https://wikipediaquality.com/api.php?action=feedcontributions&user=Ava&feedformat=atomWikipedia Quality - User contributions [en]2024-03-28T18:34:16ZUser contributionsMediaWiki 1.30.0https://wikipediaquality.com/index.php?title=Kann_Wikipedia_Unser_Fachwissen_Bereichern&diff=21160Kann Wikipedia Unser Fachwissen Bereichern2019-10-11T21:24:18Z<p>Ava: cats.</p>
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<div>{{Infobox work<br />
| title = Kann Wikipedia Unser Fachwissen Bereichern<br />
| date = 2015<br />
| authors = [[U. Rechenberg]]<br />[[C. Josten]]<br />[[S. Klima]]<br />
| doi = 10.1055/s-0034-1396207<br />
| link = https://www.thieme-connect.com/products/ejournals/abstract/10.1055/s-0034-1396207<br />
}}<br />
'''Kann Wikipedia Unser Fachwissen Bereichern''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[U. Rechenberg]], [[C. Josten]] and [[S. Klima]].<br />
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== Overview ==<br />
Hintergrund: Die neuen Medien stellen sowohl Ausbilder als auch Auszubildende im Medizinstudium und klinischen Alltag vor immer neue Herausforderungen. Dabei spielt vor allem [[Wikipedia]] eine zunehmend bedeutende Rolle fur die Informationsgewinnung. Neben vielen Vorteilen besteht fur Wikipedia weiterhin der Nachteil der fehlenden Kontrollierbarkeit auf Richtigkeit der bereitgestellten Informationen. Ziel dieser Arbeit ist es, die Relevanz orthopadischer und unfallchirurgischer Wikipedia-Artikel im klinischen Alltag zu untersuchen. Material und Methoden: Im September 2013 wurde eine Studiengruppe bestehend aus Studenten im praktischen Jahr, Assistenzarzten und einem Facharzt und Hochschullehrer anhand von 2 Fragebogen zu den medizinischen Themen auf Wikipedia befragt. Dabei wurden klinisch haufige Themen zu Krankheiten/Symptomen, Untersuchungstechniken/Klassifikationen und konservativer/operativer Therapie aus dem Gebiet der Orthopadie und Unfallchirurgie nach objektiven Kriterien bewertet. Insgesamt wurden 211 Wikipedia-Artikel zu medizinischen Themen untersucht. Abschliesend erfolgte eine subjektive Einschatzung der Inhalte auf Wikipedia durch jeden einzelnen Studienteilnehmer. Ergebnisse: 134 von 211 medizinischen Wikipedia-Seiten aus der Orthopadie und Unfallchirurgie erschienen als eigenstandige Artikel. Die Studie zeigte eine hohe Aktualitat und hervorragende Positionierung der Wikipedia-Beitrage in der [[Google]]-Suchliste. Durch zahlreiche Verknupfungen, viele Literaturverweise (z. B.: AWMF-Leitlinien, Zeitschriften), hochwertiges Bildmaterial und zuweilen Videos stehen die Fachbeitrage i. d. R. denen von Printmedien nicht nach. Fast die Halfte (42,5 %) der Beitrage beurteilten die Studienteilnehmer bei Wikipedia als geeignet zur Vorbereitung auf das Staatsexamen und den klinischen Alltag von Berufsanfangern. Schlussfolgerung: Besonders die jungen Mediziner, die sogenannte Web-2.0-Generation, nutzen verstarkt die Angebote des Internets zum Wissenserwerb, wodurch die Lernmethoden verandert werden. Wikipedia stellt die geeignete Plattform dar, sowohl wahrend des Studiums als auch im Rahmen der Facharztweiterbildung, um Inhalte aus unserem Fachgebiet vielen Lesern frei zur Verfugung zu stellen. Fur die Inhalte und die Qualitat der Beitrage in unserem Fachgebiet ist unser aller Engagement gefragt.<br />
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Rechenberg, U.; Josten, C.; Klima, S.. (2015). "[[Kann Wikipedia Unser Fachwissen Bereichern]]". Georg Thieme Verlag KG. DOI: 10.1055/s-0034-1396207. <br />
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{{cite journal |last1=Rechenberg |first1=U. |last2=Josten |first2=C. |last3=Klima |first3=S. |title=Kann Wikipedia Unser Fachwissen Bereichern |date=2015 |doi=10.1055/s-0034-1396207 |url=https://wikipediaquality.com/wiki/Kann_Wikipedia_Unser_Fachwissen_Bereichern |journal=Georg Thieme Verlag KG}}<br />
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Rechenberg, U.; Josten, C.; Klima, S.. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Kann_Wikipedia_Unser_Fachwissen_Bereichern">Kann Wikipedia Unser Fachwissen Bereichern</a>&amp;quot;. Georg Thieme Verlag KG. DOI: 10.1055/s-0034-1396207. <br />
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[[Category:Scientific works]]</div>Avahttps://wikipediaquality.com/index.php?title=Page_Protection:_Another_Missing_Dimension_of_Wikipedia_Research&diff=21159Page Protection: Another Missing Dimension of Wikipedia Research2019-10-11T21:21:38Z<p>Ava: Adding embed</p>
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<div>{{Infobox work<br />
| title = Page Protection: Another Missing Dimension of Wikipedia Research<br />
| date = 2015<br />
| authors = [[Benjamin Mako Hill]]<br />[[Aaron D. Shaw]]<br />
| doi = 10.1145/2788993.2789846<br />
| link = http://dl.acm.org/citation.cfm?doid=2788993.2789846<br />
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'''Page Protection: Another Missing Dimension of Wikipedia Research''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Benjamin Mako Hill]] and [[Aaron D. Shaw]].<br />
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== Overview ==<br />
Page protection is a feature of wiki software that allows administrators to restrict contributions to particular pages. For example, pages are frequently protected so that they can only be edited by administrators. Page protection affects tens of thousands of pages in [[English Wikipedia]] and renders many of [[Wikipedia]]'s most visible pages uneditable by the vast majority of visitors. That said, page protection has attracted very little attention and is rarely taken into account by researchers. This note describes page protection and illustrates why it plays an important role in shaping user behavior on wikis. Authors also present a new longitudinal dataset of page protection events for English Wikipedia, the software used to produce it, and results from tests that support both the validity of the dataset and the impact of page protection on patterns of editing.<br />
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Hill, Benjamin Mako; Shaw, Aaron D.. (2015). "[[Page Protection: Another Missing Dimension of Wikipedia Research]]".DOI: 10.1145/2788993.2789846. <br />
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{{cite journal |last1=Hill |first1=Benjamin Mako |last2=Shaw |first2=Aaron D. |title=Page Protection: Another Missing Dimension of Wikipedia Research |date=2015 |doi=10.1145/2788993.2789846 |url=https://wikipediaquality.com/wiki/Page_Protection:_Another_Missing_Dimension_of_Wikipedia_Research}}<br />
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Hill, Benjamin Mako; Shaw, Aaron D.. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Page_Protection:_Another_Missing_Dimension_of_Wikipedia_Research">Page Protection: Another Missing Dimension of Wikipedia Research</a>&amp;quot;.DOI: 10.1145/2788993.2789846. <br />
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</code></div>Avahttps://wikipediaquality.com/index.php?title=Summaries_of_Wikipedia_Deletion_Discussions:_a_Shallow_Semantic_Approach&diff=21158Summaries of Wikipedia Deletion Discussions: a Shallow Semantic Approach2019-10-11T21:20:26Z<p>Ava: Categories</p>
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<div>{{Infobox work<br />
| title = Summaries of Wikipedia Deletion Discussions: a Shallow Semantic Approach<br />
| date = 2006<br />
| authors = [[Scott Robert Williams]]<br />
| link = http://www.cs.colorado.edu/department/publications/theses/docs/bs/scott_williams.pdf<br />
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'''Summaries of Wikipedia Deletion Discussions: a Shallow Semantic Approach''' - scientific work related to [[Wikipedia quality]] published in 2006, written by [[Scott Robert Williams]].<br />
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== Overview ==<br />
The thesis proposes a method to produce keyword summaries of pro/con debates highlighting the issues the two opposing sides raise in discussing the question.<br />
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Williams, Scott Robert. (2006). "[[Summaries of Wikipedia Deletion Discussions: a Shallow Semantic Approach]]".<br />
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{{cite journal |last1=Williams |first1=Scott Robert |title=Summaries of Wikipedia Deletion Discussions: a Shallow Semantic Approach |date=2006 |url=https://wikipediaquality.com/wiki/Summaries_of_Wikipedia_Deletion_Discussions:_a_Shallow_Semantic_Approach}}<br />
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Williams, Scott Robert. (2006). &amp;quot;<a href="https://wikipediaquality.com/wiki/Summaries_of_Wikipedia_Deletion_Discussions:_a_Shallow_Semantic_Approach">Summaries of Wikipedia Deletion Discussions: a Shallow Semantic Approach</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Avahttps://wikipediaquality.com/index.php?title=Multi-Cultural_Wikipedia_Mining_of_Geopolitics_Interactions_Leveraging_Reduced_Google_Matrix_Analysis&diff=21157Multi-Cultural Wikipedia Mining of Geopolitics Interactions Leveraging Reduced Google Matrix Analysis2019-10-11T21:17:39Z<p>Ava: + Infobox work</p>
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<div>{{Infobox work<br />
| title = Multi-Cultural Wikipedia Mining of Geopolitics Interactions Leveraging Reduced Google Matrix Analysis<br />
| date = 2017<br />
| authors = [[Klaus M. Frahm]]<br />[[Samer El Zant]]<br />[[Katia Jaffrès-Runser]]<br />[[Dima L. Shepelyansky]]<br />
| doi = 10.1016/j.physleta.2017.06.021<br />
| link = http://www.sciencedirect.com/science/article/pii/S0375960116321879<br />
| plink = http://arxiv.org/pdf/1612.07920.pdf<br />
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'''Multi-Cultural Wikipedia Mining of Geopolitics Interactions Leveraging Reduced Google Matrix Analysis''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Klaus M. Frahm]], [[Samer El Zant]], [[Katia Jaffrès-Runser]] and [[Dima L. Shepelyansky]].<br />
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== Overview ==<br />
Geopolitics focuses on political power in relation to geographic space. Interactions among world countries have been widely studied at various scales, observing economic exchanges, world history or international politics among others. This work exhibits the potential of [[Wikipedia]] mining for such studies. Indeed, Wikipedia stores valuable fine-grained dependencies among countries by linking webpages together for diverse types of interactions (not only related to economical, political or historical facts). Authors mine herein the Wikipedia networks of several language editions using the recently proposed method of reduced [[Google]] matrix analysis. This approach allows to establish direct and hidden links between a subset of nodes that belong to a much larger directed network. Authors study concentrates on 40 major countries chosen worldwide. Authors aim is to offer a multicultural perspective on their interactions by comparing networks extracted from five different Wikipedia language editions, emphasizing English, Russian and Arabic ones. Authors demonstrate that this approach allows to recover meaningful direct and hidden links among the 40 countries of interest.</div>Avahttps://wikipediaquality.com/index.php?title=Who_Will_Stop_Contributing%3F:_Predicting_Inactive_Editors_in_Wikipedia&diff=21156Who Will Stop Contributing?: Predicting Inactive Editors in Wikipedia2019-10-11T21:15:08Z<p>Ava: cats.</p>
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<div>{{Infobox work<br />
| title = Who Will Stop Contributing?: Predicting Inactive Editors in Wikipedia<br />
| date = 2017<br />
| authors = [[Harish Arelli]]<br />[[Francesca Spezzano]]<br />
| doi = 10.1145/3110025.3110026<br />
| link = https://scholarworks.boisestate.edu/cs_facpubs/136/<br />
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'''Who Will Stop Contributing?: Predicting Inactive Editors in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Harish Arelli]] and [[Francesca Spezzano]].<br />
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== Overview ==<br />
In this paper, authors focus on [[English Wikipedia]], one of the main user-contributed content systems, and study the problem of predicting which users will become inactive and stop contributing to the encyclopedia. Authors propose a predictive model leveraging frequent patterns appearing in user's editing behavior as [[features]] to predict active vs. inactive [[Wikipedia]] users. Authors experiments show that method can effectively predict inactive users with an AUROC of 0.97 and significantly beats competitors in the task of early prediction of inactive users.<br />
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Arelli, Harish; Spezzano, Francesca. (2017). "[[Who Will Stop Contributing?: Predicting Inactive Editors in Wikipedia]]".DOI: 10.1145/3110025.3110026. <br />
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{{cite journal |last1=Arelli |first1=Harish |last2=Spezzano |first2=Francesca |title=Who Will Stop Contributing?: Predicting Inactive Editors in Wikipedia |date=2017 |doi=10.1145/3110025.3110026 |url=https://wikipediaquality.com/wiki/Who_Will_Stop_Contributing?:_Predicting_Inactive_Editors_in_Wikipedia}}<br />
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Arelli, Harish; Spezzano, Francesca. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Who_Will_Stop_Contributing?:_Predicting_Inactive_Editors_in_Wikipedia">Who Will Stop Contributing?: Predicting Inactive Editors in Wikipedia</a>&amp;quot;.DOI: 10.1145/3110025.3110026. <br />
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[[Category:English Wikipedia]]</div>Avahttps://wikipediaquality.com/index.php?title=Casting_a_Web_of_Trust_over_Wikipedia:_an_Interaction-Based_Approach&diff=21155Casting a Web of Trust over Wikipedia: an Interaction-Based Approach2019-10-11T21:12:50Z<p>Ava: + Infobox work</p>
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<div>{{Infobox work<br />
| title = Casting a Web of Trust over Wikipedia: an Interaction-Based Approach<br />
| date = 2011<br />
| authors = [[Silviu Maniu]]<br />[[Talel Abdessalem]]<br />[[Bogdan Cautis]]<br />
| doi = 10.1145/1963192.1963237<br />
| link = http://dl.acm.org/citation.cfm?doid=1963192.1963237<br />
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'''Casting a Web of Trust over Wikipedia: an Interaction-Based Approach''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Silviu Maniu]], [[Talel Abdessalem]] and [[Bogdan Cautis]].<br />
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== Overview ==<br />
Authors report in this short paper results on inferring a signed network (a "web of trust") from user interactions. On the [[Wikipedia]] network of contributors, from a collection of articles in the politics domain and their revision history, authors investigate mechanisms by which relationships between contributors - in the form of signed directed links - can be inferred from their interactions. Authors preliminary study provides valuable insight into principles underlying a signed network of Wikipedia contributors that is captured by social interaction. Authors look into whether this network (called hereafter WikiSigned) represents indeed a plausible configuration of link signs. Authors assess connections to social theories such as structural balance and status, which have already been considered in online communities. Authors also evaluate on this network the accuracy of a learned predictor for edge signs. Equipped with learning techniques that have been applied before on three explicit signed networks, authors obtain good accuracy over the WikiSigned edges. Moreover, by cross training-testing authors obtain strong evidence that network does reveal an implicit signed configuration and that it has similar characteristics to the explicit ones, even though it is inferred from interactions. Authors also report on an application of the resulting signed network that impacts Wikipedia readers, namely the classification of Wikipedia articles by importance and quality.</div>Avahttps://wikipediaquality.com/index.php?title=Videoclef_2008:_Asr_Classification_based_on_Wikipedia_Categories&diff=21154Videoclef 2008: Asr Classification based on Wikipedia Categories2019-10-11T21:11:41Z<p>Ava: Wikilinks</p>
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<div>'''Videoclef 2008: Asr Classification based on Wikipedia Categories''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Jens Kürsten]], [[Daniel Richter]] and [[Maximilian Eibl]].<br />
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== Overview ==<br />
This article describes participation at the VideoCLEF track of the CLEF campaign 2008. Authors designed and implemented a prototype for the classification of the Video ASR data. Authors approach was to regard the task as text classification problem. Authors used terms from [[Wikipedia categories]] as training data for text classifiers. For the text classification the Naive-Bayes and kNN classifier from the WEKA toolkit were used. Authors submitted experiments for classification task 1 and 2. For the translation of the feeds to English (translation task) [[Google]]’s AJAX language API was used. The evaluation of the classification task showed bad results for experiments with a precision between 10 and 15 percent. These values did not meet expectations. Interestingly, authors could not improve the quality of the classification by using the provided metadata. But at least the created translation of the RSS Feeds was well.</div>Avahttps://wikipediaquality.com/index.php?title=Review:_the_Wikipedia_Revolution_by_Andrew_Lih&diff=21153Review: the Wikipedia Revolution by Andrew Lih2019-10-11T21:09:34Z<p>Ava: Starting a page: Review: the Wikipedia Revolution by Andrew Lih</p>
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<div>'''Review: the Wikipedia Revolution by Andrew Lih''' - scientific work related to Wikipedia quality published in 2009, written by Tom Simonite.<br />
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== Overview ==<br />
The rise and rise of Wikipedia, from the technology that helped to create it to the community that made it one of the world's 10 most popular websites</div>Avahttps://wikipediaquality.com/index.php?title=Disambiguating_Words_Senses_with_the_Aid_of_Wikipedia&diff=21152Disambiguating Words Senses with the Aid of Wikipedia2019-10-11T21:07:21Z<p>Ava: + wikilinks</p>
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<div>'''Disambiguating Words Senses with the Aid of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Abdullah Bawakid]], [[Mourad Oussalah]], [[Naveed Afzal]], [[Seong-O Shim]] and [[Syed Ahsan]].<br />
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== Overview ==<br />
In this paper, a novel framework for extracting and using [[features]] from [[Wikipedia]] for the task of Word Sense Disambiguation is presented. Authors highlight how the features are extracted, re-organized and applied for building what authors call term-concepts table. Authors utilize the internal structure within Wikipedia such as its [[categories]] structure and inter links while building the proposed framework. Authors describe an evaluation authors ran on the built framework to test its effectiveness in the application of Disambiguating Word Senses. Authors also report the obtained results and compare them with those of other competing systems. (Bawakid A, Oussalah M, Afzal N, Shim S, Ahsan S. Disambiguating Word Senses with the Aid of Wikipedia.</div>Avahttps://wikipediaquality.com/index.php?title=Managing_Diversity_and_Redundancy_in_Summaries_with_the_Aid_of_Wikipedia_Categories&diff=21151Managing Diversity and Redundancy in Summaries with the Aid of Wikipedia Categories2019-10-11T21:05:18Z<p>Ava: Adding wikilinks</p>
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<div>'''Managing Diversity and Redundancy in Summaries with the Aid of Wikipedia Categories''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Abdullah Bawakid]].<br />
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== Overview ==<br />
This paper presents a novel multi-document summarization system that relies on specific [[features]] extracted from [[Wikipedia]]. The system utilizes mainly two aspects from Wikipedia, its articles titles and [[categories]] network. It does not employ the inner content of the articles in Wikipedia, nor inter or inner links. The implemented summarizer includes a module that focuses on managing diversity and redundancy when generating summaries. Authors describe in this paper how this module is applied on the subtopic clusters which are also generated during the summarization process. The evaluation authors performed on the system illustrate its competitiveness when compared against others in the literature.</div>Avahttps://wikipediaquality.com/index.php?title=Knowledge-Sharing_Intention_in_a_Virtual_Community:_a_Study_of_Participants_in_the_Chinese_Wikipedia&diff=21150Knowledge-Sharing Intention in a Virtual Community: a Study of Participants in the Chinese Wikipedia2019-10-11T21:02:38Z<p>Ava: Adding wikilinks</p>
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<div>'''Knowledge-Sharing Intention in a Virtual Community: a Study of Participants in the Chinese Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Shun-Chuan Ho]], [[Ping-Ho Ting]], [[Dong-Yih Bau]] and [[Chun-Chung Wei]].<br />
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== Overview ==<br />
Abstract This study proposes a model for evaluating virtual community members' knowledge-sharing intention toward [[Chinese Wikipedia]]. The results of this study reveal that knowledge-sharing intention is influenced directly by attitude, subjective norms, and perceived behavioral control, whereas anticipated reciprocal relationships and enjoying helping are positively related to attitude; sense of self-worth and peer influences are positively related to subjective norms; and self-efficacy and resource-facilitating conditions are positively related to perceived behavioral control on knowledge sharing.</div>Avahttps://wikipediaquality.com/index.php?title=Effects_of_Contributor_Experience_on_the_Quality_of_Health-Related_Wikipedia_Articles&diff=21149Effects of Contributor Experience on the Quality of Health-Related Wikipedia Articles2019-10-11T21:00:04Z<p>Ava: Infobox</p>
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<div>{{Infobox work<br />
| title = Effects of Contributor Experience on the Quality of Health-Related Wikipedia Articles<br />
| date = 2018<br />
| authors = [[Peter Holtz]]<br />[[Besnik Fetahu]]<br />[[Joachim Kimmerle]]<br />
| doi = 10.2196/jmir.9683<br />
| link = https://www.jmir.org/2018/5/e171/<br />
}}<br />
'''Effects of Contributor Experience on the Quality of Health-Related Wikipedia Articles''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Peter Holtz]], [[Besnik Fetahu]] and [[Joachim Kimmerle]].<br />
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== Overview ==<br />
Background: Consulting the Internet for health-related information is a common and widespread phenomenon, and [[Wikipedia]] is arguably one of the most important resources for health-related information. Therefore, it is relevant to identify factors that have an impact on the quality of health-related Wikipedia articles. Objective: In study authors have hypothesized a positive effect of contributor experience on the quality of health-related Wikipedia articles. Methods: Authors mined the edit history of all (as of February 2017) 18,805 articles that were listed in the [[categories]] on the portal health & fitness in the English language version of Wikipedia. Authors identified tags within the articles’ edit histories, which indicated potential issues with regard to the respective article’s quality or neutrality. Of all of the sampled articles, 99 (99/18,805, 0.53%) articles had at some point received at least one such tag. In analysis authors only considered those articles with a minimum of 10 edits (10,265 articles in total; 96 tagged articles, 0.94%). Additionally, to test hypothesis, authors constructed contributor profiles, where a profile consisted of all the articles edited by a contributor and the corresponding number of edits contributed. Authors did not differentiate between rollbacks and edits with novel content. Results: Nonparametric Mann-Whitney U-tests indicated a higher number of previously edited articles for editors of the nontagged articles (mean rank tagged 2348.23, mean rank nontagged 5159.29; U=9.25, P<.001). However, authors did not find a significant difference for the contributors’ total number of edits (mean rank tagged 4872.85, mean rank nontagged 5135.48; U=0.87, P=.39). Using logistic regression analysis with the respective article’s number of edits and number of editors as covariates, only the number of edited articles yielded a significant effect on the article’s status as tagged versus nontagged (dummy-coded; Nagelkerke R2 for the full model=.17; B [SE B]=-0.001 [0.00]; Wald c2 [1]=19.70; P<.001), whereas authors again found no significant effect for the mere number of edits (Nagelkerke R2 for the full model=.15; B [SE B]=0.000 [0.01]; Wald c2 [1]=0.01; P=.94). Conclusions: Authors findings indicate an effect of contributor experience on the quality of health-related Wikipedia articles. However, only the number of previously edited articles was a predictor of the articles’ quality but not the mere volume of edits. More research is needed to disentangle the different aspects of contributor experience. Authors have discussed the implications of findings with respect to ensuring the quality of health-related information in collaborative knowledge-building platforms. [J Med Internet Res 2018;20(5):e171]</div>Avahttps://wikipediaquality.com/index.php?title=Planning_a_Multi-Institution_Wikipedia_Edit-A-Thon_for_Agriculture:_Fulfilling_the_Land_Grant_Mission_While_Contributing_to_the_World%E2%80%99s_Understanding_of_Agriculture&diff=21148Planning a Multi-Institution Wikipedia Edit-A-Thon for Agriculture: Fulfilling the Land Grant Mission While Contributing to the World’s Understanding of Agriculture2019-10-11T20:57:45Z<p>Ava: Starting a page: Planning a Multi-Institution Wikipedia Edit-A-Thon for Agriculture: Fulfilling the Land Grant Mission While Contributing to the World’s Understanding of Agriculture</p>
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<div>'''Planning a Multi-Institution Wikipedia Edit-A-Thon for Agriculture: Fulfilling the Land Grant Mission While Contributing to the World’s Understanding of Agriculture''' - scientific work related to Wikipedia quality published in 2016, written by Ashley L. Downs, Sarah E. Kennedy, Jeanne Pfander, Kelly Doyle and Julie Kelly.<br />
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== Overview ==<br />
Many people, including farmers and ranchers, turn to Google when exploring a given topic. Due to the search engine’s ranking algorithms, Wikipedia articles are often at the top of the results list. Wanting to enhance the quality as well as the quantity of Wikipedia articles for agriculture topics, librarians at several U.S. land-grant universities recently collaborated to plan two different synchronous multi-institution virtual Wikipedia edit-a-thons. The openness of Wikipedia, including its free, multilingual content, its use of version control, and its low barrier to entry for novice editors, makes it the perfect venue for such an effort. In this paper, authors will discuss the objectives of the edit-a-thons which include: to facilitate inter-institutional as well as intra-departmental collaboration between campus units and librarians; to enhance the media literacy and research skills of participants; and, most importantly, to increase access to quality agricultural knowledge for the public good. Challenges and triumphs of the event will be discussed, as will future directions and goals.</div>Avahttps://wikipediaquality.com/index.php?title=Historical_Queries_on_Wikipedia:_a_Usability-Driven_Approach&diff=21147Historical Queries on Wikipedia: a Usability-Driven Approach2019-10-11T20:56:16Z<p>Ava: infobox</p>
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<div>{{Infobox work<br />
| title = Historical Queries on Wikipedia: a Usability-Driven Approach<br />
| date = 2015<br />
| authors = [[Carlo Zaniolo]]<br />
| doi = 10.1109/TIME.2015.28<br />
| link = https://dl.acm.org/citation.cfm?id=2924982<br />
}}<br />
'''Historical Queries on Wikipedia: a Usability-Driven Approach''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Carlo Zaniolo]].<br />
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== Overview ==<br />
Over time, contents of [[Wikipedia]] pages Infoboxes and knowledge bases evolve to reflect changes in the real world, and the history of that evolution is of great interest. Now, users need query languages and interfaces that access to such historical knowledge bases with ease via systems that assure fast response. Authors will discuss these challenging requirement starting from the query languages and interfaces that assure usability for non-temporal queries, and then analyzing temporal extensions proposed in the past.</div>Avahttps://wikipediaquality.com/index.php?title=Using_Ontologies_to_Model_Human_Navigation_Behavior_in_Information_Networks:_a_Study_based_on_Wikipedia&diff=21146Using Ontologies to Model Human Navigation Behavior in Information Networks: a Study based on Wikipedia2019-10-11T20:54:49Z<p>Ava: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Using Ontologies to Model Human Navigation Behavior in Information Networks: a Study based on Wikipedia<br />
| date = 2015<br />
| authors = [[Daniel Lamprecht]]<br />[[Markus Strohmaier]]<br />[[Denis Helic]]<br />[[Csongor Nyulas]]<br />[[Tania Tudorache]]<br />[[Natalya Fridman Noy]]<br />[[Mark A. Musen]]<br />
| doi = 10.3233/SW-140143<br />
| link = https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4643321/<br />
}}<br />
'''Using Ontologies to Model Human Navigation Behavior in Information Networks: a Study based on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Daniel Lamprecht]], [[Markus Strohmaier]], [[Denis Helic]], [[Csongor Nyulas]], [[Tania Tudorache]], [[Natalya Fridman Noy]] and [[Mark A. Musen]].<br />
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== Overview ==<br />
The need to examine the behavior of different user groups is a fundamental requirement when building information systems. In this paper, authors present Ontology-based Decentralized Search (OBDS), a novel method to model the navigation behavior of users equipped with different types of background knowledge. Ontology-based Decentralized Search combines decentralized search, an established method for navigation in [[social network]]s, and ontologies to model navigation behavior in information networks. The method uses ontologies as an explicit representation of background knowledge to inform the navigation process and guide it towards navigation targets. By using different ontologies, users equipped with different types of background knowledge can be represented. Authors demonstrate method using four biomedical ontologies and their associated [[Wikipedia]] articles. Authors compare simulation results with base line approaches and with results obtained from a user study. Authors find that method produces click paths that have properties similar to those originating from human navigators. The results suggest that method can be used to model human navigation behavior in systems that are based on information networks, such as Wikipedia. This paper makes the following contributions: (i) To the best of knowledge, this is the first work to demonstrate the utility of ontologies in modeling human navigation and (ii) it yields new insights and understanding about the mechanisms of human navigation in information networks.<br />
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== Embed ==<br />
=== Wikipedia Quality ===<br />
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Lamprecht, Daniel; Strohmaier, Markus; Helic, Denis; Nyulas, Csongor; Tudorache, Tania; Noy, Natalya Fridman; Musen, Mark A.. (2015). "[[Using Ontologies to Model Human Navigation Behavior in Information Networks: a Study based on Wikipedia]]". NIH Public Access. DOI: 10.3233/SW-140143. <br />
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=== English Wikipedia ===<br />
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{{cite journal |last1=Lamprecht |first1=Daniel |last2=Strohmaier |first2=Markus |last3=Helic |first3=Denis |last4=Nyulas |first4=Csongor |last5=Tudorache |first5=Tania |last6=Noy |first6=Natalya Fridman |last7=Musen |first7=Mark A. |title=Using Ontologies to Model Human Navigation Behavior in Information Networks: a Study based on Wikipedia |date=2015 |doi=10.3233/SW-140143 |url=https://wikipediaquality.com/wiki/Using_Ontologies_to_Model_Human_Navigation_Behavior_in_Information_Networks:_a_Study_based_on_Wikipedia |journal=NIH Public Access}}<br />
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=== HTML ===<br />
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Lamprecht, Daniel; Strohmaier, Markus; Helic, Denis; Nyulas, Csongor; Tudorache, Tania; Noy, Natalya Fridman; Musen, Mark A.. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Using_Ontologies_to_Model_Human_Navigation_Behavior_in_Information_Networks:_a_Study_based_on_Wikipedia">Using Ontologies to Model Human Navigation Behavior in Information Networks: a Study based on Wikipedia</a>&amp;quot;. NIH Public Access. DOI: 10.3233/SW-140143. <br />
</nowiki><br />
</code></div>Avahttps://wikipediaquality.com/index.php?title=Research_Guides:_Wikipedia:_Wikipedia_Controversies&diff=16644Research Guides: Wikipedia: Wikipedia Controversies2019-05-31T21:16:17Z<p>Ava: Starting a page: Research Guides: Wikipedia: Wikipedia Controversies</p>
<hr />
<div>'''Research Guides: Wikipedia: Wikipedia Controversies''' - scientific work related to Wikipedia quality published in 2015, written by Hilary Smith.<br />
<br />
== Overview ==<br />
A guide to help you get started with Wikipedia. Editing wars, hoaxes and other Wikipedia hi-jinx.</div>Avahttps://wikipediaquality.com/index.php?title=What_Makes_a_Link_Successful_on_Wikipedia&diff=16643What Makes a Link Successful on Wikipedia2019-05-31T21:14:53Z<p>Ava: Starting a page: What Makes a Link Successful on Wikipedia</p>
<hr />
<div>'''What Makes a Link Successful on Wikipedia''' - scientific work related to Wikipedia quality published in 2017, written by Dimitar Dimitrov, Philipp Singer, Florian Lemmerich and Markus Strohmaier.<br />
<br />
== Overview ==<br />
While a plethora of hypertext links exist on the Web, only a small amount of them are regularly clicked. Starting from this observation, authors set out to study large-scale click data from Wikipedia in order to understand what makes a link successful. Authors systematically analyze effects of link properties on the popularity of links. By utilizing mixed-effects hurdle models supplemented with descriptive insights, authors find evidence of user preference towards links leading to the periphery of the network, towards links leading to semantically similar articles, and towards links in the top and left-side of the screen. Authors integrate these findings as Bayesian priors into a navigational Markov chain model and by doing so successfully improve the model fits. Authors further adapt and improve the well-known classic PageRank algorithm that assumes random navigation by accounting for observed navigational preferences of users in a weighted variation. This work facilitates understanding navigational click behavior and thus can contribute to improving link structures and algorithms utilizing these structures.</div>Avahttps://wikipediaquality.com/index.php?title=Mathematical_Analysis_of_Subjectively_Dened_Coincidences;_a_Case_Study_Using_Wikipedia&diff=16642Mathematical Analysis of Subjectively Dened Coincidences; a Case Study Using Wikipedia2019-05-31T21:12:03Z<p>Ava: Starting a page: Mathematical Analysis of Subjectively Dened Coincidences; a Case Study Using Wikipedia</p>
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<div>'''Mathematical Analysis of Subjectively Dened Coincidences; a Case Study Using Wikipedia''' - scientific work related to Wikipedia quality published in 2008, written by David Aldous.<br />
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== Overview ==<br />
Rationalists assert that real-life coincidences occur no more frequently than is predictable by chance, but (outside stylized settings such as birthdays) empirical evidence is scant. Authors describe a study, with a few real-life features, of coincidences noticed in reading random articles in Wikipedia. Part of a rationalist program (that one can use specic observed coincidences to infer general types of unobserved coincidence and estimate probabilities of coincidences therein) can be examined in this context, and ts data well enough. Though this conclusion may be unremarkable, the study may provide guidance for the design of more</div>Avahttps://wikipediaquality.com/index.php?title=Information_Technology_and_Quantitative_Management_,_Itqm_2014_a_Method_for_Refining_a_Taxonomy_by_Using_Annotated_Suffix_Trees_and_Wikipedia_Resources&diff=16641Information Technology and Quantitative Management , Itqm 2014 a Method for Refining a Taxonomy by Using Annotated Suffix Trees and Wikipedia Resources2019-05-31T21:09:51Z<p>Ava: Adding wikilinks</p>
<hr />
<div>'''Information Technology and Quantitative Management , Itqm 2014 a Method for Refining a Taxonomy by Using Annotated Suffix Trees and Wikipedia Resources''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Ekaterina Chernyak]] and [[Boris Mirkin]].<br />
<br />
== Overview ==<br />
A two-step approach to taxonomy construction is presented. On the first step the frame of taxonomy is built manually according to some representative educational materials. On the second step, the frame is refined using the [[Wikipedia]] category tree and articles. Since the structure of Wikipedia is rather noisy, a procedure to clear the Wikipedia category tree is suggested. A string-to-text relevance score, based on annotated suffix trees, is used several times to 1) clear the Wikipedia data from noise; 2) to assign W ikipedia [[categories]] to taxonomy topics; 3) to choose whether the category should be assigned to the taxonomy topic or stay on intermediate levels. The resulting taxonomy consists of three parts: the manully set upper levels, the adopted Wikipedia category tree and the Wikipedia articles as leaves.Also, a set of so-called descriptors is assigned to every leaf; these are phrases explaining aspects of the leaf topic. The method is illustrated by its application to two domains: a) Probability theory and mathematical statistics, b) ”Numerical analysis” (both in Russian). c</div>Avahttps://wikipediaquality.com/index.php?title=%E2%80%9CCritique_of_Gy%E2%80%99s_Sampling_Theory%E2%80%9D:_Misplaced_Expectations_of_Wikipedia%E2%80%99s_Democratic_Intentions&diff=16640“Critique of Gy’s Sampling Theory”: Misplaced Expectations of Wikipedia’s Democratic Intentions2019-05-31T21:07:42Z<p>Ava: Starting a page: “Critique of Gy’s Sampling Theory”: Misplaced Expectations of Wikipedia’s Democratic Intentions</p>
<hr />
<div>'''“Critique of Gy’s Sampling Theory”: Misplaced Expectations of Wikipedia’s Democratic Intentions''' - scientific work related to Wikipedia quality published in 2013, written by Kim H. Esbenson and Geoffrey J. Lyman.<br />
<br />
== Overview ==<br />
Author Summary: The authors take issue with and respond to the Wikipedia entry for Gy’s Sampling Theory.</div>Avahttps://wikipediaquality.com/index.php?title=An_Ontology-Based_Analysis_of_Wikipedia_Usage_Data_for_Measuring_Degree-Of-Interest_in_Country&diff=16639An Ontology-Based Analysis of Wikipedia Usage Data for Measuring Degree-Of-Interest in Country2019-05-31T21:06:01Z<p>Ava: Starting a page: An Ontology-Based Analysis of Wikipedia Usage Data for Measuring Degree-Of-Interest in Country</p>
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<div>'''An Ontology-Based Analysis of Wikipedia Usage Data for Measuring Degree-Of-Interest in Country''' - scientific work related to Wikipedia quality published in 2014, written by Hyon Hee Kim, Jinnam Jo and Donggeon Kim.<br />
<br />
== Overview ==<br />
In this paper, authors propose an ontology-based approach to measuring degree-of-interest in country by analyzing wikipedia usage data. First, authors developed the degree-of-interest ontology called DOI ontology by extracting concept hierarchies from wikipedia categories. Second, authors map the title of frequently edited articles into DOI ontology, and authors measure degree-of-interest based on DOI ontology by analyzing wikipedia page views. Finally, authors perform chi-square test of independence to figure out if interesting fields are independent or not by country. This approach shows interesting fields are closely related to each country, and provides degree of interests by ∙제1저자 : 김현희 ∙교신저자 : 김현희 ∙투고일 : 2014. 3. 7, 심사일 : 2014. 3. 20, 게재확정일 : 2014. 3. 27. * 동덕여자대학교 정보통계학과(Dept. of Information and Statistics, Dongduk Women’s University) 44 Journal of The Korea Society of Computer and Information April 2014 country timely and flexibly as compared with conventional questionnaire survey analysis. ▸</div>Avahttps://wikipediaquality.com/index.php?title=Using_Wikipedia_Categories_for_Discovering_the_Themes_of_Text_Documents&diff=16638Using Wikipedia Categories for Discovering the Themes of Text Documents2019-05-31T21:04:30Z<p>Ava: Starting a page: Using Wikipedia Categories for Discovering the Themes of Text Documents</p>
<hr />
<div>'''Using Wikipedia Categories for Discovering the Themes of Text Documents''' - scientific work related to Wikipedia quality published in 2015, written by Abdullah Bawakid.<br />
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== Overview ==<br />
This paper describes a new unsupervised approach for identifying the main themes of any text document with the aid of Wikipedia. In contrast to others, the proposed algorithm relies on merely two main aspects of Wikipedia, namely its articles titles and categories structure. The inner content of the articles of Wikipedia are not employed in algorithm. Authors describe in this paper how to build a Term-Categories vector that defines how strong a term is associated to a Wikipedia concept. Authors also explain how this vector is employed when processing a text document to discover its main themes. Authors report the performance of method by attempting to predict the most representative categories for a subset of Wikipedia articles.</div>Avahttps://wikipediaquality.com/index.php?title=Towards_Identifying_Arguments_in_Wikipedia_Pages&diff=16637Towards Identifying Arguments in Wikipedia Pages2019-05-31T21:02:46Z<p>Ava: Starting a page: Towards Identifying Arguments in Wikipedia Pages</p>
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<div>'''Towards Identifying Arguments in Wikipedia Pages''' - scientific work related to Wikipedia quality published in 2011, written by Hoda Sepehri Rad and Denilson Barbosa.<br />
<br />
== Overview ==<br />
Wikipedia is one of the most widely used repositories of human knowledge today, contributed mostly by a few hundred thousand regular editors. In this open environment, inevitably, differences of opinion arise among editors of the same article. Especially for polemical topics such as religion and politics, difference of opinions among editors may lead to intense "edit wars" in which editors compete to have their opinions and points of view accepted. While such disputes can compromise the reliability of the article (or at least portions of it), they are recorded in the edit history of the articles. Authors posit that exposing such disputes to the reader, and pointing to the portions of the text where they manifest most prominently can be beneficial in helping concerned readers in understanding such topics. In this paper, authors discuss initial efforts towards the problem of automatic evaluation of extracting controversial points in Wikipedia pages.</div>Avahttps://wikipediaquality.com/index.php?title=Using_Wikipedia_to_Improve_Web_Service_Discovery&diff=16636Using Wikipedia to Improve Web Service Discovery2019-05-31T21:00:42Z<p>Ava: Starting a page: Using Wikipedia to Improve Web Service Discovery</p>
<hr />
<div>'''Using Wikipedia to Improve Web Service Discovery''' - scientific work related to Wikipedia quality published in 2012, written by Alejandro Metke Jimenez.<br />
<br />
== Overview ==<br />
Building and maintaining software are not easy tasks. However, thanks to advances in web technologies, a new paradigm is emerging in software development. The Service Oriented Architecture (SOA) is a relatively new approach that helps bridge the gap between business and IT and also helps systems remain</div>Avahttps://wikipediaquality.com/index.php?title=New_Software_Could_Unlock_Wikipedia_in_Whole_New_Way&diff=16635New Software Could Unlock Wikipedia in Whole New Way2019-05-31T20:58:13Z<p>Ava: Adding wikilinks</p>
<hr />
<div>'''New Software Could Unlock Wikipedia in Whole New Way''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Paul Marks]].<br />
<br />
== Overview ==<br />
Complex searches that today's keyword search engines can't handle could be made easy thanks to a new search tool called Swipe</div>Avahttps://wikipediaquality.com/index.php?title=Librarians_as_Wikipedians:_from_Library_History_to_%E2%80%9CLibrarianship_and_Human_Rights%E2%80%9D&diff=16634Librarians as Wikipedians: from Library History to “Librarianship and Human Rights”2019-05-31T20:55:29Z<p>Ava: Starting a page: Librarians as Wikipedians: from Library History to “Librarianship and Human Rights”</p>
<hr />
<div>'''Librarians as Wikipedians: from Library History to “Librarianship and Human Rights”''' - scientific work related to Wikipedia quality published in 2014, written by Kathleen de la Peña McCook.<br />
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== Overview ==<br />
Wikipedia, the free encyclopedia built collaboratively using wiki software, is the most visited reference site on the web. Only 270 librarians identify as Wikipedians of 21,431,799 Wikipedians with named accounts. This needs to change. Understanding Wikipedia is essential to teaching information literacy and editing Wikipedia is essential to foster successful information-seeking behavior. Librarians who become skilled Wikipedians will maintain the centrality of librarianship to knowledge management in the 21st century—especially through active participation in crowdsourcing. Crowdsourcing is the online participation model that makes use of the collective intelligence of online communities for specific purposes in this case creating and editing articles for Wikipedia.</div>Avahttps://wikipediaquality.com/index.php?title=Awarding_the_Self_in_Wikipedia_:_Identity_Work_and_the_Disclosure_of_Knowledge&diff=16633Awarding the Self in Wikipedia : Identity Work and the Disclosure of Knowledge2019-05-31T20:53:58Z<p>Ava: Starting a page: Awarding the Self in Wikipedia : Identity Work and the Disclosure of Knowledge</p>
<hr />
<div>'''Awarding the Self in Wikipedia : Identity Work and the Disclosure of Knowledge''' - scientific work related to Wikipedia quality published in 2010, written by Daniel Ashton.<br />
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== Overview ==<br />
The ‘behind-the-scenes’ discussion and edit pages of Wikipedia reveal a complex layering of debates and discussion between editors. Focusing on how Wikipedia ‘service awards’ can identify and distinguish editors, this paper explores the disclosure of knowledge as it is intimately bound up with identity work. Examining contributions/edits to Wikipedia as disclosures highlights processes of identity management and work.</div>Avahttps://wikipediaquality.com/index.php?title=Facetedpedia:_Enabling_Query-Dependent_Faceted_Search_for_Wikipedia&diff=16632Facetedpedia: Enabling Query-Dependent Faceted Search for Wikipedia2019-05-31T20:52:03Z<p>Ava: Starting a page: Facetedpedia: Enabling Query-Dependent Faceted Search for Wikipedia</p>
<hr />
<div>'''Facetedpedia: Enabling Query-Dependent Faceted Search for Wikipedia''' - scientific work related to Wikipedia quality published in 2010, written by Ning Yan, Chengkai Li, Senjuti Basu Roy, Rakesh Ramegowda and Gautam Das.<br />
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== Overview ==<br />
Facetedpedia is a faceted search system that dynamically discovers query-dependent faceted interfaces for Wikipedia search result articles. In this paper, authors give an overview of Facetedpedia , present the system architecture and implementation techniques, and elaborate on a demonstration scenario.</div>Avahttps://wikipediaquality.com/index.php?title=Indicators_of_Scientific_and_Technological_Culture:_Wikipedia&diff=16631Indicators of Scientific and Technological Culture: Wikipedia2019-05-31T20:50:20Z<p>Ava: + wikilinks</p>
<hr />
<div>'''Indicators of Scientific and Technological Culture: Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Carlos G. Figuerola]], [[Ángel Zazo-Rodríguez]] and [[José Luis Alonso-Berrocal]].<br />
<br />
== Overview ==<br />
In this presentation authors show techniques to automatically process [[Wikipedia]] (Spanish edition) in order to analyze authoring and editing Wikipedia articles, as well as to analyze topics or thematic contents. Specifically, authors centered attention on Science and Technology articles; first, authors apply automatic techniques to detect them, and them authors analyze their relationships with another topics. Also, authors analyze how they are authored and edited, searching differential models, specific of Science & Technology articles.</div>Avahttps://wikipediaquality.com/index.php?title=Wikipedians%E2%80%99_Views_on_Their_Activities&diff=16630Wikipedians’ Views on Their Activities2019-05-31T20:48:53Z<p>Ava: Starting a page: Wikipedians’ Views on Their Activities</p>
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<div>'''Wikipedians’ Views on Their Activities''' - scientific work related to Wikipedia quality published in 2017, written by Arwid Lund.<br />
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== Overview ==<br />
This chapter contains the empirical study of the Wikipedians’ perceptions of their activities on the micro level. Six relationships are investigated in the informants statements: the relations between playing and working, gaming’s relation to the other three main categories (playing, working and labouring), the relation between working and labouring, and, finally, the relationship between playing and labouring. These relationships structure an ideology analysis that aims to identify ideological positions in the empirical material.</div>Avahttps://wikipediaquality.com/index.php?title=A_Framework_to_Represent_and_Mine_Knowledge_Evolution_from_Wikipedia_Revisions&diff=16629A Framework to Represent and Mine Knowledge Evolution from Wikipedia Revisions2019-05-31T20:47:32Z<p>Ava: + links</p>
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<div>'''A Framework to Represent and Mine Knowledge Evolution from Wikipedia Revisions''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Xian Wu]], [[Wei Fan]], [[Meilun Sheng]], [[Li Zhang]], [[Xiaoxiao Shi]], [[Zhong Su]] and [[Yong Yu]].<br />
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== Overview ==<br />
State-of-the-art knowledge representation in semantic web employs a triple format (subject-relation-object). The limitation is that it can only represent static information, but cannot easily encode revisions of semantic web and knowledge evolution. In reality, knowledge does not stay still but evolves over time. In this paper, authors first introduce the concept of "quintuple representation" by adding two new fields, state and time , where state has two values, either in or out , to denote that the referred knowledge takes effective or becomes expired at the given time . Authors then discuss a two-step statistical framework to mine knowledge evolution into the proposed quintuple representation. Utilizing extracted quintuple properly, it not only can reveal knowledge changing history but also detect expired information. Authors evaluate the proposed framework on [[Wikipedia]] revisions, as well as, common web pages currently not in semantic web format.</div>Avahttps://wikipediaquality.com/index.php?title=Link_Spamming_Wikipedia_for_Profit&diff=16628Link Spamming Wikipedia for Profit2019-05-31T20:45:15Z<p>Ava: wikilinks</p>
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<div>'''Link Spamming Wikipedia for Profit''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Andrew G. West]], [[Jian Chang]], [[Krishna K. Venkatasubramanian]], [[Oleg Sokolsky]] and [[Insup Lee]].<br />
<br />
== Overview ==<br />
Collaborative functionality is an increasingly prevalent web technology. To encourage participation, these systems usually have low barriers-to-entry and permissive privileges. Unsurprisingly, ill-intentioned users try to leverage these characteristics for nefarious purposes. In this work, a particular abuse is examined -- link spamming -- the addition of promotional or otherwise inappropriate hyperlinks. Authors analysis focuses on the wiki model and the collaborative encyclopedia, [[Wikipedia]], in particular. A principal goal of spammers is to maximize exposure , the quantity of people who view a link. Creating and analyzing the first Wikipedia link spam corpus, authors find that existing spam strategies perform quite poorly in this regard. The status quo spamming model relies on link persistence to accumulate exposures, a strategy that fails given the diligence of the [[Wikipedia community]]. Instead, authors propose a model that exploits the latency inherent in human anti-spam enforcement. Statistical estimation suggests novel model would produce significantly more link exposures than status quo techniques. More critically, the strategy could prove economically viable for perpetrators, incentivizing its exploitation. To this end, authors address mitigation strategies.</div>Avahttps://wikipediaquality.com/index.php?title=Predicting_the_Popularity_of_Trending_Articles_in_the_Arabic_Wikipedia_Using_Data_Mining_Techniques&diff=16627Predicting the Popularity of Trending Articles in the Arabic Wikipedia Using Data Mining Techniques2019-05-31T20:44:02Z<p>Ava: Adding wikilinks</p>
<hr />
<div>'''Predicting the Popularity of Trending Articles in the Arabic Wikipedia Using Data Mining Techniques''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Hanadi Muqbil Al-Mutairi]] and [[Muhammad Badruddin Khan]].<br />
<br />
== Overview ==<br />
Wikipedia, the open domain encyclopaedia, is considered to be one of the most prominent and most famous online encyclopaedias. There are approximately 270,000 Arabic articles, making it the focus of research and study of many researchers and of those interested in the Arabic language field. In this poster paper, authors study the issues related to trending articles on [[Arabic Wikipedia]] and how it is influenced by certain external stimulants: for example, breaking news, celebrities' tweets, special events from the past, instant messages on any social media application or any other reasons that could affect the Arabic articles in terms of the number of visitors, which authors named the popularity level. By using a data- and text- mining techniques, and the software platform Rapidminer, authors developed four models that enabled us to predict the popularity level of Arabic articles on [[Wikipedia]], depending on the [[features]] of their stimulants.</div>Avahttps://wikipediaquality.com/index.php?title=A_Generic_Method_for_Multi_Word_Extraction_from_Wikipedia&diff=16626A Generic Method for Multi Word Extraction from Wikipedia2019-05-31T20:42:41Z<p>Ava: Wikilinks</p>
<hr />
<div>'''A Generic Method for Multi Word Extraction from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Bozo Bekavac]] and [[Marko Tadić]].<br />
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== Overview ==<br />
This paper presents the generic method for multiword expression extraction from [[Wikipedia]]. The method is using the properties of this specific encyclopedic genre in its HTML format and it relies on the intention of the authors of articles to link to other articles. The relevant links were processed by applying local regular grammars within the NooJ development environment. Authors tested the method on a Croatian version of Wikipedia and authors present the results obtained.</div>Avahttps://wikipediaquality.com/index.php?title=Analytics_and_Art_:_Visualizing_Wikipedia_Word_Frequencies_Using_Image_Collages&diff=16625Analytics and Art : Visualizing Wikipedia Word Frequencies Using Image Collages2019-05-31T20:39:40Z<p>Ava: Starting a page: Analytics and Art : Visualizing Wikipedia Word Frequencies Using Image Collages</p>
<hr />
<div>'''Analytics and Art : Visualizing Wikipedia Word Frequencies Using Image Collages''' - scientific work related to Wikipedia quality published in 2011, written by Elizabeth Do.<br />
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== Overview ==<br />
vii, 84 p. : col. ill. Honors project-Smith College, Northampton, Mass., 2011. Includes bibliographical references (p. 47-52)</div>Avahttps://wikipediaquality.com/index.php?title=The_Readability_of_the_English_Wikipedia_Article_on_Parkinson%27s_Disease&diff=16624The Readability of the English Wikipedia Article on Parkinson's Disease2019-05-31T20:37:45Z<p>Ava: Starting a page: The Readability of the English Wikipedia Article on Parkinson's Disease</p>
<hr />
<div>'''The Readability of the English Wikipedia Article on Parkinson's Disease''' - scientific work related to Wikipedia quality published in 2015, written by Francesco Brigo and Roberto Erro.<br />
<br />
== Overview ==<br />
Millions of people surf the Internet every day as a source of health care information looking for materials about symptoms, diagnosis, treatments and their possible adverse effects, or diagnostic procedures. Since its launch in 2001, the free online encyclopedia Wikipedia has become the most popular general reference website, which contains approximately 30 million articles available in up to 287 languages and over 4.6 million English articles. With 18 billion page views and nearly 500 million unique visitors per month, the English version of Wikipedia ranks the fifth place in the list of the most visited websites, thus being very likely a common source of health care information by both patients and caregivers. Worth of note, the first webpage that appears after entering the keyword ‘‘Parkinson’s disease’’ (PD) on Google, the most popular search engine worldwide, is the Wikipedia article on PD (http://en.wikipedia.org/wiki/Par kinson’s_disease) (search conducted on 7 November 2014). Hence, such an article is likely to be the most immediate source of online information on PD for millions of Internet users worldwide. To fully understand the content of health information, people need to have adequate ‘‘health literacy’’ defined as ‘‘a constellation of skills, including the ability to perform basic reading and numerical tasks required to function in the health care environment’’ [1]. Low literacy level may reduce patients’ abilities to understand health information, follow medical instructions, take drugs correctly, and learn about disease prevention [1]. Authors therefore aimed to evaluate the reading difficulty level of the English Wikipedia article on PD using quantitative readability-assessment scales. On 5 November 2014, the educational material of the Wikipedia article on PD (available at http://en.wikipedia.org/wiki/Parkinson’s_ disease) was downloaded into Microsoft Word and analyzed for its overall level of readability with six different quantitative readability scales, using the online software program ‘‘SMOG Readability Calculator’’ (freely available at http://www.harrymclaughlin.com/SMOG.htm). The readability scales obtained through this software included: the Gunning Fog index, the Coleman Liau index, the Flesch Kincaid Grade Level, the Automated Readability Index, the Simple Measure of Gobbledygook, and the Flesch Reading Ease [2]. The Flesch Reading Ease readability index ranges from 0 to 100, with higher scores indicating more readable text. The other readability indexes correspond instead to the ideal academic grade level (i.e. the number of years of education) that a person would need in order to be able to understand the text easily, on the first reading. Readability calculations are made on the basis of sentence length, number of sentences and the number of syllables or characters per word. Described in general, these calculations penalize polysyllabic words and long, complex sentences. Readility levels of the English Wikipedia article on PD are reported in Table 1. The Flesch Reading Ease yielded F. Brigo Department of Neurology, ‘‘Franz Tappeiner’’ Hospital, Merano, Italy</div>Avahttps://wikipediaquality.com/index.php?title=Discovering_Wikipedia_Conventions_Using_Dbpedia_Properties&diff=16623Discovering Wikipedia Conventions Using Dbpedia Properties2019-05-31T20:35:56Z<p>Ava: Starting a page: Discovering Wikipedia Conventions Using Dbpedia Properties</p>
<hr />
<div>'''Discovering Wikipedia Conventions Using Dbpedia Properties''' - scientific work related to Wikipedia quality published in 2013, written by D. F. Torres, Hala Skaf-Molli, Pascal Molli and Alicia Díaz.<br />
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== Overview ==<br />
Wikipedia is a public and universal encyclopedia where contributors edit articles collaboratively. Wikipedia infoboxes and categories have been used by semantic technologies to create DBpedia, a knowledge base that semantically describes Wikipedia content and makes it publicly available on the Web. Semantic descriptions of DBpedia can be exploited not only for data retrieval, but also for identifying missing navigational paths in Wikipedia. Existing approaches have demonstrated that missing navigational paths are useful for the Wikipedia community, but their injection has to respect the Wikipedia convention. In this paper, authors present a collaborative recommender system approach named BlueFinder, to enhance Wikipedia content with DBpedia properties. BlueFinder implements a supervised learning algorithm to predict the Wikipedia conventions used to represent similar connected pairs of articles; these predictions are used to recommend the best conventions to connect disconnected articles. Authors report on an exhaustive evaluation that shows three remarkable elements: 1 The evidence of a relevant information gap between DBpedia and Wikipedia; 2 Behavior and accuracy of the BlueFinder algorithm; and 3 Differences in Wikipedia conventions according to the specificity of the involved articles. BlueFinder assists Wikipedia contributors to add missing relations between articles, and consequently, it improves Wikipedia content.</div>Avahttps://wikipediaquality.com/index.php?title=Content_Disputes_in_Wikipedia_Reflect_Geopolitical_Instability&diff=16622Content Disputes in Wikipedia Reflect Geopolitical Instability2019-05-31T20:32:54Z<p>Ava: Starting a page: Content Disputes in Wikipedia Reflect Geopolitical Instability</p>
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<div>'''Content Disputes in Wikipedia Reflect Geopolitical Instability''' - scientific work related to Wikipedia quality published in 2011, written by Gordana Apic, Gordana Apic, Matthew J. Betts and Robert B. Russell.<br />
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== Overview ==<br />
Indicators that rank countries according socioeconomic measurements are important tools for regional development and political reform. Those currently in widespread use are sometimes criticized for a lack of reproducibility or the inability to compare values over time, necessitating simple, fast and systematic measures. Here, authors applied the ‘guilt by association’ principle often used in biological networks to the information network within the online encyclopedia Wikipedia to create an indicator quantifying the degree to which pages linked to a country are disputed by contributors. The indicator correlates with metrics of governance, political or economic stability about as well as they correlate with each other, and though faster and simpler, it is remarkably stable over time despite constant changes in the underlying disputes. For some countries, changes over a four year period appear to correlate with world events related to conflicts or economic problems.</div>Avahttps://wikipediaquality.com/index.php?title=Characterization_and_Prediction_of_Wikipedia_Edit_Wars&diff=16621Characterization and Prediction of Wikipedia Edit Wars2019-05-31T20:30:34Z<p>Ava: Starting a page: Characterization and Prediction of Wikipedia Edit Wars</p>
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<div>'''Characterization and Prediction of Wikipedia Edit Wars''' - scientific work related to Wikipedia quality published in 2011, written by Robert Sumi, Taha Yasseri, András Rung, András Kornai and János Kertész.<br />
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== Overview ==<br />
Authors present a new, ecient method for automatically de-</div>Avahttps://wikipediaquality.com/index.php?title=Fact_Discovery_in_Wikipedia&diff=16620Fact Discovery in Wikipedia2019-05-31T20:28:25Z<p>Ava: Starting a page: Fact Discovery in Wikipedia</p>
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<div>'''Fact Discovery in Wikipedia''' - scientific work related to Wikipedia quality published in 2007, written by Sisay Fissaha Adafre, Valentin Jijkoun and M. de Rijke.<br />
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== Overview ==<br />
Authors address the task of extracting focused salient information items, relevant and important for a given topic, from a large encyclopedic resource. Specifically, for a given topic (a Wikipedia article) authors identify snippets from other articles in Wikipedia that contain important information for the topic of the original article, without duplicates. Authors compare several methods for addressing the task, and find that a mixture of content-based, link-based, and layout-based features outperforms other methods, especially in combination with the use of so-called reference corpora that capture the key properties of entities of a common type.</div>Avahttps://wikipediaquality.com/index.php?title=Chinese_Text_Filtering_based_on_Domain_Keywords_Extracted_from_Wikipedia&diff=16619Chinese Text Filtering based on Domain Keywords Extracted from Wikipedia2019-05-31T20:26:32Z<p>Ava: Starting a page: Chinese Text Filtering based on Domain Keywords Extracted from Wikipedia</p>
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<div>'''Chinese Text Filtering based on Domain Keywords Extracted from Wikipedia''' - scientific work related to Wikipedia quality published in 2013, written by Xiang Wang, Hu Li, Yan Jia and SongChang Jin.<br />
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== Overview ==<br />
Several machine learning and information retrieval algorithms have been used for text filtering. All these methods have a common ground that they need positive and negative examples to build user profile. However, not all applications can get good training documents. In this paper, authors present a Wikipedia based method to build user profile without any other training documents. The proposed method extracts keywords of a special category from Wikipedia taxonomy and computes the weights of the extracted keywords based on Wikipedia pages. Experiment results on Chinese news text dataset SogouC show that the proposed method achieves good performance.</div>Avahttps://wikipediaquality.com/index.php?title=Hackers,_Cyborgs,_and_Wikipedians:_the_Political_Economy_and_Cultural_History_of_Wikipedia&diff=16618Hackers, Cyborgs, and Wikipedians: the Political Economy and Cultural History of Wikipedia2019-05-31T20:24:20Z<p>Ava: Starting a page: Hackers, Cyborgs, and Wikipedians: the Political Economy and Cultural History of Wikipedia</p>
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<div>'''Hackers, Cyborgs, and Wikipedians: the Political Economy and Cultural History of Wikipedia''' - scientific work related to Wikipedia quality published in 2011, written by Andrew A. Famiglietti.<br />
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== Overview ==<br />
Victoria Smith Ekstrand, Advisor This dissertation explores the political economy and cultural history of Wikipedia, the free encyclopedia. It demonstrates how Wikipedia, an influential and popular site of knowledge production and distribution, was influenced by its heritage from the hacker communities of the late twentieth century. More specifically, Wikipedia was shaped by an ideal Author call, “the cyborg individual,” which held that the production of knowledge was best entrusted to a widely distributed network of individual human subjects and individually owned computers. Author trace how this ideal emerged from hacker culture in response to anxieties hackers experienced due to their intimate relationships with machines. Author go on to demonstrate how this ideal influenced how Wikipedia was understood both those involved in the early history of the site, and those writing about it. In particular, legal scholar Yochai Benkler seems to base his understanding of Wikipedia and its strengths on the cyborg individual ideal. Having established this, Author then move on to show how the cyborg individual ideal misunderstands Wikipedia’s actual method of production. Most importantly, it overlooks the importance of how the boundaries drawn around communities and shared technological resources shape Wikipedia’s content. Author then proceed to begin the process of building what Author believe is a better way of understanding Wikipedia, by tracing how communities and shared resources shape the production of recent Wikipedia articles.</div>Avahttps://wikipediaquality.com/index.php?title=Efficient_Feature_Integration_with_Wikipedia-Based_Semantic_Feature_Extraction_for_Turkish_Text_Summarization&diff=16617Efficient Feature Integration with Wikipedia-Based Semantic Feature Extraction for Turkish Text Summarization2019-05-31T20:22:56Z<p>Ava: Int.links</p>
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<div>'''Efficient Feature Integration with Wikipedia-Based Semantic Feature Extraction for Turkish Text Summarization''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Aysun Güran]], [[Nilgün Güler Bayazit]] and [[Mustafa Zahid Gürbüz]].<br />
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== Overview ==<br />
This study presents a novel hybrid Turkish text summarization system that combines structural and semantic [[features]]. The system uses 5 structural features, 1 of which is newly proposed and 3 are semantic features whose values are extracted from Turkish [[Wikipedia]] links. The features are combined using the weights calculated by 2 novel approaches. The rst approach makes use of an analytical hierarchical process, which depends on a series of expert judgments based on pairwise comparisons of the features. The second approach makes use of the articial bee colony algorithm for automatically determining the weights of the features. To conrm the signicance of the proposed hybrid system, its performance is evaluated on a new Turkish corpus that contains 110 documents and 3 human-generated extractive summary corpora. The experimental results show that exploiting all of the features by combining them results in a better performance than exploiting each feature individually.</div>Avahttps://wikipediaquality.com/index.php?title=Estimating_Disease_Burden_Using_Google_Trends_and_Wikipedia_Data&diff=16616Estimating Disease Burden Using Google Trends and Wikipedia Data2019-05-31T20:20:13Z<p>Ava: Starting a page: Estimating Disease Burden Using Google Trends and Wikipedia Data</p>
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<div>'''Estimating Disease Burden Using Google Trends and Wikipedia Data''' - scientific work related to Wikipedia quality published in 2017, written by Riyi Qiu, Mirsad Hadzikadic, Lixia Yao and Lixia Yao.<br />
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== Overview ==<br />
Data on disease burden is often used for assessing population health, evaluating the effectiveness of interventions, formulating health policies, and planning future resource allocation. Authors investigated whether Internet usage data, particularly the search volume on Google and page view counts on Wikipedia, are correlated with the disease burden, measured by prevalence and treatment cost, for 1,633 diseases over an 11-year period. Authors also applied the method of least absolute shrinkage and selection operator (LASSO) to predict the burden of diseases, using those Internet data together with three other variables authors quantified previously. Authors found a relatively strong correlation for 39 of 1,633 diseases, including viral hepatitis, diabetes mellitus, other headache syndromes, multiple sclerosis, sleep apnea, hemorrhoids, and disaccharidase deficiency. However, an accurate analysis must consider each condition’s characteristics, including acute/chronic nature, severity, familiarity to the public, and presence of stigma.</div>Avahttps://wikipediaquality.com/index.php?title=Tag-Based_Navigation_for_Peer-To-Peer_Wikipedia&diff=16615Tag-Based Navigation for Peer-To-Peer Wikipedia2019-05-31T20:17:42Z<p>Ava: Starting a page: Tag-Based Navigation for Peer-To-Peer Wikipedia</p>
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<div>'''Tag-Based Navigation for Peer-To-Peer Wikipedia''' - scientific work related to Wikipedia quality published in 2007, written by Jenneke E. Fokker, Johan A. Pouwelse, W Buntine, de H Huib Ridder and Piet Westendorp.<br />
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== Overview ==<br />
Authors introduce P2P Wikipedia, a prototype of a personalized tag-based navigation system for Wikipedia multimedia content. It is the first peer-to-peer (P2P) file sharing system able to deal with large files like movies, music, and software, but that is also scalable to HTML content. The combined techniques in prototype are the automated calculation of tags from HTML content, a personalized P2P file sharing system built on a social network, the use of incentives for user cooperation to optimize system performance, and the design of a user interface with advanced navigational features.</div>Avahttps://wikipediaquality.com/index.php?title=A_Productive_Clash_of_Perspectives%3F_the_Interplay_Between_Articles%E2%80%99_and_Authors%E2%80%99_Perspectives_and_Their_Impact_on_Wikipedia_Edits_in_a_Controversial_Domain&diff=16614A Productive Clash of Perspectives? the Interplay Between Articles’ and Authors’ Perspectives and Their Impact on Wikipedia Edits in a Controversial Domain2019-05-31T20:15:02Z<p>Ava: Starting a page: A Productive Clash of Perspectives? the Interplay Between Articles’ and Authors’ Perspectives and Their Impact on Wikipedia Edits in a Controversial Domain</p>
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<div>'''A Productive Clash of Perspectives? the Interplay Between Articles’ and Authors’ Perspectives and Their Impact on Wikipedia Edits in a Controversial Domain''' - scientific work related to Wikipedia quality published in 2017, written by Jens Jirschitzka, Joachim Kimmerle, Iassen Halatchliyski, Julia Hancke, Detmar Meurers and Ulrike Cress.<br />
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== Overview ==<br />
This study examined predictors of the development of Wikipedia articles that deal with controversial issues. Authors chose a corpus of articles in the German-language version of Wikipedia about alternative medicine as a representative controversial issue. Authors extracted edits made until March 2013 and categorized them using a supervised machine learning setup as either being pro conventional medicine, pro alternative medicine, or neutral. Based on these categories, authors established relevant variables, such as the perspectives of articles and of authors at certain points in time, the (im)balance of an article’s perspective, the number of non-neutral edits per article, the number of authors per article, authors’ heterogeneity per article, and incongruity between authors’ and articles’ perspectives. The underlying objective was to predict the development of articles’ perspectives with regard to the controversial topic. The empirical part of the study is embedded in theoretical considerations about editorial biases and the effectiveness of norms and rules in Wikipedia, such as the neutral point of view policy. Authors findings revealed a selection bias where authors edited mainly articles with perspectives similar to their own viewpoint. Regression analyses showed that an author’s perspective as well as the article’s previous perspectives predicted the perspective of the resulting edits, albeit both predictors interact with each other. Further analyses indicated that articles with more non-neutral edits were altogether more balanced. Authors also found a positive effect of the number of authors and of the authors’ heterogeneity on articles’ balance. However, while the effect of the number of authors was reserved to pro-conventional medicine articles, the authors’ heterogenity effect was restricted to pro-alternative medicine articles. Finally, authors found a negative effect of incongruity between authors’ and articles’ perspectives that was pronounced for the pro-alternative medicine articles.</div>Avahttps://wikipediaquality.com/index.php?title=Employing_Wikipedia_Data_for_Coreference_Resolution_in_Russian&diff=16168Employing Wikipedia Data for Coreference Resolution in Russian2019-05-26T06:17:50Z<p>Ava: Starting a page: Employing Wikipedia Data for Coreference Resolution in Russian</p>
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<div>'''Employing Wikipedia Data for Coreference Resolution in Russian''' - scientific work related to Wikipedia quality published in 2017, written by Ilya Azerkovich.<br />
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== Overview ==<br />
Semantic information has been deemed a valuable resource for solving the task of coreference resolution by many researchers. Unfortunately, not much has been done in the direction of using this data when working with Russian data. This work describes the first step of a research, attempting to create a coreference resolution system for Russian based on semantic data, concerned with using Wikipedia information for the task. The obtained results are comparable to ones for English data, which gives reasons to expect their improvement in further steps of the research.</div>Avahttps://wikipediaquality.com/index.php?title=Computing_Semantic_Relatedness_from_Human_Navigational_Paths_on_Wikipedia&diff=16167Computing Semantic Relatedness from Human Navigational Paths on Wikipedia2019-05-26T06:15:46Z<p>Ava: Starting a page: Computing Semantic Relatedness from Human Navigational Paths on Wikipedia</p>
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<div>'''Computing Semantic Relatedness from Human Navigational Paths on Wikipedia''' - scientific work related to Wikipedia quality published in 2013, written by Philipp Singer, Thomas Niebler, Markus Strohmaier and Andreas Hotho.<br />
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== Overview ==<br />
This paper presents a novel approach for computing semantic relatedness between concepts on Wikipedia by using human navigational paths for this task. Authors results suggest that human navigational paths provide a viable source for calculating semantic relatedness between concepts on Wikipedia. Authors also show that authors can improve accuracy by intelligent selection of path corpora based on path characteristics indicating that not all paths are equally useful. Authors work makes an argument for expanding the existing arsenal of data sources for calculating semantic relatedness and to consider the utility of human navigational paths for this task.</div>Avahttps://wikipediaquality.com/index.php?title=Why_and_Where_Wikipedia_is_Cited_in_Journal_Articles&diff=16166Why and Where Wikipedia is Cited in Journal Articles2019-05-26T06:13:33Z<p>Ava: Starting a page: Why and Where Wikipedia is Cited in Journal Articles</p>
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<div>'''Why and Where Wikipedia is Cited in Journal Articles''' - scientific work related to Wikipedia quality published in 2013, written by Fariba Tohidinasab and Hamid R. Jamali.<br />
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== Overview ==<br />
The aim of this research was to identify the motivations for citation to Wikipedia in scientific papers. Also, the number of citation to Wikipedia, location of citation, type of citing papers, subject of citing and cited articles were determined and compared in different subject fields. From all English articles indexed in Scopus in 2007 and 2012 that have cited Wikipedia, 602 articles were selected using stratified random sampling. Content analysis and bibliometric methods were used to carry out the research. Results showed that there are 20 motivations for citing Wikipedia and the most frequent of them are providing general information and definition, facts and figures. Citations to Wikipedia often were in the introduction and introductory sections of papers. Computer sciences, internet and chemistry were the most cited subjects. The use of Wikipedia in articles is increasing both in terms of quantity and diversity. However, there are disciplinary differences both in the amount and the nature of use of Wikipedia.</div>Avahttps://wikipediaquality.com/index.php?title=Monolingual_Text_Similarity_Measures:_a_Comparison_of_Models_over_Wikipedia_Articles_Revisions&diff=16165Monolingual Text Similarity Measures: a Comparison of Models over Wikipedia Articles Revisions2019-05-26T06:11:58Z<p>Ava: Starting a page: Monolingual Text Similarity Measures: a Comparison of Models over Wikipedia Articles Revisions</p>
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<div>'''Monolingual Text Similarity Measures: a Comparison of Models over Wikipedia Articles Revisions''' - scientific work related to Wikipedia quality published in 2009, written by Andreas Eiselt and Paolo Rosso.<br />
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== Overview ==<br />
Measuring the similarity of texts is a common task in detection of co-derivatives, plagiarism and information flow. In general the objective is to locate those fragments of a document that are derived from another text. Authors have carried out an exhaustive comparison of similarity estimation models in order to determine which one performs better on different levels of granularity and languages (English, German, Spanish, and Hindi). In connection with the comparison authors introduce a publicly available corpus specially suited for this task. Furthermore authors introduce some modifications to well known algorithms in order to demonstrate their applicability to this task. Amongst others, experiments show the strengths and weaknesses of the different models with respect to the granularity of the processed texts.</div>Ava