikipedia authors. In our system, authors gain reputation when the edits they perform to Wikipedia articles are preserved by subsequent authors, and they lose reputation when their edits are rolled back or undone in short order. Thus, author reputation is computed solely on the basis of content evolution; user-to-user comments or ratings are not used. The author reputation we compute could be used to flag new contributions from low-reputation authors, or it could be used to allow only authors with high reputation to contribute to controversialor critical pages. A reputation system for the Wikipedia could also provide an incentive for high-quality contributions. We have implemented the proposed system, and we have used it to analyze the entire Italian and French Wikipedias, consisting of a total of 691, 551 pages and 5, 587, 523 revisions. Our results show that our notion of reputation has good predictive value: changes performed by low-reputation authors have a significantly larger than average probability of having poor quality, as judged by human observers, and of being later undone, as measured by our algorithms.},
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isbn = {978-1-59593-654-7}
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}
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@inproceedings{anderka_breakdown_2012,
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title = {A Breakdown of Quality Flaws in {{Wikipedia}}},
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booktitle = {Proceedings of the {{2Nd Joint WICOW}}/{{AIRWeb Workshop}} on {{Web Quality}}},
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author = {Anderka, Maik and Stein, Benno},
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year = {2012},
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series = {{{WebQuality}} '12},
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pages = {11--18},
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publisher = {{ACM}},
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address = {{New York, NY}},
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abstract = {The online encyclopedia Wikipedia is a successful example of the increasing popularity of user generated content on the Web. Despite its success, Wikipedia is often criticized for containing low-quality information, which is mainly attributed to its core policy of being open for editing by everyone. The identification of low-quality information is an important task since Wikipedia has become the primary source of knowledge for a huge number of people around the world. Previous research on quality assessment in Wikipedia either investigates only small samples of articles, or else focuses on single quality aspects, like accuracy or formality. This paper targets the investigation of quality flaws, and presents the first complete breakdown of Wikipedia's quality flaw structure. We conduct an extensive exploratory analysis, which reveals (1) the quality flaws that actually exist, (2) the distribution of flaws in Wikipedia, and (3) the extent of flawed content. An important finding is that more than one in four English Wikipedia articles contains at least one quality flaw, 70\% of which concern article verifiability.},
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isbn = {978-1-4503-1237-0},
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file = {/home/nathante/Zotero/storage/TLV8BC38/Anderka_Stein_2012_A breakdown of quality flaws in Wikipedia.pdf}
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}
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@inproceedings{anderka_predicting_2012,
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title = {Predicting Quality Flaws in User-Generated Content: The Case of {{Wikipedia}}},
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shorttitle = {Predicting Quality Flaws in User-Generated Content},
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booktitle = {Proceedings of the 35th {{International ACM SIGIR Conference}} on {{Research}} and {{Development}} in {{Information Retrieval}}},
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author = {Anderka, Maik and Stein, Benno and Lipka, Nedim},
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year = {2012},
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series = {{{SIGIR}} '12},
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pages = {981--990},
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publisher = {{ACM}},
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address = {{New York, NY, USA}},
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abstract = {The detection and improvement of low-quality information is a key concern in Web applications that are based on user-generated content; a popular example is the online encyclopedia Wikipedia. Existing research on quality assessment of user-generated content deals with the classification as to whether the content is high-quality or low-quality. This paper goes one step further: it targets the prediction of quality flaws, this way providing specific indications in which respects low-quality content needs improvement. The prediction is based on user-defined cleanup tags, which are commonly used in many Web applications to tag content that has some shortcomings. We apply this approach to the English Wikipedia, which is the largest and most popular user-generated knowledge source on the Web. We present an automatic mining approach to identify the existing cleanup tags, which provides us with a training corpus of labeled Wikipedia articles. We argue that common binary or multiclass classification approaches are ineffective for the prediction of quality flaws and hence cast quality flaw prediction as a one-class classification problem. We develop a quality flaw model and employ a dedicated machine learning approach to predict Wikipedia's most important quality flaws. Since in the Wikipedia setting the acquisition of significant test data is intricate, we analyze the effects of a biased sample selection. In this regard we illustrate the classifier effectiveness as a function of the flaw distribution in order to cope with the unknown (real-world) flaw-specific class imbalances. The flaw prediction performance is evaluated with 10,000 Wikipedia articles that have been tagged with the ten most frequent quality flaws: provided test data with little noise, four flaws can be detected with a precision close to 1.},
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isbn = {978-1-4503-1472-5},
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file = {/home/nathante/Zotero/storage/BQDLM6XK/Anderka et al_2012_Predicting quality flaws in user-generated content.pdf}
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}
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@inproceedings{anderka_towards_2011,
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title = {Towards {{Automatic Quality Assurance}} in {{Wikipedia}}},
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booktitle = {Proceedings of the 20th {{International Conference Companion}} on {{World Wide Web}}},
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author = {Anderka, Maik and Stein, Benno and Lipka, Nedim},
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year = {2011},
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series = {{{WWW}} '11},
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pages = {5--6},
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publisher = {{ACM}},
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address = {{New York, NY, USA}},
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abstract = {Featured articles in Wikipedia stand for high information quality, and it has been found interesting to researchers to analyze whether and how they can be distinguished from "ordinary" articles. Here we point out that article discrimination falls far short of writer support or automatic quality assurance: Featured articles are not identified, but are made. Following this motto we compile a comprehensive list of information quality flaws in Wikipedia, model them according to the latest state of the art, and devise one-class classification technology for their identification.},
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isbn = {978-1-4503-0637-9},
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file = {/home/nathante/Zotero/storage/D4757WKM/Anderka et al_2011_Towards Automatic Quality Assurance in Wikipedia.pdf}
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}
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@inproceedings{antelio_qualitocracy_2012,
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title = {Qualitocracy: {{A}} Data Quality Collaborative Framework Applied to Citizen Science},
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shorttitle = {Qualitocracy},
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author = {Antelio, Marcio and Esteves, Maria Gilda P. and Schneider, Daniel and de Souza, Jano Moreira},
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year = {2012},
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month = oct,
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pages = {931--936},
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publisher = {{IEEE}},
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isbn = {978-1-4673-1714-6 978-1-4673-1713-9 978-1-4673-1712-2},
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file = {/home/nathante/Zotero/storage/IURBKUZP/Antelio et al_2012_Qualitocracy.pdf}
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}
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@inproceedings{arazy_determinants_2010,
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title = {Determinants of Wikipedia Quality: The Roles of Global and Local Contribution Inequality},
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shorttitle = {Determinants of Wikipedia Quality},
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booktitle = {Proceedings of the 2010 {{ACM}} Conference on {{Computer}} Supported Cooperative Work},
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author = {Arazy, Ofer and Nov, Oded},
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year = {2010},
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month = feb,
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series = {{{CSCW}} '10},
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pages = {233--236},
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publisher = {{Association for Computing Machinery}},
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address = {{New York, NY, USA}},
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abstract = {The success of Wikipedia and the relative high quality of its articles seem to contradict conventional wisdom. Recent studies have begun shedding light on the processes contributing to Wikipedia's success, highlighting the role of coordination and contribution inequality. In this study, we expand on these works in two ways. First, we make a distinction between global (Wikipedia-wide) and local (article-specific) inequality and investigate both constructs. Second, we explore both direct and indirect effects of these inequalities, exposing the intricate relationships between global inequality, local inequality, coordination, and article quality. We tested our hypotheses on a sample of a Wikipedia articles using structural equation modeling and found that global inequality exerts significant positive impact on article quality, while the effect of local inequality is indirect and is mediated by coordination},
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isbn = {978-1-60558-795-0},
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keywords = {contribution inequality,coordination,global inequality,information quality,local inequality,wikipedia},
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file = {/home/nathante/Zotero/storage/WDJ4APS7/Arazy_Nov_2010_Determinants of wikipedia quality.pdf}
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}
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@article{arazy_evolutionary_2019,
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title = {The Evolutionary Trajectories of Peer-Produced Artifacts: {{Group}} Composition, the Trajectories' Exploration, and the Quality of Artifacts},
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shorttitle = {The Evolutionary Trajectories of Peer-Produced Artifacts},
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author = {Arazy, Ofer and Lindberg, Aron and Rezaei, Mostafa and Samorani, Michele},
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year = {2019},
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month = dec,
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journal = {MIS Quarterly},
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abstract = {Members of an online community peer-produce digital artifacts by negotiating different perspectives and personal knowledge bases. These negotiations are manifested in the temporal evolution of the peer-produced artifact. In this study we conceptualize the evolution of a digital artifact as a trajectory in a feature space. Our theoretical frame suggests that through negotiations contributors' actions "pull" the trajectory and shape its movement in the feature space. We hypothesize that the type of contributors that work on a focal article influences the extent to which that article's trajectory explores alternative positions within that space, and that the trajectory's exploration is, in turn, associated with the artifact's quality. To test these hypotheses, we analyzed the trajectories of wiki articles drawn from two peer-production communities: Wikipedia and Wikia, tracking the evolution of 242 paired articles for over a decade during which the articles went through 536,745 revisions. We found that the contributors who are the most likely to increase the trajectory's exploration are those that (a) return to work on the focal artifact and (b) are unregistered members in the broader online community Further, our results show that the trajectory's exploration has a curvilinear association with article quality, indicating that exploration contributes positively to quality, but that the effect is reversed when exploration exceeds a certain level. The insights derived from this study highlight the value of an artifact-centric approach to increasing our understanding of the dynamics underlying peer-production.},
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keywords = {peer production,wikia},
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file = {/home/nathante/Zotero/storage/ZGMAGR5H/Arazy et al_2019_The evolutionary trajectories of peer-produced artifacts.pdf}
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}
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@article{asthana_few_2018,
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title = {With {{Few Eyes}}, {{All Hoaxes Are Deep}}},
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author = {Asthana, Sumit and Halfaker, Aaron},
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year = {2018},
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month = nov,
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journal = {Proc. ACM Hum.-Comput. Interact.},
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volume = {2},
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number = {CSCW},
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pages = {21:1--21:18},
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issn = {2573-0142},
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abstract = {Quality control is critical to open production communities like Wikipedia. Wikipedia editors enact border quality control with edits (counter-vandalism) and new article creations (new page patrolling) shortly after they are saved. In this paper, we describe a long-standing set of inefficiencies that have plagued new page patrolling by drawing a contrast to the more efficient, distributed processes for counter-vandalism. Further, to address this issue, we demonstrate an effective automated topic model based on a labeling strategy that leverages a folksonomy developed by subject specific working groups in Wikipedia (WikiProject tags) and a flexible ontology (WikiProjects Directory) to arrive at a hierarchical and uniform label set. We are able to attain very high fitness measures (macro ROC-AUC: 95.2\%, macro PR-AUC: 74.5\%) and real-time performance using word2vec-based features. Finally, we present a proposal for how incorporating this model into current tools will shift the dynamics of new article review positively.},
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file = {/home/nathante/Zotero/storage/EM6Z9WPQ/Asthana and Halfaker - 2018 - With Few Eyes, All Hoaxes Are Deep.pdf}
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}
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@book{ayers_how_2008,
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title = {How {{Wikipedia}} Works and How You Can Be a Part of It},
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author = {Ayers, Phoebe and Matthews, Charles and Yates, Ben},
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year = {2008},
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publisher = {{No Starch Press}},
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address = {{San Francisco, CA}},
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abstract = {"In How Wikipedia Works, you'll learn the skills required to use and contribute to the world's largest reference work - like what constitutes good writing and research and how to work with images and templates."--Jacket.},
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isbn = {978-1-59327-227-2},
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langid = {english}
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}
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@techreport{band_wikipedias_2013,
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type = {{{SSRN Scholarly Paper}}},
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title = {Wikipedia's {{Economic Value}}},
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author = {Band, Jonathan and Gerafi, Jonathan},
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year = {2013},
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month = oct,
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address = {{Rochester, NY}},
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institution = {{Social Science Research Network}},
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abstract = {In the copyright policy debate, proponents of strong copyright protection tend to be dismissive of the quality of freely available content. In response to counter-examples such as open access scholarly publications and advertising-supported business models (e.g., newspaper websites and the over-the-air television broadcasts viewed by 50 million Americans), the strong copyright proponents center their attack on amateur content. In this narrative, YouTube is for cat videos and Wikipedia is a wildly unreliable source of information.},
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langid = {english},
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keywords = {Jonathan Band,Jonathan Gerafi,SSRN,Wikipedia's Economic Value},
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file = {/home/nathante/Zotero/storage/4Z3W8LKV/Band_Gerafi_2013_Wikipedia's Economic Value.pdf;/home/nathante/Zotero/storage/KDSXLL2E/papers.html}
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}
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@inproceedings{biancani_measuring_2014,
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title = {Measuring the {{Quality}} of {{Edits}} to {{Wikipedia}}},
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booktitle = {Proceedings of {{The International Symposium}} on {{Open Collaboration}}},
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author = {Biancani, Susan},
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year = {2014},
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series = {{{OpenSym}} '14},
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pages = {33:1--33:3},
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publisher = {{ACM}},
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address = {{New York, NY, USA}},
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abstract = {Wikipedia is unique among reference works both in its scale and in the openness of its editing interface. The question of how it can achieve and maintain high-quality encyclopedic articles is an area of active research. In order to address this question, researchers need to build consensus around a sensible metric to assess the quality of contributions to articles. This measure must not only reflect an intuitive concept of "quality," but must also be scalable and run efficiently. Building on prior work in this area, this paper uses human raters through Amazon Mechanical Turk to validate an efficient, automated quality metric.},
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isbn = {978-1-4503-3016-9},
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file = {/home/nathante/Zotero/storage/WHG7AUHK/Biancani_2014_Measuring the Quality of Edits to Wikipedia.pdf}
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}
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@inproceedings{blumenstock_size_2008,
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title = {Size Matters: Word Count as a Measure of Quality on Wikipedia},
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shorttitle = {Size Matters},
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booktitle = {Proceeding of the 17th International Conference on {{World Wide Web}} - {{WWW}} '08},
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author = {Blumenstock, Joshua E.},
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year = {2008},
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pages = {1095},
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publisher = {{ACM Press}},
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address = {{Beijing, China}},
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abstract = {Wikipedia, ``the free encyclopedia'', now contains over two million English articles, and is widely regarded as a highquality, authoritative encyclopedia. Some Wikipedia articles, however, are of questionable quality, and it is not always apparent to the visitor which articles are good and which are bad. We propose a simple metric \textendash{} word count \textendash for measuring article quality. In spite of its striking simplicity, we show that this metric significantly outperforms the more complex methods described in related work.},
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isbn = {978-1-60558-085-2},
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langid = {english},
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file = {/home/nathante/Zotero/storage/I8L8VT29/Blumenstock_2008_Size matters.pdf}
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}
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@article{burkner_brms_2017,
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title = {Brms: {{An R Package}} for {{Bayesian Multilevel Models Using Stan}}},
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shorttitle = {Brms},
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author = {B{\"u}rkner, Paul-Christian},
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year = {2017},
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month = aug,
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journal = {Journal of Statistical Software},
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volume = {80},
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number = {1},
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pages = {1--28},
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issn = {1548-7660},
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copyright = {Copyright (c) 2017 Paul-Christian B\"urkner},
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langid = {english},
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keywords = {Bayesian inference,MCMC,multilevel model,ordinal data,R,Stan},
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file = {/home/nathante/Zotero/storage/XDUCKTG7/Bürkner_2017_brms.pdf;/home/nathante/Zotero/storage/LJXX4II6/v080i01.html}
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}
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@article{burkner_ordinal_2019,
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title = {Ordinal {{Regression Models}} in {{Psychology}}: {{A Tutorial}}},
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shorttitle = {Ordinal {{Regression Models}} in {{Psychology}}},
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author = {B{\"u}rkner, Paul-Christian and Vuorre, Matti},
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year = {2019},
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month = mar,
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journal = {Advances in Methods and Practices in Psychological Science},
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volume = {2},
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number = {1},
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||
pages = {77--101},
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publisher = {{SAGE Publications Inc}},
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issn = {2515-2459},
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abstract = {Ordinal variables, although extremely common in psychology, are almost exclusively analyzed with statistical models that falsely assume them to be metric. This practice can lead to distorted effect-size estimates, inflated error rates, and other problems. We argue for the application of ordinal models that make appropriate assumptions about the variables under study. In this Tutorial, we first explain the three major classes of ordinal models: the cumulative, sequential, and adjacent-category models. We then show how to fit ordinal models in a fully Bayesian framework with the R package brms, using data sets on opinions about stem-cell research and time courses of marriage. The appendices provide detailed mathematical derivations of the models and a discussion of censored ordinal models. Compared with metric models, ordinal models provide better theoretical interpretation and numerical inference from ordinal data, and we recommend their widespread adoption in psychology.},
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langid = {english},
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||
keywords = {brms,Likert items,open data,open materials,ordinal models,R},
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file = {/home/nathante/Zotero/storage/TQJGFWGD/Bürkner_Vuorre_2019_Ordinal Regression Models in Psychology.pdf}
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}
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@article{cardoso_learning_2007,
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title = {Learning to {{Classify Ordinal Data}}: {{The Data Replication Method}}},
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author = {Cardoso, Jaime S and Cardoso, Jaime and Pt, Inescporto},
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year = {2007},
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journal = {Journal of Machine Learning Research},
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volume = {8},
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pages = {37},
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abstract = {Classification of ordinal data is one of the most important tasks of relation learning. This paper introduces a new machine learning paradigm specifically intended for classification problems where the classes have a natural order. The technique reduces the problem of classifying ordered classes to the standard two-class problem. The introduced method is then mapped into support vector machines and neural networks. Generalization bounds of the proposed ordinal classifier are also provided. An experimental study with artificial and real data sets, including an application to gene expression analysis, verifies the usefulness of the proposed approach.},
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langid = {english},
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file = {/home/nathante/Zotero/storage/FKNYFLDN/Cardoso et al. - Learning to Classify Ordinal Data The Data Replic.pdf}
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}
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@phdthesis{champion_production_2019,
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type = {Master of {{Arts Thesis}}},
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title = {Production Misalignment: A Threat to Public Knowledge},
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shorttitle = {Production Misalignment},
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author = {Champion, Kaylea},
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year = {2019},
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||
address = {{Seattle, Washington}},
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abstract = {On Wikipedia, when a high-interest topic is poorly-covered\textemdash either it is incomplete or contains inaccurate information\textemdash public knowledge is threatened. Contributors on Wikipedia are volunteers: they're not assigned to track consumer demand, and they choose their own tasks. When contributor interest doesn't align with consumer interest, the result is termed ``underproduction''\textemdash some widely consumed materials are low quality. Past research has found competing explanations for what motivates volunteers to work on particular articles, including attempts to solve their own problems and supporting project goals. I theorize that social rewards explain task selection for moderate to high levels of experience, although this trend attenuates at the highest level of experience. Using a detailed longitudinal dataset, I find support for this theory in three ways. First, that although they are a minority of contributors, persistent contributors drive what gets produced. Second, as contributors persist, they are less likely to contribute to underproduced materials, but this trend flattens over time as predicted.Third, this pattern is weaker among contributors who do not create accounts.},
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||
copyright = {CC BY},
|
||
langid = {american},
|
||
school = {University of Washington},
|
||
annotation = {Accepted: 2020-02-04T19:25:32Z},
|
||
file = {/home/nathante/Zotero/storage/WQWDRFAW/45156.html}
|
||
}
|
||
|
||
@article{champion_underproduction_2021,
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||
title = {Underproduction: {{An}} Approach for Measuring Risk in Open Source Software},
|
||
author = {Champion, Kaylea and Hill, Benjamin Mako},
|
||
year = {2021},
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||
month = feb,
|
||
journal = {IEEE International Conference on Software Analysis, Evolution and Reengineering},
|
||
eprint = {2103.00352},
|
||
eprinttype = {arxiv},
|
||
primaryclass = {cs.SE},
|
||
abstract = {The widespread adoption of Free/Libre and Open Source Software (FLOSS) means that the ongoing maintenance of many widely used software components relies on the collaborative effort of volunteers who set their own priorities and choose their own tasks. We argue that this has created a new form of risk that we call 'underproduction' which occurs when the supply of software engineering labor becomes out of alignment with the demand of people who rely on the software produced. We present a conceptual framework for identifying relative underproduction in software as well as a statistical method for applying our framework to a comprehensive dataset from the Debian GNU/Linux distribution that includes 21,902 source packages and the full history of 461,656 bugs. We draw on this application to present two experiments: (1) a demonstration of how our technique can be used to identify at-risk software packages in a large FLOSS repository and (2) a validation of these results using an alternate indicator of package risk. Our analysis demonstrates both the utility of our approach and reveals the existence of widespread underproduction in a range of widely-installed software components in Debian.},
|
||
archiveprefix = {arXiv}
|
||
}
|
||
|
||
@book{chang_inventing_2004,
|
||
title = {Inventing Temperature.},
|
||
author = {Chang, Hasok},
|
||
year = {2004},
|
||
publisher = {{OUP}},
|
||
address = {{Oxford}},
|
||
isbn = {978-0-19-517127-3},
|
||
langid = {english},
|
||
annotation = {OCLC: 538097673}
|
||
}
|
||
|
||
@inproceedings{dang_quality_2016,
|
||
title = {Quality {{Assessment}} of {{Wikipedia Articles Without Feature Engineering}}},
|
||
booktitle = {Proceedings of the 16th {{ACM}}/{{IEEE-CS}} on {{Joint Conference}} on {{Digital Libraries}}},
|
||
author = {Dang, Quang Vinh and Ignat, Claudia-Lavinia},
|
||
year = {2016},
|
||
series = {{{JCDL}} '16},
|
||
pages = {27--30},
|
||
publisher = {{ACM}},
|
||
address = {{New York, NY, USA}},
|
||
abstract = {As Wikipedia became the largest human knowledge repository, quality measurement of its articles received a lot of attention during the last decade. Most research efforts focused on classification of Wikipedia articles quality by using a different feature set. However, so far, no ``golden feature set" was proposed. In this paper, we present a novel approach for classifying Wikipedia articles by analysing their content rather than by considering a feature set. Our approach uses recent techniques in natural language processing and deep learning, and achieved a comparable result with the state-of-the-art.},
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||
isbn = {978-1-4503-4229-2},
|
||
file = {/home/nathante/Zotero/storage/KVMYP7YQ/Dang_Ignat_2016_Quality Assessment of Wikipedia Articles Without Feature Engineering.pdf}
|
||
}
|
||
|
||
@inproceedings{druck_learning_2008,
|
||
title = {Learning to {{Predict}} the {{Quality}} of {{Contributions}} to {{Wikipedia}}},
|
||
booktitle = {{{WikiAI}}},
|
||
author = {Druck, Gregory and Miklau, Gerome and McCallum, Andrew},
|
||
year = {2008},
|
||
pages = {6},
|
||
abstract = {Although some have argued that Wikipedia's open edit policy is one of the primary reasons for its success, it also raises concerns about quality \textemdash{} vandalism, bias, and errors can be problems. Despite these challenges, Wikipedia articles are often (perhaps surprisingly) of high quality, which many attribute to both the dedicated Wikipedia community and ``good Samaritan'' users. As Wikipedia continues to grow, however, it becomes more difficult for these users to keep up with the increasing number of articles and edits. This motivates the development of tools to assist users in creating and maintaining quality. In this paper, we propose metrics that quantify the quality of contributions to Wikipedia through implicit feedback from the community. We then learn discriminative probabilistic models that predict the quality of a new edit using features of the changes made, the author of the edit, and the article being edited. Through estimating parameters for these models, we also gain an understanding of factors that influence quality. We advocate using edit quality predictions and information gleaned from model analysis not to place restrictions on editing, but to instead alert users to potential quality problems, and to facilitate the development of additional incentives for contributors. We evaluate the edit quality prediction models on the Spanish Wikipedia. Experiments demonstrate that the models perform better when given access to content-based features of the edit, rather than only features of contributing user. This suggests that a user-based solution to the Wikipedia quality problem may not be sufficient.},
|
||
langid = {english},
|
||
file = {/home/nathante/Zotero/storage/IVJQI75V/Druck et al. - Learning to Predict the Quality of Contributions t.pdf}
|
||
}
|
||
|
||
@article{ford_beyond_2018,
|
||
title = {Beyond Notification: {{Filling}} Gaps in Peer Production Projects},
|
||
shorttitle = {Beyond Notification},
|
||
author = {Ford, Heather and Pensa, Iolanda and Devouard, Florence and Pucciarelli, Marta and Botturi, Luca},
|
||
year = {2018},
|
||
month = oct,
|
||
journal = {New Media \& Society},
|
||
volume = {20},
|
||
number = {10},
|
||
pages = {3799--3817},
|
||
publisher = {{SAGE Publications}},
|
||
issn = {1461-4448},
|
||
abstract = {In order to counter systemic bias in peer production projects like Wikipedia, a variety of strategies have been used to fill gaps and improve the completeness of the archive. We test a number of these strategies in a project aimed at improving articles relating to South Africa's primary school curriculum and find that many of the predominant strategies are insufficient for filling Wikipedia's gaps. Notifications that alert users to the existence of gaps including incomplete or missing articles, in particular, are found to be ineffective at improving articles. Only through the process of trust-building and the development of negotiated boundary objects, potential allies (institutional academics in this case) can be enrolled in the task of editing the encyclopaedia. Rather than a simple process of enrolment via notification, this project demonstrated the principles of negotiation required for engaging with new editor groups in the long-term project of filling Wikipedia's gaps},
|
||
langid = {english},
|
||
keywords = {Boundary objects,expertise,participation,systemic bias,Wikipedia},
|
||
file = {/home/nathante/Zotero/storage/4QJNHJFS/Ford et al_2018_Beyond notification.pdf}
|
||
}
|
||
|
||
@inproceedings{forte_why_2005,
|
||
ids = {forte_why_nodate-1},
|
||
title = {Why {{Do People Write}} for {{Wikipedia}}? {{Incentives}} to {{Contribute}} to {{Open-Content Publishing}}},
|
||
booktitle = {Proceedings of {{GROUP}}},
|
||
author = {Forte, Andrea and Bruckman, Amy},
|
||
year = {2005},
|
||
pages = {6},
|
||
abstract = {When people learn that we have spoken to individuals who spend up to 30 hours a week volunteering their time to research and write for an open-content encyclopedia, we often hear the same question: ``Why do they do it?`` The fact that this encyclopedia does not provide bylines to credit authors for their hard work makes the scenario still less fathomable. Two rounds of interviews with 22 volunteer encyclopedia writers in the fall of 2004 and spring of 2005 revealed that, in some respects, the incentive system that motivates contributions to the opencontent encyclopedia Wikipedia resembles that of the scientific community. Like scientists, contributors to Wikipedia seek to collaboratively identify and publish true facts about the world. Research on the sociology of science provides a useful touchstone for considering the incentive systems embedded in the technology and culture of online communities of collaborative authorship. In this paper we describe some of our findings in the context of Latour and Woolgar's seminal work on the incentive systems that motivate publishing scientists. We suggest that minimizing reliance on ``hard coded,`` stratified user privileges and providing indicators of engagement in desirable activities can help support the growth of incentive economies in online communities.},
|
||
langid = {english},
|
||
file = {/home/nathante/Zotero/storage/G97IG7J2/Forte and Bruckman - Why Do People Write for Wikipedia Incentives to C.pdf;/home/nathante/Zotero/storage/Z84K5QDA/Forte and Bruckman - Why Do People Write for Wikipedia Incentives to C.pdf}
|
||
}
|
||
|
||
@incollection{goodhart_problems_1984,
|
||
title = {Problems of {{Monetary Management}}: {{The UK Experience}}},
|
||
shorttitle = {Problems of {{Monetary Management}}},
|
||
booktitle = {Monetary {{Theory}} and {{Practice}}: {{The UK Experience}}},
|
||
author = {Goodhart, C. A. E.},
|
||
editor = {Goodhart, C. A. E.},
|
||
year = {1984},
|
||
pages = {91--121},
|
||
publisher = {{Macmillan Education UK}},
|
||
address = {{London}},
|
||
abstract = {In 1971 the monetary authorities1 in the UK adopted a new approach to monetary management, a change of policy announced and described in several papers on competition and credit control. The subsequent experience of trying to operate this revised system has, however, been troublesome and at times unhappy. The purpose here is to examine certain aspects of recent monetary developments in order to illustrate a number of more general analytical themes which may have relevance among several countries.},
|
||
isbn = {978-1-349-17295-5},
|
||
langid = {english}
|
||
}
|
||
|
||
@inproceedings{gorbatai_exploring_2011-1,
|
||
title = {Exploring {{Underproduction}} in {{Wikipedia}}},
|
||
booktitle = {Proceedings of the 7th {{International Symposium}} on {{Wikis}} and {{Open Collaboration}}},
|
||
author = {Gorbatai, Andreea D.},
|
||
year = {2011},
|
||
series = {{{WikiSym}} '11},
|
||
pages = {205--206},
|
||
abstract = {Researchers have used Wikipedia data to identify a wide range of antecedents to success in collective production. But we have not yet inquired whether collective production creates those public goods which bring most value-add from a social perspective. In this poster I explore two key circumstances in which collective production can fail to respond to social need: when goods fail to attain high quality despite (1) high demand or (2) explicit designation by producers as highly important. In the context of Wikipedia. I propose first to examine articles that remain low quality, or underproduced, despite the fact they are viewed often; and second, to examine articles that remain low quality despite the fact that they were identified as important by Wikipedia contributors. This research highlights the fact that collective production needs to be examined not only by itself but also in the context of a market for goods in order to ascertain the benefits of this production form. The final version of this study will integrate data on underproduced articles with data on knowledge categories to uncover systematic patterns of underproduction at the category level and predict which categories are most in need of quality improvement. Additionally I will use in-depth qualitative methods to examine the mechanisms through which underproduction occurs in select knowledge categories to distill practical recommendations for collective production improvement.},
|
||
isbn = {978-1-4503-0909-7},
|
||
keywords = {collective production,social goods,underproduction}
|
||
}
|
||
|
||
@inproceedings{halfaker_interpolating_2017,
|
||
title = {Interpolating {{Quality Dynamics}} in {{Wikipedia}} and {{Demonstrating}} the {{Keilana Effect}}},
|
||
booktitle = {Proceedings of the 13th {{International Symposium}} on {{Open Collaboration}}},
|
||
author = {Halfaker, Aaron},
|
||
year = {2017},
|
||
month = aug,
|
||
series = {{{OpenSym}} '17},
|
||
pages = {1--9},
|
||
publisher = {{Association for Computing Machinery}},
|
||
address = {{New York, NY, USA}},
|
||
abstract = {For open, volunteer generated content like Wikipedia, quality is a prominent concern. To measure Wikipedia's quality, researchers have historically relied on expert evaluation or assessments of article quality by Wikipedians themselves. While both of these methods have proven effective for answering many questions about Wikipedia's quality and processes, they are both problematic: expert evaluation is expensive and Wikipedian quality assessments are sporadic and unpredictable. Studies that explore Wikipedia's quality level or the processes that result in quality improvements have only examined small snapshots of Wikipedia and often rely on complex propensity models to deal with the unpredictable nature of Wikipedians' own assessments. In this paper, I describe a method for measuring article quality in Wikipedia historically and at a finer granularity than was previously possible. I use this method to demonstrate an important coverage dynamic in Wikipedia (specifically, articles about women scientists) and offer this method, dataset, and open API to the research community studying Wikipedia quality dynamics.},
|
||
isbn = {978-1-4503-5187-4},
|
||
keywords = {Dataset,Interpolation,Methods,Modeling,Predictive,Quality,Wikipedia},
|
||
file = {/home/nathante/Zotero/storage/5Q7YRJ92/Halfaker_2017_Interpolating Quality Dynamics in Wikipedia and Demonstrating the Keilana Effect.pdf}
|
||
}
|
||
|
||
@inproceedings{halfaker_jury_2009,
|
||
title = {A {{Jury}} of {{Your Peers}}: {{Quality}}, {{Experience}} and {{Ownership}} in {{Wikipedia}}},
|
||
shorttitle = {A {{Jury}} of {{Your Peers}}},
|
||
booktitle = {Proceedings of the 5th {{International Symposium}} on {{Wikis}} and {{Open Collaboration}}},
|
||
author = {Halfaker, Aaron and Kittur, Aniket and Kraut, Robert and Riedl, John},
|
||
year = {2009},
|
||
series = {{{WikiSym}} '09},
|
||
pages = {15:1--15:10},
|
||
publisher = {{ACM}},
|
||
address = {{New York, NY, USA}},
|
||
abstract = {Wikipedia is a highly successful example of what mass collaboration in an informal peer review system can accomplish. In this paper, we examine the role that the quality of the contributions, the experience of the contributors and the ownership of the content play in the decisions over which contributions become part of Wikipedia and which ones are rejected by the community. We introduce and justify a versatile metric for automatically measuring the quality of a contribution. We find little evidence that experience helps contributors avoid rejection. In fact, as they gain experience, contributors are even more likely to have their work rejected. We also find strong evidence of ownership behaviors in practice despite the fact that ownership of content is discouraged within Wikipedia.},
|
||
isbn = {978-1-60558-730-1},
|
||
keywords = {experience,ownership,peer,peer review,quality,wikipedia,WikiWork},
|
||
file = {/home/nathante/Zotero/storage/3D95RK5T/Halfaker et al. - 2009 - A Jury of Your Peers Quality, Experience and Owne.pdf;/home/nathante/Zotero/storage/4VTKXZIS/Halfaker et al. - 2009 - A Jury of Your Peers Quality, Experience and Owne.pdf;/home/nathante/Zotero/storage/R84D69QJ/Halfaker et al. - 2009 - A jury of your peers quality, experience and owne.pdf}
|
||
}
|
||
|
||
@article{halfaker_ores_2020,
|
||
title = {{{ORES}}: {{Lowering Barriers}} with {{Participatory Machine Learning}} in {{Wikipedia}}},
|
||
author = {Halfaker, Aaron and Geiger, R Stuart},
|
||
year = {2020},
|
||
month = oct,
|
||
volume = {4},
|
||
number = {148},
|
||
pages = {37},
|
||
langid = {english},
|
||
file = {/home/nathante/Zotero/storage/SYIM8B62/Halfaker_Geiger_2020_ORES.pdf}
|
||
}
|
||
|
||
@book{hastie_elements_2018,
|
||
title = {The {{Elements}} of Statistical Learning: Data Mining, Inference, and Prediction},
|
||
shorttitle = {The {{Elements}} of Statistical Learning},
|
||
author = {Hastie, Trevor and Friedman, Jerome and Tisbshirani, Robert},
|
||
year = {2018},
|
||
publisher = {{Springer}},
|
||
address = {{New York}},
|
||
isbn = {978-0-387-84857-0},
|
||
langid = {english},
|
||
annotation = {OCLC: 1085863671}
|
||
}
|
||
|
||
@inproceedings{hecht_tower_2010,
|
||
title = {The {{Tower}} of {{Babel}} Meets {{Web}} 2.0: User-Generated Content and Its Applications in a Multilingual Context},
|
||
shorttitle = {The Tower of Babel Meets Web 2.0},
|
||
booktitle = {Proceedings of the {{SIGCHI Conference}} on {{Human Factors}} in {{Computing Systems}}},
|
||
author = {Hecht, Brent and Gergle, Darren},
|
||
year = {2010},
|
||
month = apr,
|
||
series = {{{CHI}} '10},
|
||
pages = {291--300},
|
||
publisher = {{Association for Computing Machinery}},
|
||
address = {{Atlanta, Georgia, USA}},
|
||
abstract = {This study explores language's fragmenting effect on user-generated content by examining the diversity of knowledge representations across 25 different Wikipedia language editions. This diversity is measured at two levels: the concepts that are included in each edition and the ways in which these concepts are described. We demonstrate that the diversity present is greater than has been presumed in the literature and has a significant influence on applications that use Wikipedia as a source of world knowledge. We close by explicating how knowledge diversity can be beneficially leveraged to create "culturally-aware applications" and "hyperlingual applications".},
|
||
isbn = {978-1-60558-929-9},
|
||
langid = {english},
|
||
file = {/home/nathante/Zotero/storage/7AVHUTYF/Hecht_Gergle_2010_The Tower of Babel meets Web 2.pdf}
|
||
}
|
||
|
||
@inproceedings{javanmardi_user_2009,
|
||
ids = {<EFBFBD>},
|
||
title = {User Contribution and Trust in {{Wikipedia}}},
|
||
booktitle = {Proceedings of the 5th {{International Conference}} on {{Collaborative Computing}}: {{Networking}}, {{Applications}} and {{Worksharing}} ({{CollaborateCom}} '09)},
|
||
author = {Javanmardi, S. and Ganjisaffar, Y. and Lopes, C. and Baldi, P.},
|
||
year = {2009},
|
||
publisher = {{ITSC / IEEE}},
|
||
address = {{New York, NY}},
|
||
abstract = {Wikipedia, one of the top ten most visited websites, is commonly viewed as the largest online reference for encyclopedic knowledge. Because of its open editing model -allowing anyone to enter and edit content- Wikipedia's overall quality has often been questioned as a source of reliable information. Lack of study of the open editing model of Wikipedia and its effectiveness has resulted in a new generation of wikis that restrict contributions to registered users only, using their real names. In this paper, we present an empirical study of user contributions to Wikipedia. We statistically analyze contributions by both anonymous and registered users. The results show that submissions of anonymous and registered users in Wikipedia suggest a power law behavior. About 80\% of the revisions are submitted by less than 7\% of the users, most of whom are registered users. To further refine the analyzes, we use the Wiki Trust Model (WTM), a user reputation model developed in our previous work to assign a reputation value to each user. As expected, the results show that registered users contribute higher quality content and therefore are assigned higher reputation values. However, a significant number of anonymous users also contribute high-quality content.We provide further evidence that regardless of a user s' attribution, registered or anonymous, high reputation users are the dominant contributors that actively edit Wikipedia articles in order to remove vandalism or poor quality content.},
|
||
keywords = {encyclopaedias,encyclopedic knowledge,user contribution,user interfaces,Web sites,Wiki trust model,Wikipedia},
|
||
file = {/home/nathante/Zotero/storage/M98L5F63/Javanmardi et al_2009_User contribution and trust in Wikipedia.pdf}
|
||
}
|
||
|
||
@book{jemielniak_common_2014,
|
||
ids = {jemielniak_common_2014-1},
|
||
title = {Common {{Knowledge}}?: {{An Ethnography}} of {{Wikipedia}}},
|
||
shorttitle = {Common {{Knowledge}}?},
|
||
author = {Jemielniak, Dariusz},
|
||
year = {2014},
|
||
month = may,
|
||
publisher = {{Stanford University Press}},
|
||
abstract = {With an emphasis on peer\textendash produced content and collaboration, Wikipedia exemplifies a departure from traditional management and organizational models. This iconic "project" has been variously characterized as a hive mind and an information revolution, attracting millions of new users even as it has been denigrated as anarchic and plagued by misinformation. Have Wikipedia's structure and inner workings promoted its astonishing growth and enduring public relevance? In Common Knowledge?, Dariusz Jemielniak draws on his academic expertise and years of active participation within the Wikipedia community to take readers inside the site, illuminating how it functions and deconstructing its distinctive organization. Against a backdrop of misconceptions about its governance, authenticity, and accessibility, Jemielniak delivers the first ethnography of Wikipedia, revealing that it is not entirely at the mercy of the public: instead, it balances open access and power with a unique bureaucracy that takes a page from traditional organizational forms. Along the way, Jemielniak incorporates fascinating cases that highlight the tug of war among the participants as they forge ahead in this pioneering environment.},
|
||
isbn = {978-0-8047-9120-5},
|
||
langid = {english},
|
||
keywords = {Business \& Economics / General,Business \& Economics / Organizational Behavior,Electronic encyclopedias -- Social aspects.,Organizational sociology.,Social Science / Anthropology / Cultural,Social Science / Anthropology / Cultural \& Social,Wikipedia.},
|
||
file = {/home/nathante/Zotero/storage/LS85JVJB/Jemielniak_2014_Common knowledge.pdf;/home/nathante/Zotero/storage/WN97JGCI/reader.html}
|
||
}
|
||
|
||
@inproceedings{kittur_harnessing_2008,
|
||
ids = {kittur_harnessing_2008-1},
|
||
title = {Harnessing the {{Wisdom}} of {{Crowds}} in {{Wikipedia}}: {{Quality Through Coordination}}},
|
||
shorttitle = {Harnessing the {{Wisdom}} of {{Crowds}} in {{Wikipedia}}},
|
||
booktitle = {Proceedings of the 2008 {{ACM Conference}} on {{Computer Supported Cooperative Work}}},
|
||
author = {Kittur, Aniket and Kraut, Robert E.},
|
||
year = {2008},
|
||
series = {{{CSCW}} '08},
|
||
pages = {37--46},
|
||
publisher = {{ACM}},
|
||
address = {{New York, NY, USA}},
|
||
abstract = {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.},
|
||
isbn = {978-1-60558-007-4},
|
||
keywords = {collaboration,Collaboration,collective intelligence,coordination,distributed cognition,quality of content,social computing,social interaction,wiki,Wiki,wikipedia,Wikipedia},
|
||
file = {/home/nathante/Zotero/storage/AAKBSS79/Kittur_Kraut_2008_Harnessing the wisdom of crowds in wikipedia.pdf;/home/nathante/Zotero/storage/J7X48SKE/Kittur and Kraut - 2008 - Harnessing the wisdom of crowds in wikipedia qual.pdf}
|
||
}
|
||
|
||
@article{kleinberg_inherent_2016,
|
||
title = {Inherent {{Trade-Offs}} in the {{Fair Determination}} of {{Risk Scores}}},
|
||
author = {Kleinberg, Jon and Mullainathan, Sendhil and Raghavan, Manish},
|
||
year = {2016},
|
||
month = sep,
|
||
journal = {arXiv:1609.05807 [cs, stat]},
|
||
eprint = {1609.05807},
|
||
eprinttype = {arxiv},
|
||
primaryclass = {cs, stat},
|
||
abstract = {Recent discussion in the public sphere about algorithmic classification has involved tension between competing notions of what it means for a probabilistic classification to be fair to different groups. We formalize three fairness conditions that lie at the heart of these debates, and we prove that except in highly constrained special cases, there is no method that can satisfy these three conditions simultaneously. Moreover, even satisfying all three conditions approximately requires that the data lie in an approximate version of one of the constrained special cases identified by our theorem. These results suggest some of the ways in which key notions of fairness are incompatible with each other, and hence provide a framework for thinking about the trade-offs between them.},
|
||
archiveprefix = {arXiv},
|
||
keywords = {_tablet_modified,Computer Science - Computers and Society,Computer Science - Machine Learning,Statistics - Machine Learning},
|
||
file = {/home/nathante/ownCloud/Papers/Kleinberg et al. - 2016 - Inherent Trade-Offs in the Fair Determination of R.pdf;/home/nathante/Zotero/storage/XXQIPXY2/1609.html}
|
||
}
|
||
|
||
@article{kocielnik_reciprocity_2018,
|
||
ids = {kocielnik_reciprocity_2018-1},
|
||
title = {Reciprocity and {{Donation}}: {{How Article Topic}}, {{Quality}} and {{Dwell Time Predict Banner Donation}} on {{Wikipedia}}},
|
||
shorttitle = {Reciprocity and {{Donation}}},
|
||
author = {Kocielnik, Rafal and Keyes, Os and Morgan, Jonathan T. and Taraborelli, Dario and McDonald, David W. and Hsieh, Gary},
|
||
year = {2018},
|
||
month = nov,
|
||
journal = {Proceedings of the ACM on Human-Computer Interaction},
|
||
volume = {2},
|
||
number = {CSCW},
|
||
pages = {1--20},
|
||
issn = {25730142},
|
||
langid = {english},
|
||
file = {/home/nathante/Zotero/storage/XIWVF6VN/Kocielnik et al. - 2018 - Reciprocity and Donation How Article Topic, Quali.pdf;/home/nathante/Zotero/storage/YCF2G6ZA/Kocielnik et al. - 2018 - Reciprocity and Donation How Article Topic, Quali.pdf}
|
||
}
|
||
|
||
@inproceedings{lemmerich_why_2019,
|
||
title = {Why the {{World Reads Wikipedia}}: {{Beyond English Speakers}}},
|
||
shorttitle = {Why the {{World Reads Wikipedia}}},
|
||
booktitle = {Proceedings of the {{Twelfth ACM International Conference}} on {{Web Search}} and {{Data Mining}}},
|
||
author = {Lemmerich, Florian and {S{\'a}ez-Trumper}, Diego and West, Robert and Zia, Leila},
|
||
year = {2019},
|
||
series = {{{WSDM}} '19},
|
||
pages = {618--626},
|
||
publisher = {{ACM}},
|
||
address = {{New York, NY, USA}},
|
||
abstract = {As one of the Web's primary multilingual knowledge sources, Wikipedia is read by millions of people across the globe every day. Despite this global readership, little is known about why users read Wikipedia's various language editions. To bridge this gap, we conduct a comparative study by combining a large-scale survey of Wikipedia readers across 14 language editions with a log-based analysis of user activity. We proceed in three steps. First, we analyze the survey results to compare the prevalence of Wikipedia use cases across languages, discovering commonalities, but also substantial differences, among Wikipedia languages with respect to their usage. Second, we match survey responses to the respondents' traces in Wikipedia's server logs to characterize behavioral patterns associated with specific use cases, finding that distinctive patterns consistently mark certain use cases across language editions. Third, we show that certain Wikipedia use cases are more common in countries with certain socio-economic characteristics; e.g., in-depth reading of Wikipedia articles is substantially more common in countries with a low Human Development Index. These findings advance our understanding of reader motivations and behaviors across Wikipedia languages and have implications for Wikipedia editors and developers of Wikipedia and other Web technologies.},
|
||
isbn = {978-1-4503-5940-5},
|
||
keywords = {cross-cultural analysis,log analysis,motivation,multi-language,survey,wikipedia},
|
||
file = {/home/nathante/Zotero/storage/HY6T3E2I/Lemmerich et al_2019_Why the World Reads Wikipedia.pdf}
|
||
}
|
||
|
||
@article{lewoniewski_relative_2017,
|
||
title = {Relative {{Quality}} and {{Popularity Evaluation}} of {{Multilingual Wikipedia Articles}}},
|
||
author = {Lewoniewski, W{\l}odzimierz and W{\k{e}}cel, Krzysztof and Abramowicz, Witold},
|
||
year = {2017},
|
||
month = dec,
|
||
journal = {Informatics},
|
||
volume = {4},
|
||
number = {4},
|
||
pages = {43},
|
||
publisher = {{Multidisciplinary Digital Publishing Institute}},
|
||
abstract = {Despite the fact that Wikipedia is often criticized for its poor quality, it continues to be one of the most popular knowledge bases in the world. Articles in this free encyclopedia on various topics can be created and edited in about 300 different language versions independently. Our research has showed that in language sensitive topics, the quality of information can be relatively better in the relevant language versions. However, in most cases, it is difficult for the Wikipedia readers to determine the language affiliation of the described subject. Additionally, each language edition of Wikipedia can have own rules in the manual assessing of the content's quality. There are also differences in grading schemes between language versions: some use a 6\textendash 8 grade system to assess articles, and some are limited to 2\textendash 3. This makes automatic quality comparison of articles between various languages a challenging task, particularly if we take into account a large number of unassessed articles; some of the Wikipedia language editions have over 99\% of articles without a quality grade. The paper presents the results of a relative quality and popularity assessment of over 28 million articles in 44 selected language versions. Comparative analysis of the quality and the popularity of articles in popular topics was also conducted. Additionally, the correlation between quality and popularity of Wikipedia articles of selected topics in various languages was investigated. The proposed method allows us to find articles with information of better quality that can be used to automatically enrich other language editions of Wikipedia.},
|
||
copyright = {http://creativecommons.org/licenses/by/3.0/},
|
||
langid = {english},
|
||
keywords = {DBpedia,information quality,Wikipedia,WikiRank},
|
||
file = {/home/nathante/Zotero/storage/FLWMK7U5/Lewoniewski et al_2017_Relative Quality and Popularity Evaluation of Multilingual Wikipedia Articles.pdf;/home/nathante/Zotero/storage/JQJTEH6S/htm.html}
|
||
}
|
||
|
||
@article{lewoniewski_relative_2017-2,
|
||
title = {Relative {{Quality}} and {{Popularity Evaluation}} of {{Multilingual Wikipedia Articles}}},
|
||
author = {Lewoniewski, W{\l}odzimierz and W{\k{e}}cel, Krzysztof and Abramowicz, Witold},
|
||
year = {2017},
|
||
month = dec,
|
||
journal = {Informatics},
|
||
volume = {4},
|
||
number = {4},
|
||
pages = {43},
|
||
abstract = {Despite the fact that Wikipedia is often criticized for its poor quality, it continues to be one of the most popular knowledge bases in the world. Articles in this free encyclopedia on various topics can be created and edited in about 300 different language versions independently. Our research has showed that in language sensitive topics, the quality of information can be relatively better in the relevant language versions. However, in most cases, it is difficult for the Wikipedia readers to determine the language affiliation of the described subject. Additionally, each language edition of Wikipedia can have own rules in the manual assessing of the content's quality. There are also differences in grading schemes between language versions: some use a 6\textendash 8 grade system to assess articles, and some are limited to 2\textendash 3. This makes automatic quality comparison of articles between various languages a challenging task, particularly if we take into account a large number of unassessed articles; some of the Wikipedia language editions have over 99\% of articles without a quality grade. The paper presents the results of a relative quality and popularity assessment of over 28 million articles in 44 selected language versions. Comparative analysis of the quality and the popularity of articles in popular topics was also conducted. Additionally, the correlation between quality and popularity of Wikipedia articles of selected topics in various languages was investigated. The proposed method allows us to find articles with information of better quality that can be used to automatically enrich other language editions of Wikipedia.},
|
||
copyright = {http://creativecommons.org/licenses/by/3.0/},
|
||
langid = {english},
|
||
file = {/home/nathante/Zotero/storage/589NUW97/Lewoniewski et al. - 2017 - Relative Quality and Popularity Evaluation of Mult.pdf;/home/nathante/Zotero/storage/RCNA3RIS/Lewoniewski et al. - 2017 - Relative Quality and Popularity Evaluation of Mult.pdf;/home/nathante/Zotero/storage/A447QV7Z/43.html}
|
||
}
|
||
|
||
@article{lukyanenko_iq_2014,
|
||
title = {The {{IQ}} of the {{Crowd}}: {{Understanding}} and {{Improving Information Quality}} in {{Structured User-Generated Content}}},
|
||
shorttitle = {The {{IQ}} of the {{Crowd}}},
|
||
author = {Lukyanenko, Roman and Parsons, Jeffrey and Wiersma, Yolanda F.},
|
||
year = {2014},
|
||
month = dec,
|
||
journal = {Information Systems Research},
|
||
volume = {25},
|
||
number = {4},
|
||
pages = {669--689},
|
||
issn = {1047-7047, 1526-5536},
|
||
langid = {english},
|
||
file = {/home/nathante/Zotero/storage/BNME7RBJ/Lukyanenko et al_2014_The IQ of the Crowd.pdf}
|
||
}
|
||
|
||
@inproceedings{matias_civilservant_2018,
|
||
title = {Civilservant: Community-Led Experiments in Platform Governance},
|
||
booktitle = {Proceedings of the 2018 {{CHI Conference}} on {{Human Factors}} in {{Computing Systems}}},
|
||
author = {Matias, J. Nathan and Mou, Merry},
|
||
year = {2018},
|
||
series = {{{CHI}} '18},
|
||
pages = {9:1--9:13},
|
||
publisher = {{ACM}},
|
||
address = {{New York, NY, USA}},
|
||
abstract = {As online platforms monitor and intervene in the daily lives of billions of people, platforms are being used to govern enduring social problems. Field experiments could inform wise uses of this power if tensions between democratic values and experimentation could be resolved. In this paper, we introduce CivilServant, a novel experimentation infrastructure that online communities and their moderators use to evaluate policies and replicate each others' findings. We situate CivilServant in the political history of policy experiments and present design considerations for community participation, ethics, and replication. Based on two case studies of community-led experiments and public debriefings on the reddit platform, we share findings on community deliberation about experiment results. We also report on uses of evidence, finding that experiments informed moderator practices, community policies, and replications by communities and platforms. We discuss the implications of these findings for evaluating platform governance in an open, democratic, experimenting society.},
|
||
isbn = {978-1-4503-5620-6},
|
||
keywords = {action research,ethics,field experiments,governance,moderation,platforms,policy evaluation,randomized trials},
|
||
file = {/home/nathante/Zotero/storage/3ULGKV83/Matias_Mou_2018_Civilservant.pdf}
|
||
}
|
||
|
||
@book{mcelreath_statistical_2018,
|
||
title = {Statistical {{Rethinking}}},
|
||
author = {McElreath, Richard and Safari, an O'Reilly Media Company},
|
||
year = {2018},
|
||
abstract = {Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers' knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today's model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author's website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.},
|
||
langid = {english},
|
||
annotation = {OCLC: 1107423386}
|
||
}
|
||
|
||
@incollection{menking_people_2019,
|
||
title = {People {{Who Can Take It}}: {{How Women Wikipedians Negotiate}} and {{Navigate Safety}}},
|
||
shorttitle = {People {{Who Can Take It}}},
|
||
booktitle = {Proceedings of the 2019 {{CHI Conference}} on {{Human Factors}} in {{Computing Systems}}},
|
||
author = {Menking, Amanda and Erickson, Ingrid and Pratt, Wanda},
|
||
year = {2019},
|
||
month = may,
|
||
pages = {1--14},
|
||
publisher = {{Association for Computing Machinery}},
|
||
address = {{New York, NY, USA}},
|
||
abstract = {Wikipedia is one of the most successful online communities in history, yet it struggles to attract and retain women editors-a phenomenon known as the gender gap. We investigate this gap by focusing on the voices of experienced women Wikipedians. In this interview-based study (N=25), we identify a core theme among these voices: safety. We reveal how our participants perceive safety within their community, how they manage their safety both conceptually and physically, and how they act on this understanding to create safe spaces on and off Wikipedia. Our analysis shows Wikipedia functions as both a multidimensional and porous space encompassing a spectrum of safety. Navigating this space requires these women to employ sophisticated tactics related to identity management, boundary management, and emotion work. We conclude with a set of provocations to spur the design of future online environments that encourage equity, inclusivity, and safety for historically marginalized users.},
|
||
isbn = {978-1-4503-5970-2},
|
||
keywords = {gender gap,online communities,participation,safe spaces,safety,wikipedia},
|
||
file = {/home/nathante/Zotero/storage/YAQL3MGV/Menking et al_2019_People Who Can Take It.pdf}
|
||
}
|
||
|
||
@article{mesgari_sum_2015,
|
||
title = {``{{The}} Sum of All Human Knowledge'': {{A}} Systematic Review of Scholarly Research on the Content of {{Wikipedia}}},
|
||
shorttitle = {``{{The}} Sum of All Human Knowledge''},
|
||
author = {Mesgari, Mostafa and Okoli, Chitu and Mehdi, Mohamad and Nielsen, Finn {\AA}rup and Lanam{\"a}ki, Arto},
|
||
year = {2015},
|
||
journal = {Journal of the Association for Information Science and Technology},
|
||
volume = {66},
|
||
number = {2},
|
||
pages = {219--245},
|
||
issn = {2330-1643},
|
||
abstract = {Wikipedia may be the best-developed attempt thus far to gather all human knowledge in one place. Its accomplishments in this regard have made it a point of inquiry for researchers from different fields of knowledge. A decade of research has thrown light on many aspects of the Wikipedia community, its processes, and its content. However, due to the variety of fields inquiring about Wikipedia and the limited synthesis of the extensive research, there is little consensus on many aspects of Wikipedia's content as an encyclopedic collection of human knowledge. This study addresses the issue by systematically reviewing 110 peer-reviewed publications on Wikipedia content, summarizing the current findings, and highlighting the major research trends. Two major streams of research are identified: the quality of Wikipedia content (including comprehensiveness, currency, readability, and reliability) and the size of Wikipedia. Moreover, we present the key research trends in terms of the domains of inquiry, research design, data source, and data gathering methods. This review synthesizes scholarly understanding of Wikipedia content and paves the way for future studies.},
|
||
copyright = {\textcopyright{} 2014 ASIS\&T},
|
||
langid = {english},
|
||
keywords = {encyclopedias,quality,reliability},
|
||
annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/asi.23172},
|
||
file = {/home/nathante/Zotero/storage/9MGZIS9V/Mesgari et al_2015_“The sum of all human knowledge”.pdf;/home/nathante/Zotero/storage/H5F2PUN5/asi.html}
|
||
}
|
||
|
||
@article{michlmayr_quality_2003,
|
||
title = {Quality and the {{Reliance}} on {{Individuals}} in {{Free Software Projects}}},
|
||
author = {Michlmayr, Martin and Hill, Benjamin Mako},
|
||
year = {2003},
|
||
journal = {3rd Workshop on Open Source Software Engineering, ICSE},
|
||
abstract = {It has been suggested that the superior quality of many Free Software projects in comparison to their proprietary counterparts is in part due to the Free Software commu- nity's extensive source code peer-review process. While many argue that software is best developed by individuals or small teams, the process of debugging is highly paral- lizable. This ``one and many'' model describes a template employed by many Free Software projects. However, re- liance on a single developer or maintainer creates a sin- gle point of failure that raises a number of serious quality and reliability concerns \textendash{} especially when considered in the context of the volunteer-based nature of most Free Software projects. This paper will investigate the nature of problems raised by this model within the Debian Project and will ex- plore several possible strategies aimed at removing or de- emphasizing the reliance on individual developers.}
|
||
}
|
||
|
||
@article{miquel-ribe_wikipedia_2018,
|
||
title = {Wikipedia {{Culture Gap}}: {{Quantifying Content Imbalances Across}} 40 {{Language Editions}}},
|
||
shorttitle = {Wikipedia {{Culture Gap}}},
|
||
author = {{Miquel-Rib{\'e}}, Marc and Laniado, David},
|
||
year = {2018},
|
||
journal = {Frontiers in Physics},
|
||
volume = {6},
|
||
issn = {2296-424X},
|
||
abstract = {The online encyclopedia Wikipedia is the largest general information repository created through collaborative efforts from all over the globe. Despite the project's goal being to achieve the sum of human knowledge, there are strong content imbalances across the language editions. In order to quantify and investigate these imbalances, we study the impact of cultural context in 40 language editions. To this purpose, we developed a computational method to identify articles that can be related to the editors' cultural context associated to each Wikipedia language edition. We employed a combination of strategies taking into account geolocated articles, specific keywords and categories, as well as links between articles. We verified the method's quality with manual assessment and found an average precision of 0.92 and an average recall of 0.95. The results show that about a quarter of each Wikipedia language edition is dedicated to represent the corresponding cultural context. Although a considerable part of this content was created during the first years of the project, its creation is sustained over time. An analysis of cross-language coverage of this content shows that most of it is unique in its original language, and reveals special links between cultural contexts; at the same time, it highlights gaps where the encyclopaedia could extend its content. The approach and findings presented in this study can help to foster participation and inter-cultural enrichment of Wikipedias. The datasets produced are made available for further research.},
|
||
langid = {english},
|
||
keywords = {Big Data.,content imbalance,cross-cultural studies,Cultural Diversity,Data Collection,Data Mining,Digital Humanities,online communities,Wikipedia},
|
||
file = {/home/nathante/Zotero/storage/WJSZZBVF/Miquel-Ribé_Laniado_2018_Wikipedia Culture Gap.pdf}
|
||
}
|
||
|
||
@misc{noauthor_ordinal_nodate,
|
||
title = {Ordinal {{Regression}}},
|
||
howpublished = {https://betanalpha.github.io/assets/case\_studies/ordinal\_regression.html},
|
||
file = {/home/nathante/Zotero/storage/5CLVS2WM/ordinal_regression.html}
|
||
}
|
||
|
||
@article{pedregosa_scikit-learn_2011,
|
||
title = {Scikit-Learn: {{Machine Learning}} in {{Python}}},
|
||
shorttitle = {Scikit-Learn},
|
||
author = {Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David and Brucher, Matthieu and Perrot, Matthieu and Duchesnay, {\'E}douard},
|
||
year = {2011},
|
||
journal = {Journal of Machine Learning Research},
|
||
volume = {12},
|
||
number = {85},
|
||
pages = {2825--2830},
|
||
abstract = {Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.},
|
||
file = {/home/nathante/Zotero/storage/AWW8RZYB/Pedregosa et al_2011_Scikit-learn.pdf}
|
||
}
|
||
|
||
@book{phoebe_ayers_how_2008,
|
||
title = {How {{Wikipedia Works}}},
|
||
author = {{Phoebe Ayers} and {Charles Matthews} and {Ben Yates}},
|
||
year = {2008},
|
||
publisher = {{No Starch Press}},
|
||
abstract = {"We cover Wikipedia from soup to nuts: for readers trying to understand what's in Wikipedia, how and why it got there, and how to analyze the quality of the content you might find on the site; for current and future editors, from basic editing techniques and wikisyntax to not-so-basic information on complicated syntax, referencing and researching content, and editing collaboratively and harmoniously; and finally for anyone interested in how Wikipedia's vibrant and complicated community comes together to produce content, resolve disputes, and keep the site running. Finally, we touch on the wider world of Wikipedias in other languages, other Wikimedia projects, and the Wikimedia Foundation itself. We close with appendices about reusing Wikipedia content according to the terms of the GFDL license, and thoughts on using Wikipedia in a classroom setting. "Throughout, we provide community consensus viewpoints and our own thoughts on a common-sense approach to using and participating in Wikipedia, and a selection of carefully-chosen links to the thousands of pages of documentation, help and Wikipedia-space pages that we discuss -- not to mention a sprinkling of humor. In every discussion, we try to provide a sense of the community that supports and is at the heart of the Wikipedia project and mission." -- Phoebe Ayers,},
|
||
collaborator = {{Phoebe Ayers; Charles Matthews; Ben Yates}},
|
||
copyright = {Copyright (C) 2008 by Phoebe Ayers, Charles Matthews, and Ben Yates Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and with the Back-Cover Texts being "How Wikipedia Works", by Phoebe Ayers, Charles Matthews, and Ben Yates, published by No Starch Press. A copy of the license is included in the section entitled "GNU Free Documentation License".},
|
||
langid = {english},
|
||
keywords = {documentation,encyclopedias,Mediawiki,Social media,User-generated content,Wikimedia,Wikipedia,Wikipedia--Handbooks; manuals; etc.},
|
||
file = {/home/nathante/Zotero/storage/MB2AZG45/HowWikipediaWorks%2FHowWikipediaWorks.epub}
|
||
}
|
||
|
||
@inproceedings{raman_classifying_2020,
|
||
title = {Classifying {{Wikipedia Article Quality With Revision History Networks}}},
|
||
booktitle = {Proceedings of the 16th {{International Symposium}} on {{Open Collaboration}}},
|
||
author = {Raman, Narun and Sauerberg, Nathaniel and Fisher, Jonah and Narayan, Sneha},
|
||
year = {2020},
|
||
month = aug,
|
||
series = {{{OpenSym}} 2020},
|
||
pages = {1--7},
|
||
publisher = {{Association for Computing Machinery}},
|
||
address = {{New York, NY, USA}},
|
||
abstract = {We present a novel model for classifying the quality of Wikipedia articles based on structural properties of a network representation of the article's revision history. We create revision history networks (an adaptation of Keegan et. al's article trajectory networks [7]), where nodes correspond to individual editors of an article, and edges join the authors of consecutive revisions. Using descriptive statistics generated from these networks, along with general properties like the number of edits and article size, we predict which of six quality classes (Start, Stub, C-Class, B-Class, Good, Featured) articles belong to, attaining a classification accuracy of 49.35\% on a stratified sample of articles. These results suggest that structures of collaboration underlying the creation of articles, and not just the content of the article, should be considered for accurate quality classification.},
|
||
isbn = {978-1-4503-8779-8},
|
||
keywords = {article quality,classification,collaboration,network analysis,quantitative methods,Wikipedia},
|
||
file = {/home/nathante/Zotero/storage/3ZJCZI6W/Raman et al_2020_Classifying Wikipedia Article Quality With Revision History Networks.pdf}
|
||
}
|
||
|
||
@article{reavley_quality_2012,
|
||
title = {Quality of Information Sources about Mental Disorders: A Comparison of {{Wikipedia}} with Centrally Controlled Web and Printed Sources},
|
||
shorttitle = {Quality of Information Sources about Mental Disorders},
|
||
author = {Reavley, N. J. and Mackinnon, A. J. and Morgan, A. J. and {Alvarez-Jimenez}, M. and Hetrick, S. E. and Killackey, E. and Nelson, B. and Purcell, R. and Yap, M. B. H. and Jorm, A. F.},
|
||
year = {2012},
|
||
month = aug,
|
||
journal = {Psychological Medicine},
|
||
volume = {42},
|
||
number = {8},
|
||
pages = {1753--1762},
|
||
issn = {1469-8978, 0033-2917},
|
||
abstract = {Background Although mental health information on the internet is often of poor quality, relatively little is known about the quality of websites, such as Wikipedia, that involve participatory information sharing. The aim of this paper was to explore the quality of user-contributed mental health-related information on Wikipedia and compare this with centrally controlled information sources. Method Content on 10 mental health-related topics was extracted from 14 frequently accessed websites (including Wikipedia) providing information about depression and schizophrenia, Encyclopaedia Britannica, and a psychiatry textbook. The content was rated by experts according to the following criteria: accuracy, up-to-dateness, breadth of coverage, referencing and readability. Results Ratings varied significantly between resources according to topic. Across all topics, Wikipedia was the most highly rated in all domains except readability. Conclusions The quality of information on depression and schizophrenia on Wikipedia is generally as good as, or better than, that provided by centrally controlled websites, Encyclopaedia Britannica and a psychiatry textbook.},
|
||
langid = {english},
|
||
file = {/home/nathante/Zotero/storage/PPKE7WIA/Reavley et al_2012_Quality of information sources about mental disorders.pdf;/home/nathante/Zotero/storage/YMZK3KM8/595CEE672BB7C503101FAF5A9E303673.html}
|
||
}
|
||
|
||
@inproceedings{recht_imagenet_2019,
|
||
title = {Do {{ImageNet Classifiers Generalize}} to {{ImageNet}}?},
|
||
booktitle = {International {{Conference}} on {{Machine Learning}}},
|
||
author = {Recht, Benjamin and Roelofs, Rebecca and Schmidt, Ludwig and Shankar, Vaishaal},
|
||
year = {2019},
|
||
month = may,
|
||
pages = {5389--5400},
|
||
publisher = {{PMLR}},
|
||
issn = {2640-3498},
|
||
abstract = {We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to excessively re-used ...},
|
||
langid = {english},
|
||
file = {/home/nathante/Zotero/storage/D7JPAXNZ/Recht et al_2019_Do ImageNet Classifiers Generalize to ImageNet.pdf;/home/nathante/Zotero/storage/HH6SE7TA/recht19a.html}
|
||
}
|
||
|
||
@article{redi_taxonomy_2021,
|
||
title = {A {{Taxonomy}} of {{Knowledge Gaps}} for {{Wikimedia Projects}} ({{Second Draft}})},
|
||
author = {Redi, Miriam and Gerlach, Martin and Johnson, Isaac and Morgan, Jonathan and Zia, Leila},
|
||
year = {2021},
|
||
month = jan,
|
||
journal = {arXiv:2008.12314 [cs]},
|
||
eprint = {2008.12314},
|
||
eprinttype = {arxiv},
|
||
primaryclass = {cs},
|
||
abstract = {In January 2019, prompted by the Wikimedia Movement's 2030 strategic direction, the Research team at the Wikimedia Foundation identified the need to develop a knowledge gaps index -- a composite index to support the decision makers across the Wikimedia movement by providing: a framework to encourage structured and targeted brainstorming discussions; data on the state of the knowledge gaps across the Wikimedia projects that can inform decision making and assist with measuring the long term impact of large scale initiatives in the Movement. After its first release in July 2020, the Research team has developed the second complete draft of a taxonomy of knowledge gaps for the Wikimedia projects, as the first step towards building the knowledge gap index. We studied more than 250 references by scholars, researchers, practitioners, community members and affiliates -- exposing evidence of knowledge gaps in readership, contributorship, and content of Wikimedia projects. We elaborated the findings and compiled the taxonomy of knowledge gaps in this paper, where we describe, group and classify knowledge gaps into a structured framework. The taxonomy that you will learn more about in the rest of this work will serve as a basis to operationalize and quantify knowledge equity, one of the two 2030 strategic directions, through the knowledge gaps index.},
|
||
archiveprefix = {arXiv},
|
||
langid = {english},
|
||
keywords = {Computer Science - Computers and Society},
|
||
file = {/home/nathante/Zotero/storage/TIFWV8J6/Redi et al. - 2021 - A Taxonomy of Knowledge Gaps for Wikimedia Project.pdf}
|
||
}
|
||
|
||
@article{sarkar_stre_2019,
|
||
title = {{{StRE}}: {{Self Attentive Edit Quality Prediction}} in {{Wikipedia}}},
|
||
shorttitle = {{{StRE}}},
|
||
author = {Sarkar, Soumya and Reddy, Bhanu Prakash and Sikdar, Sandipan and Mukherjee, Animesh},
|
||
year = {2019},
|
||
month = jun,
|
||
journal = {arXiv:1906.04678 [cs]},
|
||
eprint = {1906.04678},
|
||
eprinttype = {arxiv},
|
||
primaryclass = {cs},
|
||
abstract = {Wikipedia can easily be justified as a behemoth, considering the sheer volume of content that is added or removed every minute to its several projects. This creates an immense scope, in the field of natural language processing towards developing automated tools for content moderation and review. In this paper we propose Self Attentive Revision Encoder (StRE) which leverages orthographic similarity of lexical units toward predicting the quality of new edits. In contrast to existing propositions which primarily employ features like page reputation, editor activity or rule based heuristics, we utilize the textual content of the edits which, we believe contains superior signatures of their quality. More specifically, we deploy deep encoders to generate representations of the edits from its text content, which we then leverage to infer quality. We further contribute a novel dataset containing 21M revisions across 32K Wikipedia pages and demonstrate that StRE outperforms existing methods by a significant margin at least 17\% and at most 103\%. Our pretrained model achieves such result after retraining on a set as small as 20\% of the edits in a wikipage. This, to the best of our knowledge, is also the first attempt towards employing deep language models to the enormous domain of automated content moderation and review in Wikipedia.},
|
||
archiveprefix = {arXiv},
|
||
keywords = {Computer Science - Neural and Evolutionary Computing,Computer Science - Social and Information Networks},
|
||
file = {/home/nathante/Zotero/storage/3BAHCLC7/Sarkar et al_2019_StRE.pdf;/home/nathante/Zotero/storage/DSMFT5CS/1906.html}
|
||
}
|
||
|
||
@inproceedings{schmidt_article_2019,
|
||
title = {Article Quality Classification on {{Wikipedia}}: Introducing Document Embeddings and Content Features},
|
||
shorttitle = {Article Quality Classification on {{Wikipedia}}},
|
||
booktitle = {Proceedings of the 15th {{International Symposium}} on {{Open Collaboration}}},
|
||
author = {Schmidt, Manuel and Zangerle, Eva},
|
||
year = {2019},
|
||
month = aug,
|
||
series = {{{OpenSym}} '19},
|
||
pages = {1--8},
|
||
publisher = {{Association for Computing Machinery}},
|
||
address = {{New York, NY, USA}},
|
||
abstract = {The quality of articles on the Wikipedia platform is vital for its success. Currently, the assessment of quality is performed manually by the Wikipedia community, where editors classify articles into pre-defined quality classes. However, this approach is hardly scalable and hence, approaches for the automatic classification have been investigated. In this paper, we extend this previous line of research on article quality classification by extending the set of features with novel content and edit features (e.g., document em-beddings of articles). We propose a classification approach utilizing gradient boosted trees based on this novel, extended set of features extracted from Wikipedia articles. Based on an established dataset containing Wikipedia articles and quality classes, we show that our approach is able to substantially outperform previous approaches (also including recent deep learning methods). Furthermore, we shed light on the contribution of individual features and show that the proposed features indeed capture the quality of an article well.},
|
||
isbn = {978-1-4503-6319-8},
|
||
keywords = {classification,collaborative information systems,gradient boosted trees,information quality,Wikipedia},
|
||
file = {/home/nathante/Zotero/storage/N8QRISAN/Schmidt_Zangerle_2019_Article quality classification on Wikipedia.pdf}
|
||
}
|
||
|
||
@inproceedings{sheppard_quality_2011,
|
||
title = {Quality Is a {{Verb}}: {{The Operationalization}} of {{Data Quality}} in a {{Citizen Science Community}}},
|
||
shorttitle = {Quality Is a {{Verb}}},
|
||
booktitle = {Proceedings of the 7th {{International Symposium}} on {{Wikis}} and {{Open Collaboration}}},
|
||
author = {Sheppard, S. Andrew and Terveen, Loren},
|
||
year = {2011},
|
||
series = {{{WikiSym}} '11},
|
||
pages = {29--38},
|
||
publisher = {{ACM}},
|
||
address = {{New York, NY, USA}},
|
||
abstract = {Citizen science is becoming more valuable as a potential source of environmental data. Involving citizens in data collection has the added educational benefits of increased scientific awareness and local ownership of environmental concerns. However, a common concern among domain experts is the presumed lower quality of data submitted by volunteers. In this paper, we explore data quality assurance practices in River Watch, a community-based monitoring program in the Red River basin. We investigate how the participants in River Watch understand and prioritize data quality concerns. We found that data quality in River Watch is primarily maintained through universal adherence to standard operating procedures, but there remain areas where technological intervention may help. We also found that rigorous data quality assurance practices appear to enhance rather than hinder the educational goals of the program. We draw implications for the design of quality assurance mechanisms for River Watch and other citizen science projects.},
|
||
isbn = {978-1-4503-0909-7},
|
||
file = {/home/nathante/Zotero/storage/AW9CJY5B/Sheppard_Terveen_2011_Quality is a Verb.pdf}
|
||
}
|
||
|
||
@article{shi_wisdom_2019,
|
||
ids = {shi_wisdom_2019-1},
|
||
title = {The Wisdom of Polarized Crowds},
|
||
author = {Shi, Feng and Teplitskiy, Misha and Duede, Eamon and Evans, James A.},
|
||
year = {2019},
|
||
month = apr,
|
||
journal = {Nature Human Behaviour},
|
||
volume = {3},
|
||
number = {4},
|
||
pages = {329--336},
|
||
publisher = {{Nature Publishing Group}},
|
||
issn = {2397-3374},
|
||
abstract = {As political polarization in the United States continues to rise1\textendash 3, the question of whether polarized individuals can fruitfully cooperate becomes pressing. Although diverse perspectives typically lead to superior team performance on complex tasks4,5, strong political perspectives have been associated with conflict, misinformation and a reluctance to engage with people and ideas beyond one's echo chamber6\textendash 8. Here, we explore the effect of ideological composition on team performance by analysing millions of edits to Wikipedia's political, social issues and science articles. We measure editors' online ideological preferences by how much they contribute to conservative versus liberal articles. Editor surveys suggest that online contributions associate with offline political party affiliation and ideological self-identity. Our analysis reveals that polarized teams consisting of a balanced set of ideologically diverse editors produce articles of a higher quality than homogeneous teams. The effect is most clearly seen in Wikipedia's political articles, but also in social issues and even science articles. Analysis of article `talk pages' reveals that ideologically polarized teams engage in longer, more constructive, competitive and substantively focused but linguistically diverse debates than teams of ideological moderates. More intense use of Wikipedia policies by ideologically diverse teams suggests institutional design principles to help unleash the power of polarization.},
|
||
copyright = {2019 The Author(s), under exclusive licence to Springer Nature Limited},
|
||
langid = {english},
|
||
file = {/home/nathante/Zotero/storage/SUP5HZ6U/Shi et al_2019_The wisdom of polarized crowds.pdf;/home/nathante/Zotero/storage/YT9TVD7R/Shi et al_2019_The wisdom of polarized crowds.pdf;/home/nathante/Zotero/storage/D8DIA97B/s41562-019-0541-6.html;/home/nathante/Zotero/storage/JB34TKTT/s41562-019-0541-6.html}
|
||
}
|
||
|
||
@article{strathern_improving_1997,
|
||
title = {`{{Improving}} Ratings': Audit in the {{British University}} System},
|
||
shorttitle = {`{{Improving}} Ratings'},
|
||
author = {Strathern, Marilyn},
|
||
year = {1997},
|
||
month = jul,
|
||
journal = {European Review},
|
||
volume = {5},
|
||
number = {3},
|
||
pages = {305--321},
|
||
publisher = {{Cambridge University Press}},
|
||
issn = {1474-0575, 1062-7987},
|
||
abstract = {This paper gives an anthropological comment on what has been called the `audit explosion', the proliferation of procedures for evaluating performance. In higher education the subject of audit (in this sense) is not so much the education of the students as the institutional provision for their education. British universities, as institutions, are increasingly subject to national scrutiny for teaching, research and administrative competence. In the wake of this scrutiny comes a new cultural apparatus of expectations and technologies. While the metaphor of financial auditing points to the important values of accountability, audit does more than monitor\textemdash it has a life of its own that jeopardizes the life it audits. The runaway character of assessment practices is analysed in terms of cultural practice. Higher education is intimately bound up with the origins of such practices, and is not just the latter day target of them. \textcopyright{} 1997 by John Wiley \& Sons, Ltd.},
|
||
langid = {english},
|
||
file = {/home/nathante/Zotero/storage/8BHAJ9RN/Strathern_1997_‘Improving ratings’.pdf;/home/nathante/Zotero/storage/SWKTUUPH/FC2EE640C0C44E3DB87C29FB666E9AAB.html}
|
||
}
|
||
|
||
@inproceedings{teblunthuis_dwelling_2019,
|
||
ids = {teblunthuis_dwelling_2019-1},
|
||
title = {Dwelling on {{Wikipedia}}: {{Investigating}} Time Spent by Global Encyclopedia Readers},
|
||
booktitle = {{{OpenSym}} '19, {{The}} 15th {{International Symposium}} on {{Open Collaboration}}},
|
||
author = {TeBlunthuis, Nathan and Bayer, Tilman and Vasileva, Olga},
|
||
year = {2019},
|
||
month = aug,
|
||
pages = {14},
|
||
address = {{Sk\"ovde, Sweden}},
|
||
langid = {english},
|
||
file = {/home/nathante/Zotero/storage/5NGFRX7L/TeBlunthuis et al_2019_Dwelling on Wikipedia.pdf;/home/nathante/Zotero/storage/JR9UCSKW/TeBlunthuis et al. - Dwelling on Wikipedia Investigating time spent by.pdf}
|
||
}
|
||
|
||
@article{teblunthuis_effects_2021,
|
||
ids = {teblunthuis_effects_2020},
|
||
title = {Effects of {{Algorithmic Flagging}} on {{Fairness}}: {{Quasi-experimental Evidence}} from {{Wikipedia}}},
|
||
shorttitle = {Effects of {{Algorithmic Flagging}} on {{Fairness}}},
|
||
author = {TeBlunthuis, Nathan and Hill, Benjamin Mako and Halfaker, Aaron},
|
||
year = {2021},
|
||
month = apr,
|
||
journal = {Proceedings of the ACM on Human-Computer Interaction},
|
||
volume = {5},
|
||
number = {CSCW1},
|
||
eprint = {2006.03121},
|
||
eprinttype = {arxiv},
|
||
pages = {56:1--56:27},
|
||
abstract = {Online community moderators often rely on social signals such as whether or not a user has an account or a profile page as clues that users may cause problems. Reliance on these clues can lead to "overprofiling'' bias when moderators focus on these signals but overlook the misbehavior of others. We propose that algorithmic flagging systems deployed to improve the efficiency of moderation work can also make moderation actions more fair to these users by reducing reliance on social signals and making norm violations by everyone else more visible. We analyze moderator behavior in Wikipedia as mediated by RCFilters, a system which displays social signals and algorithmic flags, and estimate the causal effect of being flagged on moderator actions. We show that algorithmically flagged edits are reverted more often, especially those by established editors with positive social signals, and that flagging decreases the likelihood that moderation actions will be undone. Our results suggest that algorithmic flagging systems can lead to increased fairness in some contexts but that the relationship is complex and contingent.},
|
||
archiveprefix = {arXiv},
|
||
keywords = {ai,causal inference,community norms,Computer Science - Computers and Society,Computer Science - Human-Computer Interaction,Computer Science - Machine Learning,Computer Science - Social and Information Networks,fairness,K.4.3,machine learning,moderation,online communities,peer production,sociotechnical systems,wikipedia},
|
||
file = {/home/nathante/Zotero/storage/9LEWQEUJ/TeBlunthuis et al_2020_The effects of algorithmic flagging on fairness.pdf;/home/nathante/Zotero/storage/DYFEYFUT/TeBlunthuis et al_2021_Effects of Algorithmic Flagging on Fairness.pdf;/home/nathante/Zotero/storage/EQV69NYF/2006.html}
|
||
}
|
||
|
||
@inproceedings{tran_are_2020,
|
||
title = {Are Anonymity-Seekers Just like Everybody Else? {{An}} Analysis of Contributions to {{Wikipedia}} from {{Tor}}},
|
||
shorttitle = {Are Anonymity-Seekers Just like Everybody Else?},
|
||
booktitle = {2020 {{IEEE Symposium}} on {{Security}} and {{Privacy}} ({{SP}})},
|
||
author = {Tran, Chau and Champion, Kaylea and Forte, Andrea and Hill, Benjamin Mako and Greenstadt, Rachel},
|
||
year = {2020},
|
||
volume = {1},
|
||
pages = {974--990},
|
||
publisher = {{IEEE Computer Society}},
|
||
address = {{San Francisco, California}},
|
||
abstract = {User-generated content sites routinely block contributions from users of privacy-enhancing proxies like Tor because of a perception that proxies are a source of vandalism, spam, and abuse. Although these blocks might be effective, collateral damage in the form of unrealized valuable contributions from anonymity seekers is invisible. One of the largest and most important user-generated content sites, Wikipedia, has attempted to block contributions from Tor users since as early as 2005. We demonstrate that these blocks have been imperfect and that thousands of attempts to edit on Wikipedia through Tor have been successful. We draw upon several data sources and analytical techniques to measure and describe the history of Tor editing on Wikipedia over time and to compare contributions from Tor users to those from other groups of Wikipedia users. Our analysis suggests that although Tor users who slip through Wikipedia's ban contribute content that is more likely to be reverted and to revert others, their contributions are otherwise similar in quality to those from other unregistered participants and to the initial contributions of registered users.},
|
||
langid = {english},
|
||
file = {/home/nathante/Zotero/storage/RGAM25XB/1j2LfZYlubC.html}
|
||
}
|
||
|
||
@article{tripodi_ms_2021,
|
||
title = {Ms. {{Categorized}}: {{Gender}}, Notability, and Inequality on {{Wikipedia}}},
|
||
shorttitle = {Ms. {{Categorized}}},
|
||
author = {Tripodi, Francesca},
|
||
year = {2021},
|
||
month = jun,
|
||
journal = {New Media \& Society},
|
||
pages = {14614448211023772},
|
||
publisher = {{SAGE Publications}},
|
||
issn = {1461-4448},
|
||
abstract = {Gender is one of the most pervasive and insidious forms of inequality. For example, English-language Wikipedia contains more than 1.5 million biographies about notable writers, inventors, and academics, but less than 19\% of these biographies are about women. To try and improve these statistics, activists host ``edit-a-thons'' to increase the visibility of notable women. While this strategy helps create several biographies previously inexistent, it fails to address a more inconspicuous form of gender exclusion. Drawing on ethnographic observations, interviews, and quantitative analysis of web-scraped metadata, this article demonstrates that biographies about women who meet Wikipedia's criteria for inclusion are more frequently considered non-notable and nominated for deletion compared to men's biographies. This disproportionate rate is another dimension of gender inequality previously unexplored by social scientists and provides broader insights into how women's achievements are (under)valued.},
|
||
langid = {english},
|
||
keywords = {Articles for Deletion,gender gap,gender inequality,metadata,Wikipedia},
|
||
file = {/home/nathante/Zotero/storage/QAXNPJ72/Tripodi_2021_Ms.pdf}
|
||
}
|
||
|
||
@article{van_der_velden_decentering_2013,
|
||
title = {Decentering {{Design}}: {{Wikipedia}} and {{Indigenous Knowledge}}},
|
||
shorttitle = {Decentering {{Design}}},
|
||
author = {{van der Velden}, Maja},
|
||
year = {2013},
|
||
month = mar,
|
||
journal = {International Journal of Human\textendash Computer Interaction},
|
||
volume = {29},
|
||
number = {4},
|
||
pages = {308--316},
|
||
publisher = {{Taylor \& Francis}},
|
||
issn = {1044-7318},
|
||
abstract = {This article is a reflection on the case of Wikipedia, the largest online reference site with 23 million articles, with 365 million readers, and without a page called Indigenous knowledge. A Postcolonial Computing lens, extended with the notion of decentering, is used to find out what happened with Indigenous knowledge in Wikipedia. Wikipedia's ordering technologies, such as policies and templates, play a central role in producing knowledge. Two designs, developed with and for Indigenous communities, are introduced to explore if another Wikipedia's design is possible.},
|
||
annotation = {\_eprint: https://doi.org/10.1080/10447318.2013.765768},
|
||
file = {/home/nathante/Zotero/storage/IU8S7FRL/van der Velden_2013_Decentering Design.pdf;/home/nathante/Zotero/storage/SIRXEIGA/10447318.2013.html}
|
||
}
|
||
|
||
@article{vehtari_practical_2017,
|
||
ids = {vehtari_practical_2017-1},
|
||
title = {Practical {{Bayesian}} Model Evaluation Using Leave-One-out Cross-Validation and {{WAIC}}},
|
||
author = {Vehtari, Aki and Gelman, Andrew and Gabry, Jonah},
|
||
year = {2017},
|
||
month = sep,
|
||
journal = {Statistics and Computing},
|
||
volume = {27},
|
||
number = {5},
|
||
eprint = {1507.04544},
|
||
eprinttype = {arxiv},
|
||
pages = {1413--1432},
|
||
issn = {0960-3174, 1573-1375},
|
||
abstract = {Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior simulations of the parameter values. LOO and WAIC have various advantages over simpler estimates of predictive error such as AIC and DIC but are less used in practice because they involve additional computational steps. Here we lay out fast and stable computations for LOO and WAIC that can be performed using existing simulation draws. We introduce an efficient computation of LOO using Pareto-smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. Although WAIC is asymptotically equal to LOO, we demonstrate that PSIS-LOO is more robust in the finite case with weak priors or influential observations. As a byproduct of our calculations, we also obtain approximate standard errors for estimated predictive errors and for comparing of predictive errors between two models. We implement the computations in an R package called 'loo' and demonstrate using models fit with the Bayesian inference package Stan.},
|
||
archiveprefix = {arXiv},
|
||
keywords = {Statistics - Computation,Statistics - Methodology},
|
||
file = {/home/nathante/Zotero/storage/M5H8F7EZ/Vehtari et al_2017_Practical Bayesian model evaluation using leave-one-out cross-validation and.pdf;/home/nathante/Zotero/storage/PHFMKLFX/Vehtari et al. - 2017 - Practical Bayesian model evaluation using leave-on.pdf;/home/nathante/Zotero/storage/AW3FBQRP/1507.html;/home/nathante/Zotero/storage/LKATPX25/1507.html}
|
||
}
|
||
|
||
@book{venables_modern_2002,
|
||
title = {Modern Applied Statistics with {{S}}},
|
||
author = {Venables, W. N and Ripley, Brian D and Venables, W. N},
|
||
year = {2002},
|
||
publisher = {{Springer}},
|
||
address = {{New York}},
|
||
abstract = {S is a powerful environment for the statistical and graphical analysis of data. It provides the tools to implement many statistical ideas that have been made possible by the widespread availability of workstations having good graphics and computational capabilities. This book is a guide to using S environments to perform statistical analyses and provides both an introduction to the use of S and a course in modern statistical methods. Implementations of S are available commercially in S-PLUS(R) workstations and as the Open Source R for a wide range of computer systems. The aim of this book is to show how to use S as a powerful and graphical data analysis system. Readers are assumed to have a basic grounding in statistics, and so the book is intended for would-be users of S-PLUS or R and both students and researchers using statistics. Throughout, the emphasis is on presenting practical problems and full analyses of real data sets. Many of the methods discussed are state of the art approaches to topics such as linear, nonlinear and smooth regression models, tree-based methods, multivariate analysis, pattern recognition, survival analysis, time series and spatial statistics. Throughout modern techniques such as robust methods, non-parametric smoothing and bootstrapping are used where appropriate. This fourth edition is intended for users of S-PLUS 6.0 or R 1.5.0 or later. A substantial change from the third edition is updating for the current versions of S-PLUS and adding coverage of R. The introductory material has been rewritten to emphasis the import, export and manipulation of data. Increased computational power allows even more computer-intensive methods to be used, and methods such as GLMMs.},
|
||
isbn = {9780387954578 9786610189373 9781280189371},
|
||
langid = {english},
|
||
annotation = {OCLC: 1058013209}
|
||
}
|
||
|
||
@article{volsky_quality_2012,
|
||
ids = {volsky_quality_2012-1},
|
||
title = {Quality of {{Internet}} Information in Pediatric Otolaryngology: {{A}} Comparison of Three Most Referenced Websites},
|
||
shorttitle = {Quality of {{Internet}} Information in Pediatric Otolaryngology},
|
||
author = {Volsky, Peter G. and Baldassari, Cristina M. and Mushti, Sirisha and Derkay, Craig S.},
|
||
year = {2012},
|
||
month = sep,
|
||
journal = {International Journal of Pediatric Otorhinolaryngology},
|
||
volume = {76},
|
||
number = {9},
|
||
pages = {1312--1316},
|
||
issn = {0165-5876},
|
||
abstract = {Objective Patients commonly refer to Internet health-related information. To date, no quantitative comparison of the accuracy and readability of common diagnoses in Pediatric Otolaryngology exist. Study aims: (1) identify the three most frequently referenced Internet sources; (2) compare the content accuracy and (3) ascertain user-friendliness of each site; (4) inform practitioners and patients of the quality of available information. Methods Twenty-four diagnoses in pediatric otolaryngology were entered in Google and the top five URLs for each were ranked. Articles were accessed for each topic in the three most frequently referenced sites. Standard rubrics were developed to include proprietary scores for content, errors, navigability, and validated metrics of readability. Results Wikipedia, eMedicine, and NLM/NIH MedlinePlus were the most referenced sources. For content accuracy, eMedicine scored highest (84\%; p \< 0.05) over MedlinePlus (49\%) and Wikipedia (46\%). The highest incidence of errors and omissions per article was found in Wikipedia (0.98 {$\pm$} 0.19), twice more than eMedicine (0.42 {$\pm$} 0.19; p \< 0.05). Errors were similar between MedlinePlus and both eMedicine and Wikipedia. On ratings for user interface, which incorporated Flesch\textendash Kinkaid Reading Level and Flesch Reading Ease, MedlinePlus was the most user-friendly (4.3 {$\pm$} 0.29). This was nearly twice that of eMedicine (2.4 {$\pm$} 0.26) and slightly greater than Wikipedia (3.7 {$\pm$} 0.3). All differences were significant (p \< 0.05). There were 7 topics for which articles were not available on MedlinePlus. Conclusions Knowledge of the quality of available information on the Internet improves pediatric otolaryngologists' ability to counsel parents. The top web search results for pediatric otolaryngology diagnoses are Wikipedia, MedlinePlus, and eMedicine. Online information varies in quality, with a 46\textendash 84\% concordance with current textbooks. eMedicine has the most accurate, comprehensive content and fewest errors, but is more challenging to read and navigate. Both Wikipedia and MedlinePlus have lower content accuracy and more errors, however MedlinePlus is simplest of all to read, at a 9th Grade level.},
|
||
file = {/home/nathante/Zotero/storage/KQ3G6CNY/Volsky et al_2012_Quality of Internet information in pediatric otolaryngology.pdf;/home/nathante/Zotero/storage/UMX6FM8I/S0165587612003369.html}
|
||
}
|
||
|
||
@article{warncke-wang_misalignment_2015-1,
|
||
title = {Misalignment {{Between Supply}} and {{Demand}} of {{Quality Content}} in {{Peer Production Communities}}},
|
||
author = {{Warncke-Wang}, Morten and Ranjan, Vivek and Terveen, Loren and Hecht, Brent},
|
||
year = {2015},
|
||
month = apr,
|
||
journal = {Proceedings of the International AAAI Conference on Web and Social Media},
|
||
volume = {9},
|
||
number = {1},
|
||
issn = {2334-0770},
|
||
copyright = {Copyright (c)},
|
||
langid = {english},
|
||
keywords = {Wikipedia},
|
||
file = {/home/nathante/Zotero/storage/EUK8XAYT/Warncke-Wang et al_2015_Misalignment Between Supply and Demand of Quality Content in Peer Production.pdf}
|
||
}
|
||
|
||
@inproceedings{warncke-wang_success_2015,
|
||
ids = {warncke-wang_success_2015-1},
|
||
title = {The {{Success}} and {{Failure}} of {{Quality Improvement Projects}} in {{Peer Production Communities}}},
|
||
booktitle = {Proceedings of the 18th {{ACM Conference}} on {{Computer Supported Cooperative Work}} \& {{Social Computing}}},
|
||
author = {{Warncke-Wang}, Morten and Ayukaev, Vladislav R. and Hecht, Brent and Terveen, Loren G.},
|
||
year = {2015},
|
||
month = feb,
|
||
series = {{{CSCW}} '15},
|
||
pages = {743--756},
|
||
publisher = {{Association for Computing Machinery}},
|
||
address = {{New York, NY, USA}},
|
||
abstract = {Peer production communities have been proven to be successful at creating valuable artefacts, with Wikipedia as a prime example. However, a number of studies have shown that work in these communities tends to be of uneven quality and certain content areas receive more attention than others. In this paper, we examine the efficacy of a range of targeted strategies to increase the quality of under-attended content areas in peer production communities. Mining data from five quality improvement projects in the English Wikipedia, the largest peer production community in the world, we show that certain types of strategies (e.g. creating artefacts from scratch) have better quality outcomes than others (e.g. improving existing artefacts), even if both are done by a similar cohort of participants. We discuss the implications of our findings for Wikipedia as well as other peer production communities.},
|
||
isbn = {978-1-4503-2922-4},
|
||
keywords = {peer production,quality modelling,user-generated content,wikipedia},
|
||
file = {/home/nathante/Zotero/storage/7RKRZ5J9/Warncke-Wang et al_2015_The Success and Failure of Quality Improvement Projects in Peer Production.pdf;/home/nathante/Zotero/storage/XXZ6US6B/Warncke-Wang et al_2015_The Success and Failure of Quality Improvement Projects in Peer Production.pdf}
|
||
}
|
||
|
||
@inproceedings{warncke-wang_tell_2013,
|
||
title = {Tell Me More: An Actionable Quality Model for {{Wikipedia}}},
|
||
shorttitle = {Tell Me More},
|
||
booktitle = {Proceedings of the 9th {{International Symposium}} on {{Open Collaboration}}},
|
||
author = {{Warncke-Wang}, Morten and Cosley, Dan and Riedl, John},
|
||
year = {2013},
|
||
month = aug,
|
||
series = {{{WikiSym}} '13},
|
||
pages = {1--10},
|
||
publisher = {{Association for Computing Machinery}},
|
||
address = {{New York, NY, USA}},
|
||
abstract = {In this paper we address the problem of developing actionable quality models for Wikipedia, models whose features directly suggest strategies for improving the quality of a given article. We first survey the literature in order to understand the notion of article quality in the context of Wikipedia and existing approaches to automatically assess article quality. We then develop classification models with varying combinations of more or less actionable features, and find that a model that only contains clearly actionable features delivers solid performance. Lastly we discuss the implications of these results in terms of how they can help improve the quality of articles across Wikipedia.},
|
||
isbn = {978-1-4503-1852-5},
|
||
keywords = {classification,flaw detection,information quality,machine learning,modelling,Wikipedia},
|
||
file = {/home/nathante/Zotero/storage/E3GPHFKF/Warncke-Wang et al_2013_Tell me more.pdf}
|
||
}
|
||
|
||
@inproceedings{west_drawing_2012,
|
||
title = {Drawing a Data-Driven Portrait of {{Wikipedia}} Editors},
|
||
booktitle = {Proceedings of the {{Eighth Annual International Symposium}} on {{Wikis}} and {{Open Collaboration}}},
|
||
author = {West, Robert and Weber, Ingmar and Castillo, Carlos},
|
||
year = {2012},
|
||
month = aug,
|
||
series = {{{WikiSym}} '12},
|
||
pages = {1--10},
|
||
publisher = {{Association for Computing Machinery}},
|
||
address = {{New York, NY, USA}},
|
||
abstract = {While there has been a substantial amount of research into the editorial and organizational processes within Wikipedia, little is known about how Wikipedia editors (Wikipedians) relate to the online world in general. We attempt to shed light on this issue by using aggregated log data from Yahoo!'s browser toolbar in order to analyze Wikipedians' editing behavior in the context of their online lives beyond Wikipedia. We broadly characterize editors by investigating how their online behavior differs from that of other users; e.g., we find that Wikipedia editors search more, read more news, play more games, and, perhaps surprisingly, are more immersed in popular culture. Then we inspect how editors' general interests relate to the articles to which they contribute; e.g., we confirm the intuition that editors are more familiar with their active domains than average users. Finally, we analyze the data from a temporal perspective; e.g., we demonstrate that a user's interest in the edited topic peaks immediately before the edit. Our results are relevant as they illuminate novel aspects of what has become many Web users' prevalent source of information.},
|
||
isbn = {978-1-4503-1605-7},
|
||
keywords = {editors,expertise,web usage,Wikipedia},
|
||
file = {/home/nathante/Zotero/storage/HS7786WY/West et al_2012_Drawing a data-driven portrait of Wikipedia editors.pdf}
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}
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@inproceedings{wilkinson_cooperation_2007-1,
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title = {Cooperation and Quality in {{Wikipedia}}},
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booktitle = {Proceedings of the 2007 {{International Symposium}} on {{Wikis}}},
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author = {Wilkinson, Dennis M. and Huberman, Bernardo A.},
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year = {2007},
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series = {{{WikiSym}} '07},
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pages = {157--164},
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publisher = {{ACM}},
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address = {{New York, NY}},
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abstract = {The rise of the Internet has enabled collaboration and cooperation on anunprecedentedly large scale. The online encyclopedia Wikipedia, which presently comprises 7.2 million articles created by 7.04 million distinct editors, provides a consummate example. We examined all 50 million edits made tothe 1.5 million English-language Wikipedia articles and found that the high-quality articles are distinguished by a marked increase in number of edits, number of editors, and intensity of cooperative behavior, as compared to other articles of similar visibility and age. This is significant because in other domains, fruitful cooperation has proven to be difficult to sustain as the size of the collaboration increases. Furthermore, in spite of the vagaries of human behavior, we show that Wikipedia articles accrete edits according to a simple stochastic mechanism in which edits beget edits. Topics of high interest or relevance are thus naturally brought to the forefront of quality.},
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isbn = {978-1-59593-861-9},
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keywords = {collaborative authoring,cooperation,groupware,Wikipedia},
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||
file = {/home/nathante/Zotero/storage/BA4AU4F9/Wilkinson and Huberman - 2007 - Cooperation and Quality in Wikipedia.pdf;/home/nathante/Zotero/storage/WSPRZK54/Wilkinson_and_Huberman-2007-Cooperation_and_quality_wikipedia.pdf}
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||
}
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@misc{yeates_re_2020,
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title = {Re: [{{Wiki-research-l}}] {{How}} to Quantifying "Effort" or "Time Spent" Put into Articles?},
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||
shorttitle = {Reply on {{Wiki-research-l}}},
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author = {Yeates, Stuart},
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||
year = {2020},
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||
month = oct
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||
}
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@article{zhang_crowd_2017,
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title = {Crowd {{Development}}: {{The Interplay}} between {{Crowd Evaluation}} and {{Collaborative Dynamics}} in {{Wikipedia}}},
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||
shorttitle = {Crowd {{Development}}},
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||
author = {Zhang, Ark Fangzhou and Livneh, Danielle and Budak, Ceren and Robert, Lionel P. and Romero, Daniel M.},
|
||
year = {2017},
|
||
month = dec,
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||
journal = {Proceedings of the ACM on Human-Computer Interaction},
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||
volume = {1},
|
||
number = {CSCW},
|
||
pages = {1--21},
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||
issn = {2573-0142},
|
||
langid = {english},
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||
file = {/home/nathante/Zotero/storage/3J2SN8YD/Zhang et al. - 2017 - Crowd Development The Interplay between Crowd Eva.pdf}
|
||
}
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||
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||
@inproceedings{zhang_history-based_2018,
|
||
title = {History-{{Based Article Quality Assessment}} on {{Wikipedia}}},
|
||
booktitle = {2018 {{IEEE International Conference}} on {{Big Data}} and {{Smart Computing}} ({{BigComp}})},
|
||
author = {Zhang, Shiyue and Hu, Zheng and Zhang, Chunhong and Yu, Ke},
|
||
year = {2018},
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||
month = jan,
|
||
pages = {1--8},
|
||
issn = {2375-9356},
|
||
abstract = {Wikipedia is widely considered as the biggest encyclopedia on Internet. Quality assessment of articles on Wikipedia has been studied for years. Conventional methods addressed this task by feature engineering and statistical machine learning algorithms. However, manually defined features are difficult to represent the long edit history of an article. Recently, researchers proposed an end-to-end neural model which used a Recurrent Neural Network(RNN) to learn the representation automatically. Although RNN showed its power in modeling edit history, the end-to-end method is time and resource consuming. In this paper, we propose a new history-based method to represent an article. We also take advantage of an RNN to handle the long edit history, but we do not abandon feature engineering. We still represent each revision of an article by manually defined features. This combination of deep neural model and feature engineering enables our model to be both simple and effective. Experiments demonstrate our model has better or comparable performance than previous works, and has the potential to work as a real-time service. Plus, we extend our model to do quality prediction.},
|
||
keywords = {Electronic publishing,Encyclopedias,Feature extraction,History,Information Quality,Internet,LSTM,Quality assessment,Wikipedia},
|
||
file = {/home/nathante/Zotero/storage/JVIN5RGA/Zhang et al_2018_History-Based Article Quality Assessment on Wikipedia.pdf;/home/nathante/Zotero/storage/XDSP7EI9/8367090.html}
|
||
}
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