https://wikipediaquality.com/api.php?action=feedcontributions&user=Violet&feedformat=atomWikipedia Quality - User contributions [en]2024-03-29T12:44:37ZUser contributionsMediaWiki 1.30.0https://wikipediaquality.com/index.php?title=Learning_Chronobiology_by_Improving_Wikipedia&diff=23392Learning Chronobiology by Improving Wikipedia2020-01-16T05:59:41Z<p>Violet: + Infobox work</p>
<hr />
<div>{{Infobox work<br />
| title = Learning Chronobiology by Improving Wikipedia<br />
| date = 2012<br />
| authors = [[C.D. Chiang]]<br />[[C.L. Lewis]]<br />[[M.D.E. Wright]]<br />[[S. Agapova]]<br />[[B. Akers]]<br />[[Tej D. Azad]]<br />[[K. Banerjee]]<br />[[P. Carrera]]<br />[[A. Chen]]<br />[[J. Chen]]<br />[[X. Chi]]<br />[[J. Chiou]]<br />[[J. Cooper]]<br />[[M. Czurylo]]<br />[[C. Downs]]<br />[[S.Y. Ebstein]]<br />[[P.G. Fahey]]<br />[[J.W. Goldman]]<br />[[A. Grieff]]<br />[[S. Hsiung]]<br />[[R. Hu]]<br />[[Y. Huang]]<br />[[A. Kapuria]]<br />[[K. Li]]<br />[[I. Marcu]]<br />[[S.H. Moore]]<br />[[A.C. Moseley]]<br />[[N. Nauman]]<br />[[K.M. Ness]]<br />[[D.M. Ngai]]<br />[[A. Panzer]]<br />[[P. Peters]]<br />[[Elizabeth Y. Qin]]<br />[[S. Sadhu]]<br />[[A. Sariol]]<br />[[A. Schellhase]]<br />[[M.B. Schoer]]<br />[[M. Steinberg]]<br />[[G. Surick]]<br />[[Connie Tsai]]<br />[[K. Underwood]]<br />[[A. Wang]]<br />[[M.H. Wang]]<br />[[V.M. Wang]]<br />[[D. Westrich]]<br />[[L.J. Yockey]]<br />[[L. Zhang]]<br />[[Erik D. Herzog]]<br />
| doi = 10.1177/0748730412449578<br />
| link = https://www.ncbi.nlm.nih.gov/pubmed/22855578<br />
}}<br />
'''Learning Chronobiology by Improving Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[C.D. Chiang]], [[C.L. Lewis]], [[M.D.E. Wright]], [[S. Agapova]], [[B. Akers]], [[Tej D. Azad]], [[K. Banerjee]], [[P. Carrera]], [[A. Chen]], [[J. Chen]], [[X. Chi]], [[J. Chiou]], [[J. Cooper]], [[M. Czurylo]], [[C. Downs]], [[S.Y. Ebstein]], [[P.G. Fahey]], [[J.W. Goldman]], [[A. Grieff]], [[S. Hsiung]], [[R. Hu]], [[Y. Huang]], [[A. Kapuria]], [[K. Li]], [[I. Marcu]], [[S.H. Moore]], [[A.C. Moseley]], [[N. Nauman]], [[K.M. Ness]], [[D.M. Ngai]], [[A. Panzer]], [[P. Peters]], [[Elizabeth Y. Qin]], [[S. Sadhu]], [[A. Sariol]], [[A. Schellhase]], [[M.B. Schoer]], [[M. Steinberg]], [[G. Surick]], [[Connie Tsai]], [[K. Underwood]], [[A. Wang]], [[M.H. Wang]], [[V.M. Wang]], [[D. Westrich]], [[L.J. Yockey]], [[L. Zhang]] and [[Erik D. Herzog]].<br />
<br />
== Overview ==<br />
Although chronobiology is of growing interest to scientists, physicians, and the general public, access to recent discoveries and historical perspectives is limited. [[Wikipedia]] is an online, user-written encyclopedia that could enhance public access to current understanding in chronobiology. However, Wikipedia is lacking important information and is not universally trusted. Here, 46 students in a university course edited Wikipedia to enhance public access to important discoveries in chronobiology. Students worked for an average of 9 h each to evaluate the primary literature and available Wikipedia information, nominated sites for editing, and, after voting, edited the 15 Wikipedia pages they determined to be highest priorities. This assignment (http://www.nslc.wustl.edu/courses/Bio4030/wikipedia_project.html) was easy to implement, required relatively short time commitments from the professor and students, and had measurable impacts on Wikipedia and the students. Students created 3 new Wikipedia sites, edi...</div>Violethttps://wikipediaquality.com/index.php?title=A_Wikipedia_Literature_Review&diff=23391A Wikipedia Literature Review2020-01-16T05:57:25Z<p>Violet: Wikilinks</p>
<hr />
<div>'''A Wikipedia Literature Review''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Owen S. Martin]].<br />
<br />
== Overview ==<br />
This paper was originally designed as a literature review for a doctoral dissertation focusing on [[Wikipedia]]. This exposition gives the structure of Wikipedia and the latest trends in Wikipedia research.</div>Violethttps://wikipediaquality.com/index.php?title=Is_Wikipedia_Really_Neutral%3F_a_Sentiment_Perspective_Study_of_War-Related_Wikipedia_Articles_Since_1945&diff=23390Is Wikipedia Really Neutral? a Sentiment Perspective Study of War-Related Wikipedia Articles Since 19452020-01-16T05:56:00Z<p>Violet: Adding wikilinks</p>
<hr />
<div>'''Is Wikipedia Really Neutral? a Sentiment Perspective Study of War-Related Wikipedia Articles Since 1945''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Yiwei Zhou]], [[Alexandra I. Cristea]] and [[Zachary L. Roberts]].<br />
<br />
== Overview ==<br />
Wikipedia is supposed to be supporting the “Neutral Point of View”. Instead of accepting this statement as a fact, the current paper analyses its veracity by specifically analysing a typically controversial (negative) topic, such as war, and answering questions such as “Are there sentiment differences in how [[Wikipedia]] articles in [[different language]]s describe the same war?”. This paper tackles this challenge by proposing an automatic methodology based on article level and concept level sentiment analysis on [[multilingual]] Wikipedia articles. The results obtained so far show that reasons such as people’s feelings of involvement and empathy can lead to sentiment expression differences across multilingual Wikipedia on war-related topics; the more people contribute to an article on a war-related topic, the more extreme sentiment the article will express; different cultures also focus on different concepts about the same war and present different sentiments towards them. Moreover, research provides a framework for performing different levels of sentiment analysis on multilingual texts.</div>Violethttps://wikipediaquality.com/index.php?title=Multiwibi:_the_Multilingual_Wikipedia_Bitaxonomy_Project&diff=23389Multiwibi: the Multilingual Wikipedia Bitaxonomy Project2020-01-16T05:53:36Z<p>Violet: New study: Multiwibi: the Multilingual Wikipedia Bitaxonomy Project</p>
<hr />
<div>'''Multiwibi: the Multilingual Wikipedia Bitaxonomy Project''' - scientific work related to Wikipedia quality published in 2016, written by Tiziano Flati, Daniele Vannella, Tommaso Pasini and Roberto Navigli.<br />
<br />
== Overview ==<br />
Abstract Authors present MultiWiBi, an approach to the automatic creation of two integrated taxonomies for Wikipedia pages and categories written in different languages. In order to create both taxonomies in an arbitrary language, authors first build them in English and then project the two taxonomies to other languages automatically, without the help of language-specific resources or tools. The process crucially leverages a novel algorithm which exploits the information available in either one of the taxonomies to reinforce the creation of the other taxonomy. Authors experiments show that the taxonomical information in MultiWiBi is characterized by a higher quality and coverage than state-of-the-art resources like DBpedia, YAGO, MENTA, WikiNet, LHD and WikiTaxonomy, also across languages. MultiWiBi is available online at http://wibitaxonomy.org/multiwibi .</div>Violethttps://wikipediaquality.com/index.php?title=Infoguides:_Women_on_Wikipedia_Edit-A-Thon:_Biographical_Resources&diff=23388Infoguides: Women on Wikipedia Edit-A-Thon: Biographical Resources2020-01-16T05:50:37Z<p>Violet: Information about: Infoguides: Women on Wikipedia Edit-A-Thon: Biographical Resources</p>
<hr />
<div>'''Infoguides: Women on Wikipedia Edit-A-Thon: Biographical Resources''' - scientific work related to Wikipedia quality published in 2017, written by Lara Nicosia.<br />
<br />
== Overview ==<br />
Information and resources relevant to the Women on Wikipedia Edit-a-thon hosted at RIT on Saturday, March 24th from 11am-4pm</div>Violethttps://wikipediaquality.com/index.php?title=Governance_in_Social_Media:_a_Case_Study_of_the_Wikipedia_Promotion_Process&diff=23387Governance in Social Media: a Case Study of the Wikipedia Promotion Process2020-01-16T05:47:51Z<p>Violet: Adding wikilinks</p>
<hr />
<div>'''Governance in Social Media: a Case Study of the Wikipedia Promotion Process''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Jure Leskovec]], [[Daniel P. Huttenlocher]] and [[Jon M. Kleinberg]].<br />
<br />
== Overview ==<br />
Social media sites are often guided by a core group of committed users engaged in various forms of governance. A crucial aspect of this type of governance is deliberation, in which such a group reaches decisions on issues of importance to the site. Despite its crucial — though subtle — role in how a number of prominent social media sites function, there has been relatively little investigation of the deliberative aspects of social media governance. Here authors explore this issue, investigating a particular deliberative process that is extensive, public, and recorded: the promotion of [[Wikipedia]] admins, which is determined by elections that engage committed members of the [[Wikipedia community]]. Authors find that the group decision-making at the heart of this process exhibits several fundamental forms of relative assessment. First authors observe that the chance that a voter will support a candidate is strongly dependent on the relationship between characteristics of the voter and the candidate. Second authors investigate how both individual voter decisions and overall election outcomes can be based on models that take into account the sequential, public nature of the voting.</div>Violethttps://wikipediaquality.com/index.php?title=Xanthusbase:_Adapting_Wikipedia_Principles_to_a_Model_Organism_Database&diff=23386Xanthusbase: Adapting Wikipedia Principles to a Model Organism Database2020-01-16T05:45:43Z<p>Violet: Categories</p>
<hr />
<div>{{Infobox work<br />
| title = Xanthusbase: Adapting Wikipedia Principles to a Model Organism Database<br />
| date = 2007<br />
| authors = [[Bradley I. Arshinoff]]<br />[[Garret Suen]]<br />[[Eric M. Just]]<br />[[Sohel M. Merchant]]<br />[[Warren A. Kibbe]]<br />[[Rex L. Chisholm]]<br />[[Roy D. Welch]]<br />
| doi = 10.1093/nar/gkl881<br />
| link = https://www.ncbi.nlm.nih.gov/pubmed/17090585<br />
}}<br />
'''Xanthusbase: Adapting Wikipedia Principles to a Model Organism Database''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Bradley I. Arshinoff]], [[Garret Suen]], [[Eric M. Just]], [[Sohel M. Merchant]], [[Warren A. Kibbe]], [[Rex L. Chisholm]] and [[Roy D. Welch]].<br />
<br />
== Overview ==<br />
xanthusBase (http://www.xanthusbase.org) is the official model organism database (MOD) for the social bacterium Myxococcus xanthus. In many respects, M.xanthus represents the pioneer model organism (MO) for studying the genetic, biochemical, and mechanistic basis of prokaryotic multicellularity, a topic that has garnered considerable attention due to the significance of biofilms in both basic and applied microbiology research. To facilitate its utility, the design of xanthusBase incorporates [[open-source]] software, leveraging the cumulative experience made available through the Generic Model Organism Database (GMOD) project, [[MediaWiki]] (http://www.mediawiki.org), and dictyBase (http://www.dictybase.org), to create a MOD that is both highly useful and easily navigable. In addition, authors have incorporated a unique [[Wikipedia]]-style curation model which exploits the internet's inherent interactivity, thus enabling M.xanthus and other myxobacterial researchers to contribute directly toward the ongoing genome annotation.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Arshinoff, Bradley I.; Suen, Garret; Just, Eric M.; Merchant, Sohel M.; Kibbe, Warren A.; Chisholm, Rex L.; Welch, Roy D.. (2007). "[[Xanthusbase: Adapting Wikipedia Principles to a Model Organism Database]]". Oxford University Press. DOI: 10.1093/nar/gkl881. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Arshinoff |first1=Bradley I. |last2=Suen |first2=Garret |last3=Just |first3=Eric M. |last4=Merchant |first4=Sohel M. |last5=Kibbe |first5=Warren A. |last6=Chisholm |first6=Rex L. |last7=Welch |first7=Roy D. |title=Xanthusbase: Adapting Wikipedia Principles to a Model Organism Database |date=2007 |doi=10.1093/nar/gkl881 |url=https://wikipediaquality.com/wiki/Xanthusbase:_Adapting_Wikipedia_Principles_to_a_Model_Organism_Database |journal=Oxford University Press}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Arshinoff, Bradley I.; Suen, Garret; Just, Eric M.; Merchant, Sohel M.; Kibbe, Warren A.; Chisholm, Rex L.; Welch, Roy D.. (2007). &amp;quot;<a href="https://wikipediaquality.com/wiki/Xanthusbase:_Adapting_Wikipedia_Principles_to_a_Model_Organism_Database">Xanthusbase: Adapting Wikipedia Principles to a Model Organism Database</a>&amp;quot;. Oxford University Press. DOI: 10.1093/nar/gkl881. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Violethttps://wikipediaquality.com/index.php?title=What_is_Wikipedia_and_Could_It_Help_Your_Digital_Collection&diff=23385What is Wikipedia and Could It Help Your Digital Collection2020-01-16T05:43:31Z<p>Violet: Adding embed</p>
<hr />
<div>{{Infobox work<br />
| title = What is Wikipedia and Could It Help Your Digital Collection<br />
| date = 2008<br />
| authors = [[K. Menzies]]<br />
| link = https://pure.strath.ac.uk/portal/en/publications/what-is-wikipedia-and-could-it-help-your-digital-collection(68abfcc0-24a3-4b47-b46d-adb370e12a40)/export.html<br />
}}<br />
'''What is Wikipedia and Could It Help Your Digital Collection''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[K. Menzies]].<br />
<br />
== Overview ==<br />
Using a combination of qualitative and quantitative methods, this article proposes a potential framework for understanding and measuring the representation and visibility of digital library collections on the free community-driven encyclopaedia website, [[Wikipedia]]. The Glasgow Digital Library is used as an example. References to it on Wikipedia are compared with references made to five other digital library collections, including the New York Public Library. Various technical concerns such as the implementation of links and inter-language links are considered. The author assumes the validity and relevance of Wikipedia as a useful tool and collaborator for those wishing to assess awareness of their digital library collection and discusses the similarities in mission between digital library practitioners and Wikipedia contributors and developers.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Menzies, K.. (2008). "[[What is Wikipedia and Could It Help Your Digital Collection]]".<br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Menzies |first1=K. |title=What is Wikipedia and Could It Help Your Digital Collection |date=2008 |url=https://wikipediaquality.com/wiki/What_is_Wikipedia_and_Could_It_Help_Your_Digital_Collection}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Menzies, K.. (2008). &amp;quot;<a href="https://wikipediaquality.com/wiki/What_is_Wikipedia_and_Could_It_Help_Your_Digital_Collection">What is Wikipedia and Could It Help Your Digital Collection</a>&amp;quot;.<br />
</nowiki><br />
</code></div>Violethttps://wikipediaquality.com/index.php?title=Wikibabel:_a_System_for_Multilingual_Wikipedia_Content&diff=23384Wikibabel: a System for Multilingual Wikipedia Content2020-01-16T05:41:47Z<p>Violet: + links</p>
<hr />
<div>'''Wikibabel: a System for Multilingual Wikipedia Content''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[A. Kumaran]], [[Naren Datha]], [[Balasubramanyan Ashok]], [[K. Saravanan]], [[Anil Ande]], [[Ashwani Sharma]], [[Sridhar Vedantham]], [[Vidya Natampally]], [[Vikram Dendi]] and [[Sandor Maurice]].<br />
<br />
== Overview ==<br />
This position paper outlines project - WikiBABEL - which will be released as an [[open source]] project for the creation of multi- lingual [[Wikipedia]] content, and has potential to produce parallel data as a by-product for Ma- chine Translation systems research. Authors dis- cuss its architecture, functionality and the user-experience components, and briefly pre- sent an analysis that emphasizes the resonance that the WikiBABEL design and the planned involvement with Wikipedia has with the open source communities in general and Wik- ipedians in particular.</div>Violethttps://wikipediaquality.com/index.php?title=Qa%2BMl@Wikipedia%26Google&diff=23383Qa+Ml@Wikipedia&Google2020-01-16T05:39:11Z<p>Violet: + wikilinks</p>
<hr />
<div>'''Qa+Ml@Wikipedia&Google''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Gomes Silva]].<br />
<br />
== Overview ==<br />
As the amount of textual information available in the World Wide Web increases, it is becoming harder and harder for regular users to find specific information in a convenient manner. For instance, finding an answer to a simple factual question, such as “Who is the tallest man in the world ?”, can be a fairly tedious task. Web [[question answering]] systems offer a solution to this problem by quickly retrieving succinct answers to questions posed in natural language. However, building such systems typically requires a fairly amount of tedious, time-consuming, and error-prone human labor, which leads to systems that are costly, and difficult to adapt to different application domains or languages. To cope with these problems, in this thesis, authors propose a multi-pronged approach to web question answering, with a strong focus on machine learning techniques, that allow the system to learn rules instead of having a human expert handcrafting them. Particularly, authors propose a system comprised of three components: question classification, passage retrieval, and answer extraction. For the first component, authors developed a state-of-the-art machine learning-based question classifier, that uses a rich set of lexical, syntactic and semantic [[features]]. For passage retrieval, authors employ a multi-strategy approach that selects the appropriate information source, depending on the type of the question. Finally, for answer extraction, authors utilize several extraction techniques that range from simple regular expressions to automatic machine learning-based [[named entity]] recognizers. The system was evaluated using a set of questions that were asked by potential users of the system, yielding very promising results.</div>Violethttps://wikipediaquality.com/index.php?title=Getting_a_%E2%80%9CQuick_Fix%E2%80%9D:_First-Year_College_Students%E2%80%99_Use_of_Wikipedia&diff=23382Getting a “Quick Fix”: First-Year College Students’ Use of Wikipedia2020-01-16T05:37:58Z<p>Violet: Embed</p>
<hr />
<div>{{Infobox work<br />
| title = Getting a “Quick Fix”: First-Year College Students’ Use of Wikipedia<br />
| date = 2015<br />
| authors = [[John C. Garrison]]<br />
| doi = 10.5210/fm.v20i10.5401<br />
| link = http://firstmonday.org/ojs/index.php/fm/article/view/5401/5003<br />
}}<br />
'''Getting a “Quick Fix”: First-Year College Students’ Use of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[John C. Garrison]].<br />
<br />
== Overview ==<br />
College students use [[Wikipedia]] frequently, despite educators’ highly divided opinions about it, and so it is important to understand how and why they are using it. This study followed a first-year class of undergraduate, liberal arts students over the course of their first semester to see how they used, were influenced about, and rated Wikipedia. Data was collected via two surveys of the first-year class, as well as focus groups and a survey of college faculty. This study found that first-year students are uncertain about the variety of ways to use information sources like Wikipedia, and that a direct and balanced approach to this area from instructors may lead to better outcomes than strict prohibition or silence.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Garrison, John C.. (2015). "[[Getting a “Quick Fix”: First-Year College Students’ Use of Wikipedia]]".DOI: 10.5210/fm.v20i10.5401. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Garrison |first1=John C. |title=Getting a “Quick Fix”: First-Year College Students’ Use of Wikipedia |date=2015 |doi=10.5210/fm.v20i10.5401 |url=https://wikipediaquality.com/wiki/Getting_a_“Quick_Fix”:_First-Year_College_Students’_Use_of_Wikipedia}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Garrison, John C.. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Getting_a_“Quick_Fix”:_First-Year_College_Students’_Use_of_Wikipedia">Getting a “Quick Fix”: First-Year College Students’ Use of Wikipedia</a>&amp;quot;.DOI: 10.5210/fm.v20i10.5401. <br />
</nowiki><br />
</code></div>Violethttps://wikipediaquality.com/index.php?title=Cyberactivism_and_Nationalistic_Communicative_Actions_of_Publics:_Framing_and_Agenda-Building_over_Wikipedia_in_International_Disputes&diff=23381Cyberactivism and Nationalistic Communicative Actions of Publics: Framing and Agenda-Building over Wikipedia in International Disputes2020-01-16T05:35:35Z<p>Violet: + embed code</p>
<hr />
<div>{{Infobox work<br />
| title = Cyberactivism and Nationalistic Communicative Actions of Publics: Framing and Agenda-Building over Wikipedia in International Disputes<br />
| date = 2013<br />
| authors = [[Lisa Tam]]<br />
| link = https://eprints.qut.edu.au/103885/<br />
}}<br />
'''Cyberactivism and Nationalistic Communicative Actions of Publics: Framing and Agenda-Building over Wikipedia in International Disputes''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Lisa Tam]].<br />
<br />
== Overview ==<br />
Different from other information processing theories, the Situational Theory of Problem Solving (STOPS) proposes that the underlying goal of communication is problem solving rather than decision making. Whether and how individuals become engaged in the processes of information acquisition, selection and transmission depends on whether they find an issue to be problematic (problem recognition), perceive to be involved in the issue (involvement recognition), feel constrained about resolving the issue (constraint recognition) and have the applicable knowledge to deal with the issue(referent criterion). Using the [[Wikipedia]] page of ”Senkaku Islands dispute” as a case, the present study seeks to examine how individuals become motivated to engage in communicative actions to co-construct an agenda about an ongoing international dispute between the Chinese and Japanese governments. Based on data collected using textual and content analysis of the ”article” page, the "talk” page, the "view history” page, the ”references” section, the ”sources” section and the ”external links” section, the present study seeks to redefine both the independent and dependent variables in STOPS and discusses the significance of Wikipedia, as an international platform for the co-construction of agendas, for the expression of nationalistic sentiments towards international disputes.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Tam, Lisa. (2013). "[[Cyberactivism and Nationalistic Communicative Actions of Publics: Framing and Agenda-Building over Wikipedia in International Disputes]]".<br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Tam |first1=Lisa |title=Cyberactivism and Nationalistic Communicative Actions of Publics: Framing and Agenda-Building over Wikipedia in International Disputes |date=2013 |url=https://wikipediaquality.com/wiki/Cyberactivism_and_Nationalistic_Communicative_Actions_of_Publics:_Framing_and_Agenda-Building_over_Wikipedia_in_International_Disputes}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Tam, Lisa. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Cyberactivism_and_Nationalistic_Communicative_Actions_of_Publics:_Framing_and_Agenda-Building_over_Wikipedia_in_International_Disputes">Cyberactivism and Nationalistic Communicative Actions of Publics: Framing and Agenda-Building over Wikipedia in International Disputes</a>&amp;quot;.<br />
</nowiki><br />
</code></div>Violethttps://wikipediaquality.com/index.php?title=Dft-Extractor:_a_System_to_Extract_Domain-Specific_Faceted_Taxonomies_from_Wikipedia&diff=23380Dft-Extractor: a System to Extract Domain-Specific Faceted Taxonomies from Wikipedia2020-01-16T05:32:59Z<p>Violet: wikilinks</p>
<hr />
<div>'''Dft-Extractor: a System to Extract Domain-Specific Faceted Taxonomies from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Bifan Wei]], [[Jun Liu]], [[Jian Ma]], [[Qinghua Zheng]], [[Wei Zhang]] and [[Boqin Feng]].<br />
<br />
== Overview ==<br />
Extracting faceted taxonomies from the Web has received increasing attention in recent years from the web mining community. Authors demonstrate in this study a novel system called DFT-Extractor, which automatically constructs domain-specific faceted taxonomies from [[Wikipedia]] in three steps: 1) It crawls domain terms from Wikipedia by using a modified topical crawler. 2) Then it exploits a classification model to extract hyponym relations with the use of motif-based [[features]]. 3) Finally, it constructs a faceted taxonomy by applying a community detection algorithm and a group of heuristic rules. DFT-Extractor also provides a graphical user interface to visualize the learned hyponym relations and the tree structure of taxonomies.</div>Violethttps://wikipediaquality.com/index.php?title=Application_of_Seo_Metrics_to_Determine_the_Quality_of_Wikipedia_Articles_and_Their_Sources&diff=23379Application of Seo Metrics to Determine the Quality of Wikipedia Articles and Their Sources2020-01-16T05:31:21Z<p>Violet: Int.links</p>
<hr />
<div>'''Application of Seo Metrics to Determine the Quality of Wikipedia Articles and Their Sources''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Włodzimierz Lewoniewski]], [[Ralf-Christian Härting]], [[Krzysztof Węcel]], [[Christopher Reichstein]] and [[Witold Abramowicz]].<br />
<br />
== Overview ==<br />
The leading online encyclopedia [[Wikipedia]] is struggling with inconsistent [[article quality]] caused by the collaborative editing model. While one can find many helpful articles with consistent information on Wikipedia, there are also a lot of questionable articles with unclear or unfinished information yet. The quality of each article may vary over time as different users repeatedly re-edit content. One of the most important elements of the Wikipedia articles are references which allow to verify content and to show its source to user. Based on the fact that most of these references are web pages, it is possible to get more information about their quality by using citation analysis tools. For science and practice the empirical proof of the quality of the articles in Wikipedia could have a further signal effect, as the citation of Wikipedia articles, especially in scientific practice, is not yet recognised. This paper presents general results of Wikipedia analysis using metrics from the Toolbox SISTRIX, which is one of the leading providers of [[indicators]] for Search Engine Optimization (SEO). In addition to the preliminary analysis of the Wikipedia articles as separate web pages, authors extracted data from more than 30 million references in different [[language versions]] of Wikipedia and analyzed over 180 thousand most popular hosts. In addition, authors compared the same sources from different geographical perspectives using country-specific visibility indices.</div>Violethttps://wikipediaquality.com/index.php?title=What_Types_of_Translations_Hide_in_Wikipedia&diff=23378What Types of Translations Hide in Wikipedia2020-01-16T05:30:11Z<p>Violet: + category</p>
<hr />
<div>{{Infobox work<br />
| title = What Types of Translations Hide in Wikipedia<br />
| date = 2008<br />
| authors = [[Jonas Sjöbergh]]<br />[[Olof Sjöbergh]]<br />[[Kenji Araki]]<br />
| doi = 10.1007/978-3-540-78159-2_6<br />
| link = https://dl.acm.org/citation.cfm?id=1787808<br />
}}<br />
'''What Types of Translations Hide in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Jonas Sjöbergh]], [[Olof Sjöbergh]] and [[Kenji Araki]].<br />
<br />
== Overview ==<br />
Authors extend an automatically generated bilingual Japanese-Swedish dictionary with new translations, automatically discovered from the multi-lingual online encyclopedia [[Wikipedia]]. Over 50,000 translations, most of which are not present in the original dictionary, are generated, with very high translation quality. Authors analyze what types of translations can be generated by this simple method. The majority of the words are proper nouns, and other types of (usually) uninteresting translations are also generated. Not counting the less interesting words, about 15,000 new translations are still found. Checking against logs of search queries from the old dictionary shows that the new translations would significantly reduce the number of searches with no matching translation.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Sjöbergh, Jonas; Sjöbergh, Olof; Araki, Kenji. (2008). "[[What Types of Translations Hide in Wikipedia]]". Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-540-78159-2_6. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Sjöbergh |first1=Jonas |last2=Sjöbergh |first2=Olof |last3=Araki |first3=Kenji |title=What Types of Translations Hide in Wikipedia |date=2008 |doi=10.1007/978-3-540-78159-2_6 |url=https://wikipediaquality.com/wiki/What_Types_of_Translations_Hide_in_Wikipedia |journal=Springer, Berlin, Heidelberg}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Sjöbergh, Jonas; Sjöbergh, Olof; Araki, Kenji. (2008). &amp;quot;<a href="https://wikipediaquality.com/wiki/What_Types_of_Translations_Hide_in_Wikipedia">What Types of Translations Hide in Wikipedia</a>&amp;quot;. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-540-78159-2_6. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]<br />
[[Category:Swedish Wikipedia]]<br />
[[Category:Japanese Wikipedia]]</div>Violethttps://wikipediaquality.com/index.php?title=Automatising_the_Learning_of_Lexical_Patterns:_an_Application_to_the_Enrichment_of_Wordnet_by_Extracting_Semantic_Relationships_from_Wikipedia&diff=23377Automatising the Learning of Lexical Patterns: an Application to the Enrichment of Wordnet by Extracting Semantic Relationships from Wikipedia2020-01-16T05:28:33Z<p>Violet: + categories</p>
<hr />
<div>{{Infobox work<br />
| title = Automatising the Learning of Lexical Patterns: an Application to the Enrichment of Wordnet by Extracting Semantic Relationships from Wikipedia<br />
| date = 2007<br />
| authors = [[Maria Ruiz-Casado]]<br />[[Enrique Alfonseca]]<br />[[Pablo Castells]]<br />
| doi = 10.1016/j.datak.2006.06.011<br />
| link = http://www.sciencedirect.com/science/article/pii/S0169023X06001169<br />
}}<br />
'''Automatising the Learning of Lexical Patterns: an Application to the Enrichment of Wordnet by Extracting Semantic Relationships from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Maria Ruiz-Casado]], [[Enrique Alfonseca]] and [[Pablo Castells]].<br />
<br />
== Overview ==<br />
This paper describes an automatic approach to identify lexical patterns that represent semantic relationships between concepts in an on-line encyclopedia. Next, these patterns can be applied to extend existing ontologies or semantic networks with new relations. The experiments have been performed with the Simple [[English Wikipedia]] and [[WordNet]] 1.7. A new algorithm has been devised for automatically generalising the lexical patterns found in the encyclopedia entries. Authors have found general patterns for the hyperonymy, hyponymy, holonymy and meronymy relations and, using them, authors have extracted more than 2600 new relationships that did not appear in WordNet originally. The precision of these relationships depends on the degree of generality chosen for the patterns and the type of relation, being around 60-70% for the best combinations proposed.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Ruiz-Casado, Maria; Alfonseca, Enrique; Castells, Pablo. (2007). "[[Automatising the Learning of Lexical Patterns: an Application to the Enrichment of Wordnet by Extracting Semantic Relationships from Wikipedia]]". Elsevier Science Publishers B. V.. DOI: 10.1016/j.datak.2006.06.011. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Ruiz-Casado |first1=Maria |last2=Alfonseca |first2=Enrique |last3=Castells |first3=Pablo |title=Automatising the Learning of Lexical Patterns: an Application to the Enrichment of Wordnet by Extracting Semantic Relationships from Wikipedia |date=2007 |doi=10.1016/j.datak.2006.06.011 |url=https://wikipediaquality.com/wiki/Automatising_the_Learning_of_Lexical_Patterns:_an_Application_to_the_Enrichment_of_Wordnet_by_Extracting_Semantic_Relationships_from_Wikipedia |journal=Elsevier Science Publishers B. V.}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Ruiz-Casado, Maria; Alfonseca, Enrique; Castells, Pablo. (2007). &amp;quot;<a href="https://wikipediaquality.com/wiki/Automatising_the_Learning_of_Lexical_Patterns:_an_Application_to_the_Enrichment_of_Wordnet_by_Extracting_Semantic_Relationships_from_Wikipedia">Automatising the Learning of Lexical Patterns: an Application to the Enrichment of Wordnet by Extracting Semantic Relationships from Wikipedia</a>&amp;quot;. Elsevier Science Publishers B. V.. DOI: 10.1016/j.datak.2006.06.011. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]<br />
[[Category:English Wikipedia]]</div>Violethttps://wikipediaquality.com/index.php?title=Socialization_Tactics_in_Wikipedia_and_Their_Effects&diff=23376Socialization Tactics in Wikipedia and Their Effects2020-01-16T05:27:21Z<p>Violet: Infobox</p>
<hr />
<div>{{Infobox work<br />
| title = Socialization Tactics in Wikipedia and Their Effects<br />
| date = 2010<br />
| authors = [[Boreum Choi]]<br />[[Kira Alexander]]<br />[[Robert E. Kraut]]<br />[[John M. Levine]]<br />
| doi = 10.1145/1718918.1718940<br />
| link = https://dl.acm.org/citation.cfm?id=1718918.1718940<br />
}}<br />
'''Socialization Tactics in Wikipedia and Their Effects''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Boreum Choi]], [[Kira Alexander]], [[Robert E. Kraut]] and [[John M. Levine]].<br />
<br />
== Overview ==<br />
Socialization of newcomers is critical both for conventional groups. It helps groups perform effectively and the newcomers develop commitment. However, little empirical research has investigated the impact of specific socialization tactics on newcomers' commitment to online groups. Authors examined WikiProjects, subgroups in [[Wikipedia]] organized around working on common topics or tasks. In study 1, authors identified the seven socialization tactics used most frequently: invitations to join, welcome messages, requests to work on project-related tasks, offers of assistance, positive feedback on a new member's work, constructive criticism, and personal-related comments. In study 2, authors examined their impact on newcomers' commitment to the project. Whereas most newcomers contributed fewer edits over time, the declines were slowed or reversed for those socialized with welcome messages, assistance, and constructive criticism. In contrast, invitations led to steeper declines in edits. These results suggest that different socialization tactics play different roles in socializing new members in online groups compared to offline ones.</div>Violethttps://wikipediaquality.com/index.php?title=24_Wikipedia_Medical_Page_Editing_as_a_Platform_to_Teach_Evidence-Based_Medicine&diff=2337524 Wikipedia Medical Page Editing as a Platform to Teach Evidence-Based Medicine2020-01-16T05:26:04Z<p>Violet: Adding wikilinks</p>
<hr />
<div>'''24 Wikipedia Medical Page Editing as a Platform to Teach Evidence-Based Medicine''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Heather Murray]], [[Melanie Walker]], [[Lauren A. Maggio]] and [[Jennifer Dawson]].<br />
<br />
== Overview ==<br />
Objectives Medical articles on [[Wikipedia]] are viewed over 10 million times a day and Wikipedia is arguably the most-read medical information platform on the internet. The quality and evidence-base of Wikipedia medical articles are improving but there is an ongoing need for refinement and updating. Editing and improving these articles represents a ‘whole task’ application of the steps in Evidence-Based Medicine (EBM) while simultaneously contributing to an altruistic mission of knowledge sharing and health advocacy. Involving medical students in Wikipedia-editing initiatives provides an opportunity for application of EBM skills while also improving medical articles on Wikipedia. Authors developed an embedded longitudinal Wikipedia editing project as part of a first year critical appraisal course in the School of Medicine at Queen’s University, Canada. Authors goal was to evaluate the design and implementation of this project using student feedback in a structured survey. Method Students completed online training modules provided by Wikipedia and chose a medical article to improve. Students worked in small groups to assess their articles, made suggestions for improvement, and searched the literature for high-quality secondary sources containing suitable evidence. They posted suggested changes to the [[Wikipedia community]] for feedback and consulted with a faculty expert prior to making final page edits. All students completed a Wikipedia project evaluation form. Feedback was sought on the perceived strengths, weaknesses, struggles in project completion, and suggestions for improvement going forward. Using the Five-Dimensional Framework for Authentic Assessment (Gulikers, JTM et al., 2004), student feedback data was reviewed by two investigators (MW and LM) who independently identified barriers to/facilitators in project completion and assigned them into one of five dimensions relating to (1) the task (2) the physical/virtual context (3) the social context; (4) the result and (5) the criteria for evaluation. Results One hundred and one students made over 1000 edits to 16 articles, adding over 10 000 words to the pages, all with appropriate secondary source citations. Based on a preliminary review of the feedback data, students enjoyed applying the critical appraisal skills taught within the broader scope of the course (task), they liked making an improvement to a highly accessed public resource (result), they reported positive collaboration within their teams (social context), and they enjoyed learning about the process involved in forming and editing a Wikipedia medical page (task). Barriers to the project identified by the students included a lack of clarity regarding assignment expectations (task), frustration with Wikipedia coding (task), difficulty engaging with the [[Wikipedia editors]]/community (social context), distrust of Wikipedia editors as content experts (social context), and a perceived mismatch in efforts dedicated to the assignment and the resulting change/impact on their Wikipedia medical page (result). Conclusions Initial results highlight important barriers and facilitators identified by medical students in engaging with and completing the longitudinal Wikipedia assignment as part of their first-year critical appraisal, research and life long learning course. These results will inform the future delivery and assessment of this assignment in an effort to increase engagement among first-year medical students in improving one of the leading sources of online health information worldwide.</div>Violethttps://wikipediaquality.com/index.php?title=Organizing_the_Vision_for_Web_2.0:_a_Study_of_the_Evolution_of_the_Concept_in_Wikipedia&diff=23374Organizing the Vision for Web 2.0: a Study of the Evolution of the Concept in Wikipedia2020-01-16T05:23:20Z<p>Violet: Wikilinks</p>
<hr />
<div>'''Organizing the Vision for Web 2.0: a Study of the Evolution of the Concept in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Arnaud Gorgeon]] and [[E. Burton Swanson]].<br />
<br />
== Overview ==<br />
Information Systems (IS) innovations are often characterized by buzzwords, reflecting organizing visions that structure and express the images and ideas formed by a wide community of users about their meaning and purpose. In this paper, authors examine the evolution of Web 2.0 , a buzzword that is now part of the discourse of a broad community, and look at its entry in [[Wikipedia]] over the three years since its inception in March 2005. Authors imported the revision history from Wikipedia, and analyzed and categorized the edits that were performed and the users that contributed to the article. The patterns of evolution of the types and numbers of contributors and edits lead us to propose four major periods in the evolution of the Web 2.0 article: Seeding, Germination, Growth and Maturity. During the Seeding period, the article evolved mostly underground, with few edits and few contributors active. The article growth took off during the Germination period, receiving increasing attention. Growth was the most active period of development, but also the most controversial. During the last period, Maturity, the article received a decreasing level of attention, current and potential contributors losing interest, as a consensus about what the concept of Web 2.0 means seemed to have been reached.</div>Violethttps://wikipediaquality.com/index.php?title=Wikipedia_Missing_Link_Discovery:_a_Comparative_Study&diff=23373Wikipedia Missing Link Discovery: a Comparative Study2020-01-16T05:21:29Z<p>Violet: + embed code</p>
<hr />
<div>{{Infobox work<br />
| title = Wikipedia Missing Link Discovery: a Comparative Study<br />
| date = 2010<br />
| authors = [[Omer Sunercan]]<br />[[Aysenur Birturk]]<br />
| link = <br />
}}<br />
'''Wikipedia Missing Link Discovery: a Comparative Study''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Omer Sunercan]] and [[Aysenur Birturk]].<br />
<br />
== Overview ==<br />
In this paper, authors describe work on discovering missing links in [[Wikipedia]] articles. This task is important for both readers and authors of Wikipedia. The readers will benefit from the increased [[article quality]] with better navigation support. On the other hand, the system can be employed to support the authors during editing. This study combines the strengths of different approaches previously applied for the task, and adds its own techniques to reach satisfactory results. Because of the subjectivity in the nature of the task; automatic evaluation is hard to apply. Comparing approaches seems to be the best method to evaluate new techniques, and authors offer a semi-automatized method for evaluation of the results. The recall is calculated automatically using existing links in Wikipedia. The precision is calculated according to manual evaluations of human assessors. Comparative results for different techniques are presented, showing the success of improvements. Authors employ Turkish Wikipedia, authors are the first to study on it, to examine whether a small instance is scalable enough for such purposes.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Sunercan, Omer; Birturk, Aysenur. (2010). "[[Wikipedia Missing Link Discovery: a Comparative Study]]".<br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Sunercan |first1=Omer |last2=Birturk |first2=Aysenur |title=Wikipedia Missing Link Discovery: a Comparative Study |date=2010 |url=https://wikipediaquality.com/wiki/Wikipedia_Missing_Link_Discovery:_a_Comparative_Study}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Sunercan, Omer; Birturk, Aysenur. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia_Missing_Link_Discovery:_a_Comparative_Study">Wikipedia Missing Link Discovery: a Comparative Study</a>&amp;quot;.<br />
</nowiki><br />
</code></div>Violethttps://wikipediaquality.com/index.php?title=Building_an_Indonesian_Named_Entity_Recognizer_Using_Wikipedia_and_Dbpedia&diff=23372Building an Indonesian Named Entity Recognizer Using Wikipedia and Dbpedia2020-01-16T05:20:16Z<p>Violet: + links</p>
<hr />
<div>'''Building an Indonesian Named Entity Recognizer Using Wikipedia and Dbpedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Andry Luthfi]], [[Bayu Distiawan]] and [[Ruli Manurung]].<br />
<br />
== Overview ==<br />
This paper describes the development of an Indonesian NER system using online data such as [[Wikipedia]] 1 and DBPedia 2. The system is based on the Stanford NER system [8] and utilizes training documents constructed automatically from Wikipedia. Each entity, i.e. word or phrase that has a hyperlink, in the Wikipedia documents are tagged according to information that is obtained from DBPedia. In this very first version, authors are only interested in three entities, namely: Person, Place, and Organization. The system is evaluated using cross fold validation and also evaluated using a gold standard that was manually annotated. Using cross validation evaluation, Indonesian NER managed to obtain precision and recall values above 90%, whereas the evaluation using gold standard shows that the Indonesian NER achieves high precision but very low recall.</div>Violethttps://wikipediaquality.com/index.php?title=Casting_a_Web_of_Trust_over_Wikipedia:_an_Interaction-Based_Approach&diff=23371Casting a Web of Trust over Wikipedia: an Interaction-Based Approach2020-01-16T05:18:47Z<p>Violet: Cats.</p>
<hr />
<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 />
}}<br />
'''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 />
<br />
== 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.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Maniu, Silviu; Abdessalem, Talel; Cautis, Bogdan. (2011). "[[Casting a Web of Trust over Wikipedia: an Interaction-Based Approach]]".DOI: 10.1145/1963192.1963237. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Maniu |first1=Silviu |last2=Abdessalem |first2=Talel |last3=Cautis |first3=Bogdan |title=Casting a Web of Trust over Wikipedia: an Interaction-Based Approach |date=2011 |doi=10.1145/1963192.1963237 |url=https://wikipediaquality.com/wiki/Casting_a_Web_of_Trust_over_Wikipedia:_an_Interaction-Based_Approach}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Maniu, Silviu; Abdessalem, Talel; Cautis, Bogdan. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Casting_a_Web_of_Trust_over_Wikipedia:_an_Interaction-Based_Approach">Casting a Web of Trust over Wikipedia: an Interaction-Based Approach</a>&amp;quot;.DOI: 10.1145/1963192.1963237. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Violethttps://wikipediaquality.com/index.php?title=What_Do_Wikidata_and_Wikipedia_Have_in_Common%3F:_an_Analysis_of_Their_Use_of_External_References&diff=23370What Do Wikidata and Wikipedia Have in Common?: an Analysis of Their Use of External References2020-01-16T05:16:37Z<p>Violet: Categories</p>
<hr />
<div>{{Infobox work<br />
| title = What Do Wikidata and Wikipedia Have in Common?: an Analysis of Their Use of External References<br />
| date = 2017<br />
| authors = [[Alessandro Piscopo]]<br />[[Pavlos Vougiouklis]]<br />[[Lucie-Aimée Kaffee]]<br />[[Christopher Phethean]]<br />[[Jonathon S. Hare]]<br />[[Elena Simperl]]<br />
| doi = 10.1145/3125433.3125445<br />
| link = http://dl.acm.org/citation.cfm?id=3125445<br />
}}<br />
'''What Do Wikidata and Wikipedia Have in Common?: an Analysis of Their Use of External References''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Alessandro Piscopo]], [[Pavlos Vougiouklis]], [[Lucie-Aimée Kaffee]], [[Christopher Phethean]], [[Jonathon S. Hare]] and [[Elena Simperl]].<br />
<br />
== Overview ==<br />
Wikidata is a community-driven knowledge graph, strongly linked to [[Wikipedia]]. However, the connection between the two projects has been sporadically explored. Authors investigated the relationship between the two projects in terms of the information they contain by looking at their external references. Authors findings show that while only a small number of sources is directly reused across [[Wikidata]] and Wikipedia, references often point to the same domain. Furthermore, Wikidata appears to use less Anglo-American-centred sources. These results deserve further in-depth investigation.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Piscopo, Alessandro; Vougiouklis, Pavlos; Kaffee, Lucie-Aimée; Phethean, Christopher; Hare, Jonathon S.; Simperl, Elena. (2017). "[[What Do Wikidata and Wikipedia Have in Common?: an Analysis of Their Use of External References]]".DOI: 10.1145/3125433.3125445. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Piscopo |first1=Alessandro |last2=Vougiouklis |first2=Pavlos |last3=Kaffee |first3=Lucie-Aimée |last4=Phethean |first4=Christopher |last5=Hare |first5=Jonathon S. |last6=Simperl |first6=Elena |title=What Do Wikidata and Wikipedia Have in Common?: an Analysis of Their Use of External References |date=2017 |doi=10.1145/3125433.3125445 |url=https://wikipediaquality.com/wiki/What_Do_Wikidata_and_Wikipedia_Have_in_Common?:_an_Analysis_of_Their_Use_of_External_References}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Piscopo, Alessandro; Vougiouklis, Pavlos; Kaffee, Lucie-Aimée; Phethean, Christopher; Hare, Jonathon S.; Simperl, Elena. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/What_Do_Wikidata_and_Wikipedia_Have_in_Common?:_an_Analysis_of_Their_Use_of_External_References">What Do Wikidata and Wikipedia Have in Common?: an Analysis of Their Use of External References</a>&amp;quot;.DOI: 10.1145/3125433.3125445. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Violethttps://wikipediaquality.com/index.php?title=Sinai_at_Imageclef_2009_Wikipediamm_Task&diff=23369Sinai at Imageclef 2009 Wikipediamm Task2020-01-16T05:14:06Z<p>Violet: cats.</p>
<hr />
<div>{{Infobox work<br />
| title = Sinai at Imageclef 2009 Wikipediamm Task<br />
| date = 2009<br />
| authors = [[Manuel Carlos Díaz-Galiano]]<br />[[María Teresa Martín-Valdivia]]<br />[[Luis Alfonso Ureña López]]<br />[[José M. Perea-Ortega]]<br />
| link = http://ceur-ws.org/Vol-1175/CLEF2009wn-ImageCLEF-DiazGalianoEt2009.pdf<br />
}}<br />
'''Sinai at Imageclef 2009 Wikipediamm Task''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Manuel Carlos Díaz-Galiano]], [[María Teresa Martín-Valdivia]], [[Luis Alfonso Ureña López]] and [[José M. Perea-Ortega]].<br />
<br />
== Overview ==<br />
This paper describes the rst participation of the SINAI team in the CLEF 2009 wikipediaMM task. This year, authors only want to establish a rst contact with the task and the collections. Thus, authors have generated a new collection expanding with [[WordNet]] terms in order to perform the information included in this collection. In addition, authors have expanded de queries with WordNet too. Authors have used the LEMUR toolkit as the Information Retrieval system in experiments.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Díaz-Galiano, Manuel Carlos; Martín-Valdivia, María Teresa; López, Luis Alfonso Ureña; Perea-Ortega, José M.. (2009). "[[Sinai at Imageclef 2009 Wikipediamm Task]]".<br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Díaz-Galiano |first1=Manuel Carlos |last2=Martín-Valdivia |first2=María Teresa |last3=López |first3=Luis Alfonso Ureña |last4=Perea-Ortega |first4=José M. |title=Sinai at Imageclef 2009 Wikipediamm Task |date=2009 |url=https://wikipediaquality.com/wiki/Sinai_at_Imageclef_2009_Wikipediamm_Task}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Díaz-Galiano, Manuel Carlos; Martín-Valdivia, María Teresa; López, Luis Alfonso Ureña; Perea-Ortega, José M.. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Sinai_at_Imageclef_2009_Wikipediamm_Task">Sinai at Imageclef 2009 Wikipediamm Task</a>&amp;quot;.<br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Violethttps://wikipediaquality.com/index.php?title=Semi-Automatic_Construction_of_a_Domain_Ontology_for_Wind_Energy_Using_Wikipedia_Articles&diff=23368Semi-Automatic Construction of a Domain Ontology for Wind Energy Using Wikipedia Articles2020-01-16T05:12:55Z<p>Violet: Adding infobox</p>
<hr />
<div>{{Infobox work<br />
| title = Semi-Automatic Construction of a Domain Ontology for Wind Energy Using Wikipedia Articles<br />
| date = 2014<br />
| authors = [[Dilek Küçük]]<br />[[Yusuf Arslan]]<br />
| doi = 10.1016/j.renene.2013.08.002<br />
| link = http://www.sciencedirect.com/science/article/pii/S0960148113004035<br />
| plink = https://arxiv.org/abs/1410.8581v1<br />
}}<br />
'''Semi-Automatic Construction of a Domain Ontology for Wind Energy Using Wikipedia Articles''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Dilek Küçük]] and [[Yusuf Arslan]].<br />
<br />
== Overview ==<br />
Domain ontologies are important information sources for knowledge-based systems. Yet, building domain ontologies from scratch is known to be a very labor-intensive process. In this study, authors present semi-automatic approach to building an [[ontology]] for the domain of wind energy which is an important type of renewable energy with a growing share in electricity generation all over the world. Related [[Wikipedia]] articles are first processed in an automated manner to determine the basic concepts of the domain together with their properties and next the concepts, properties, and relationships are organized to arrive at the ultimate ontology. Authors also provide pointers to other engineering ontologies which could be utilized together with the proposed wind energy ontology in addition to its prospective application areas. The current study is significant as, to the best of knowledge, it proposes the first considerably wide-coverage ontology for the wind energy domain and the ontology is built through a semi-automatic process which makes use of the related Web resources, thereby reducing the overall cost of the ontology building process.</div>Violethttps://wikipediaquality.com/index.php?title=Iranian_Efl_Learners%27_Vocabulary_Development_Through_Wikipedia&diff=23367Iranian Efl Learners' Vocabulary Development Through Wikipedia2020-01-16T05:11:19Z<p>Violet: Adding wikilinks</p>
<hr />
<div>'''Iranian Efl Learners' Vocabulary Development Through Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Reza Khany]] and [[Fereshteh Khosravian]].<br />
<br />
== Overview ==<br />
Language teaching has passed through a long way in search of a remedy for language learners and teachers. Countless theories, approaches, and methods have been recommended. With all these, however, more inclusive L2 theories and models ought to be considered to come up with real classroom practices. One of such crucial practices is authenticity, being straightforwardly found in web-based materials in general and [[Wikipedia]] texts and tasks in particular. In the same line and based on sound theoretical underpinnings, the place of Wikipedia is investigated in this study as a prospective tool to teach and learn a major language component with practical procedures i.e. vocabulary knowledge. To this end, 36 intermediate Iranian EFL students assigned to two control and experimental groups took part in the study. The results of the tests administered divulged that the learners in the Wikipedia group surpassed those of the control group. Hence, Wikipedia is considered as an encouraging authentic resource to assist EFL learners in improving their vocabulary knowledge. Implications of present findings and suggestions for further research are discussed.</div>Violethttps://wikipediaquality.com/index.php?title=Educational_Tool_based_on_Topology_and_Evolution_of_Hyperlinks_in_the_Wikipedia&diff=23366Educational Tool based on Topology and Evolution of Hyperlinks in the Wikipedia2020-01-16T05:10:17Z<p>Violet: Infobox work</p>
<hr />
<div>{{Infobox work<br />
| title = Educational Tool based on Topology and Evolution of Hyperlinks in the Wikipedia<br />
| date = 2010<br />
| authors = [[Lauri Lahti]]<br />
| doi = 10.1109/ICALT.2010.224<br />
| link = http://ieeexplore.ieee.org/xpl/abstractSimilar.jsp?reload=true&amp;arnumber=5571281&amp;filter%3DAND%28p_IS_Number%3A5571093%29<br />
}}<br />
'''Educational Tool based on Topology and Evolution of Hyperlinks in the Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Lauri Lahti]].<br />
<br />
== Overview ==<br />
Authors propose a new method to support educational exploration in the hyperlink network of the [[Wikipedia]] online encyclopedia. The learner is provided with alternative parallel ranking lists, each one promoting hyperlinks that represent a different pedagogical perspective to the desired learning topic. The learner can browse the conceptual relations between the latest versions of articles or the conceptual relations belonging to consecutive temporal versions of an article, or a mixture of both approaches. Based on her needs and intuition, the learner explores hyperlink network and meanwhile the method builds automatically concept maps that reflect her conceptualization process and can be used for varied educational purposes. Initial experiments with a prototype tool based on the method indicate enhancement to ordinary learning results and suggest further research.</div>Violethttps://wikipediaquality.com/index.php?title=Latent_Groups_in_Online_Communities:_a_Longitudinal_Study_in_Wikipedia&diff=23365Latent Groups in Online Communities: a Longitudinal Study in Wikipedia2020-01-16T05:08:29Z<p>Violet: Int.links</p>
<hr />
<div>'''Latent Groups in Online Communities: a Longitudinal Study in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Arto Lanamäki]] and [[Juho Lindman]].<br />
<br />
== Overview ==<br />
Research on online communities has shown that content production involves manifest groups and latent users. This paper conceptualizes a related but distinct phenomenon of latent groups. Authors ground this contribution in a longitudinal study on the Finnish [[Wikipedia]] (2007---2014). In the case of experts working on content within their area of expertise, individuals can constitute a group that maintains itself over time. In such a setting, it becomes viable to view the group as an acting unit instead of as individual nodes in a network. Such groups are able to sustain their activities even over periods of inactivity. Authors theoretical contribution is the conceptualization of latent groups, which includes two conditions: 1) a group is capable of reforming after inactivity (i.e., dormant), and 2) a group is difficult to observe to an outsider (i.e., non-manifest).</div>Violethttps://wikipediaquality.com/index.php?title=Geo-Temporal_Retrieval_Filtering_Versus_Answer_Resolution_Using_Wikipedia&diff=23364Geo-Temporal Retrieval Filtering Versus Answer Resolution Using Wikipedia2020-01-16T05:07:11Z<p>Violet: Wikilinks</p>
<hr />
<div>'''Geo-Temporal Retrieval Filtering Versus Answer Resolution Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Jorge Machado]], [[José Luis Borbinha]] and [[Bruno Martins]].<br />
<br />
== Overview ==<br />
describe an evaluation experiment on GeoTemporal Document Retrieval created for the GeoTime evaluation task of NTCIR 2011. This work describes the retrieval techniques developed to accomplish this task. Authors describe the collections used in the workshop, detailing the composition of the collections in terms of geographic and temporal expressions. The first contribution of this work is the collections' statistics, which by itself reveals the relevance of this subject. Authors parsing techniques found millions of references related with the dimensions of relevance time and space. Those references were used to index the documents in order to score them in those dimensions. Authors also introduce a technique to find extra references in [[Wikipedia]] using [[Google]] Search Service and the same parsers used in the collections. Those references were used in four different scenarios depending on the queries: first authors used the references found in topics to filter documents without geographic or temporal expressions and used pseudo relevance feedback to expand topics with no references using the indexes created for places and dates; in other approach authors used the Wikipedia references to filter documents from the result set, in a last approach authors expanded all topics with the Wikipedia references. Finally authors used another technique based on metric distances calculated through coordinates (latitudes and longitudes) and dates in order to create a scope for documents and topics, and rank them according to the distance between each other.</div>Violethttps://wikipediaquality.com/index.php?title=Gender_Differences_in_Wikipedia_Editing&diff=23363Gender Differences in Wikipedia Editing2020-01-16T05:05:25Z<p>Violet: + Infobox work</p>
<hr />
<div>{{Infobox work<br />
| title = Gender Differences in Wikipedia Editing<br />
| date = 2011<br />
| authors = [[Judd Antin]]<br />[[Raymond Yee]]<br />[[Coye Cheshire]]<br />[[Oded Nov]]<br />
| doi = 10.1145/2038558.2038561<br />
| link = https://dl.acm.org/citation.cfm?doid=2038558.2038561<br />
}}<br />
'''Gender Differences in Wikipedia Editing''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Judd Antin]], [[Raymond Yee]], [[Coye Cheshire]] and [[Oded Nov]].<br />
<br />
== Overview ==<br />
As [[Wikipedia]] has become an indispensable source of online information, concerns about who writes, edits, and maintains it have come to the forefront. In particular, the 2010 UNU-MERIT survey found evidence of a significant gender skew: fewer than 13% of Wikipedia contributors are women. However, the number of contributors is just one way to examine gender differences in contribution. In this paper authors take a more fine-grained perspective by examining how much and what types of Wiki-work men and women tend to do. First, authors find that the so-called "Gender Gap" in number of editors may not be as wide as prior studies have suggested. Second, although more than 80% of editors in sample were men, among the bottom 75% of editors by activity-level, authors find that men and women made similar numbers of revisions. However, among the most active [[Wikipedians]] men tended to make many more revisions than women. Finally, authors find that the most active women in sample tended to make larger revisions than the most active men. Authors conclude by discussing directions for future research.</div>Violethttps://wikipediaquality.com/index.php?title=Embracing_Wikipedia_as_a_Research_Tool_for_Law:_to_Wikipedia_or_Not_to_Wikipedia%3F&diff=23362Embracing Wikipedia as a Research Tool for Law: to Wikipedia or Not to Wikipedia?2020-01-16T05:03:57Z<p>Violet: Category</p>
<hr />
<div>{{Infobox work<br />
| title = Embracing Wikipedia as a Research Tool for Law: to Wikipedia or Not to Wikipedia?<br />
| date = 2011<br />
| authors = [[Eola Barnett]]<br />[[Roslyn Baer]]<br />
| doi = 10.1080/03069400.2011.578883<br />
| link = https://eprints.qut.edu.au/52379/<br />
}}<br />
'''Embracing Wikipedia as a Research Tool for Law: to Wikipedia or Not to Wikipedia?''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Eola Barnett]] and [[Roslyn Baer]].<br />
<br />
== Overview ==<br />
Useful aspects of [[Wikipedia]] should be embraced as a research tool. Arguments are based upon a consideration of Wikipedia's purpose; policies and controlling mechanisms; commentator views on and academic use of Wikipedia as a teaching and learning tool; the fact that empirical research has found students will use Wikipedia. The results of a survey on the research preferences of a range of students engaged in various levels of legal study, from senior secondary school to second year law students, are presented and recommendations regarding educating students in the appropriate use of Wikipedia for research are made.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Barnett, Eola; Baer, Roslyn. (2011). "[[Embracing Wikipedia as a Research Tool for Law: to Wikipedia or Not to Wikipedia?]]". Taylor & Francis. DOI: 10.1080/03069400.2011.578883. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Barnett |first1=Eola |last2=Baer |first2=Roslyn |title=Embracing Wikipedia as a Research Tool for Law: to Wikipedia or Not to Wikipedia? |date=2011 |doi=10.1080/03069400.2011.578883 |url=https://wikipediaquality.com/wiki/Embracing_Wikipedia_as_a_Research_Tool_for_Law:_to_Wikipedia_or_Not_to_Wikipedia? |journal=Taylor & Francis}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Barnett, Eola; Baer, Roslyn. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Embracing_Wikipedia_as_a_Research_Tool_for_Law:_to_Wikipedia_or_Not_to_Wikipedia?">Embracing Wikipedia as a Research Tool for Law: to Wikipedia or Not to Wikipedia?</a>&amp;quot;. Taylor & Francis. DOI: 10.1080/03069400.2011.578883. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Violethttps://wikipediaquality.com/index.php?title=Towards_a_Class-Based_Model_of_Information_Organization_in_Wikipedia&diff=23361Towards a Class-Based Model of Information Organization in Wikipedia2020-01-16T05:01:59Z<p>Violet: Category</p>
<hr />
<div>{{Infobox work<br />
| title = Towards a Class-Based Model of Information Organization in Wikipedia<br />
| date = 2015<br />
| authors = [[Michael Gilbert]]<br />[[Mark Zachry]]<br />
| doi = 10.1007/978-3-319-20612-7_29<br />
| link = https://link.springer.com/chapter/10.1007/978-3-319-20612-7_29<br />
}}<br />
'''Towards a Class-Based Model of Information Organization in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Michael Gilbert]] and [[Mark Zachry]].<br />
<br />
== Overview ==<br />
As complexity increases in commons-based peer production communities, the means of organizing and facilitating collective action must also mature to ensure the ongoing health and active maintenance of those communities [1]. This study examines the types of structured data that exist in [[Wikipedia]], introduces an argument for an extension to the types of structured and semi-structured data within Wikipedia supported by that descriptive analysis; and presents an implementation of that extension that supports instantiations of semi-structured content that facilitate both human and tool-mediated interactions with Wikipedia data. This extension offers a novel means of structuring data to support the ongoing health and maintenance of online communities like the community of editors that maintain and develop Wikipedia.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Gilbert, Michael; Zachry, Mark. (2015). "[[Towards a Class-Based Model of Information Organization in Wikipedia]]". Springer, Cham. DOI: 10.1007/978-3-319-20612-7_29. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Gilbert |first1=Michael |last2=Zachry |first2=Mark |title=Towards a Class-Based Model of Information Organization in Wikipedia |date=2015 |doi=10.1007/978-3-319-20612-7_29 |url=https://wikipediaquality.com/wiki/Towards_a_Class-Based_Model_of_Information_Organization_in_Wikipedia |journal=Springer, Cham}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Gilbert, Michael; Zachry, Mark. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Towards_a_Class-Based_Model_of_Information_Organization_in_Wikipedia">Towards a Class-Based Model of Information Organization in Wikipedia</a>&amp;quot;. Springer, Cham. DOI: 10.1007/978-3-319-20612-7_29. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Violethttps://wikipediaquality.com/index.php?title=Manypedia:_Comparing_Language_Points_of_View_of_Wikipedia_Communities&diff=23360Manypedia: Comparing Language Points of View of Wikipedia Communities2020-01-16T04:59:16Z<p>Violet: + links</p>
<hr />
<div>'''Manypedia: Comparing Language Points of View of Wikipedia Communities''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Paolo Massa]] and [[Federico Scrinzi]].<br />
<br />
== Overview ==<br />
The 4 million articles of the [[English Wikipedia]] have been written in a collaborative fashion by more than 16 million volunteer editors. On each article, the community of editors strive to reach a [[neutral point of view]], representing all significant views fairly, proportionately, and without biases. However, beside the English one, there are more than 280 editions of [[Wikipedia]] in [[different language]]s and their relatively isolated communities of editors are not forced by the platform to discuss and negotiate their points of view. So the empirical question is: do communities on different language Wikipedias develop their own diverse Linguistic Points of View (LPOV)? To answer this question authors created and released as [[open source]] Manypedia, a web tool whose aim is to facilitate cross-cultural analysis of Wikipedia language communities by providing an easy way to compare automatically translated versions of their different representations of the same topic.</div>Violethttps://wikipediaquality.com/index.php?title=Improving_Query_Expansion_for_Information_Retrieval_Using_Wikipedia&diff=23359Improving Query Expansion for Information Retrieval Using Wikipedia2020-01-16T04:58:11Z<p>Violet: New work - Improving Query Expansion for Information Retrieval Using Wikipedia</p>
<hr />
<div>'''Improving Query Expansion for Information Retrieval Using Wikipedia''' - scientific work related to Wikipedia quality published in 2015, written by Lixin Gan and Huan Hong.<br />
<br />
== Overview ==<br />
Query expansion (QE) is one of the key technologies to improve retrieval efficiency. Many studies on query expansion with relationships from single local corpus suffer from two problems resulting in low retrieval performance: term relationships are limited and unlisted query terms have no expansion terms. To address these problems, relationships between terms captured from Wikipedia are superimposed to the basic Markov network that pre-built using single local corpus. A new larger Markov network is formed with more and richer relationship for each term. Evaluation is performed on three standard information retrieval corpuses including ADI, CISI and CACM.Experimental results show that the proposed technique of superimposed Markov network is effective to select more and confident candidatesfor query expansion and it outperforms other state-of-the-art QE methods.</div>Violethttps://wikipediaquality.com/index.php?title=Critical_Point_of_View:_a_Wikipedia_Reader&diff=23358Critical Point of View: a Wikipedia Reader2020-01-16T04:56:52Z<p>Violet: Infobox</p>
<hr />
<div>{{Infobox work<br />
| title = Critical Point of View: a Wikipedia Reader<br />
| date = 2012<br />
| authors = [[Geert Lovink]]<br />[[Nathaniel Tkacz]]<br />
| doi = 10.2139/ssrn.2075015<br />
| link = https://papers.ssrn.com/abstract=2075015<br />
}}<br />
'''Critical Point of View: a Wikipedia Reader''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Geert Lovink]] and [[Nathaniel Tkacz]].<br />
<br />
== Overview ==<br />
As part of the CPOV project with the Institute of Network Cultures, authors published a [[Wikipedia]] reader titled Critical Point of View. The Reader is edited by Geert Lovink and Nathaniel Tkacz. The essays, interviews and artworks brought together in this reader form part of the overarching Critical Point of View research initiative, which began with a conference in Bangalore (January 2010), followed by events in Amsterdam (March 2010) and Leipzig (September 2010). The Reader collects original insights on the next generation of wiki-related research, from radical artistic interventions and the significant role of bots to hidden trajectories of encyclopaedic knowledge and the politics of agency and exclusion.</div>Violethttps://wikipediaquality.com/index.php?title=Wikipedia_as_Public_Scholarship:_Communicating_Our_Impact_Online&diff=23357Wikipedia as Public Scholarship: Communicating Our Impact Online2020-01-16T04:54:46Z<p>Violet: + wikilinks</p>
<hr />
<div>'''Wikipedia as Public Scholarship: Communicating Our Impact Online''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Elizabeth K. Rush]] and [[Sarah J. Tracy]].<br />
<br />
== Overview ==<br />
To contribute to the forum asking “Has Communication Research Made a Difference?,” this essay examines whether communication scholarship makes a difference (a) to those who search for information online, (b) in the sense that a primary way research can make a difference is through its accessibility, and (c) by using the criteria of its presence (or absence) on [[Wikipedia]]. In this essay, authors reason that Wikipedia is a useful benchmark for online accessibility of public scholarship in that it provides immediate, freely available information to today's diverse global public seeking online answers to questions and relief from problems.</div>Violethttps://wikipediaquality.com/index.php?title=Accuracy_and_Completeness_of_Drug_Information_in_Wikipedia:_an_Assessment&diff=23356Accuracy and Completeness of Drug Information in Wikipedia: an Assessment2020-01-16T04:52:04Z<p>Violet: Adding categories</p>
<hr />
<div>{{Infobox work<br />
| title = Accuracy and Completeness of Drug Information in Wikipedia: an Assessment<br />
| date = 2011<br />
| authors = [[Natalie Kupferberg]]<br />[[Bridget McCrate Protus]]<br />
| doi = 10.3163/1536-5050.99.4.010<br />
| link = https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3193353/<br />
}}<br />
'''Accuracy and Completeness of Drug Information in Wikipedia: an Assessment''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Natalie Kupferberg]] and [[Bridget McCrate Protus]].<br />
<br />
== Overview ==<br />
How complete, accurate, and reliable is the drug information that Americans retrieve from [[Wikipedia]]? The question is important because 74% of American adults look online for health information [1], and Wikipedia is the 7th-most visited site on the web [2]. Consumers using general search engines like [[Google]] or [[Yahoo]] often arrive at Wikipedia. A study using keywords from 3 health indexes found that Wikipedia “ranked among the first ten results in 71–85% of search engines and keywords tested,” with its articles viewed more frequently than the corresponding MedlinePlus topic page [3]. Practitioners also go regularly to Wikipedia for health information. A 2009 study of 1,900 physicians found that 50% used Wikipedia to answer health questions, twice the percentage of the year before [4]. Other studies found that Wikipedia was used by 70% of 35 “junior physicians” who graduated from a major London medical school [5] and by 28% of pharmacists seeking drug information, in most cases to identify medication indications [6].<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Kupferberg, Natalie; Protus, Bridget McCrate. (2011). "[[Accuracy and Completeness of Drug Information in Wikipedia: an Assessment]]". Medical Library Association. DOI: 10.3163/1536-5050.99.4.010. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Kupferberg |first1=Natalie |last2=Protus |first2=Bridget McCrate |title=Accuracy and Completeness of Drug Information in Wikipedia: an Assessment |date=2011 |doi=10.3163/1536-5050.99.4.010 |url=https://wikipediaquality.com/wiki/Accuracy_and_Completeness_of_Drug_Information_in_Wikipedia:_an_Assessment |journal=Medical Library Association}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Kupferberg, Natalie; Protus, Bridget McCrate. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Accuracy_and_Completeness_of_Drug_Information_in_Wikipedia:_an_Assessment">Accuracy and Completeness of Drug Information in Wikipedia: an Assessment</a>&amp;quot;. Medical Library Association. DOI: 10.3163/1536-5050.99.4.010. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Violethttps://wikipediaquality.com/index.php?title=Impact_of_Wikipedia_on_Citation_Trends&diff=23355Impact of Wikipedia on Citation Trends2020-01-16T04:50:22Z<p>Violet: + Infobox work</p>
<hr />
<div>{{Infobox work<br />
| title = Impact of Wikipedia on Citation Trends<br />
| date = 2013<br />
| authors = [[Khadijeh Alishah]]<br />[[Mahdieh Hadi]]<br />[[Saeedeh Hosseinian]]<br />[[Seyed Mohammad Amin Hosseini-Nami]]<br />[[Zhaleh Hosseini]]<br />[[Ali Karimi]]<br />[[Sayed-Amir Marashi]]<br />[[Reihaneh Sadat Mirhassani]]<br />[[Rouhallah RamezaniFard]]<br />[[Zahra Shojaie]]<br />
| doi = 10.17877/DE290R-14943<br />
| link = https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4803015/<br />
}}<br />
'''Impact of Wikipedia on Citation Trends''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Khadijeh Alishah]], [[Mahdieh Hadi]], [[Saeedeh Hosseinian]], [[Seyed Mohammad Amin Hosseini-Nami]], [[Zhaleh Hosseini]], [[Ali Karimi]], [[Sayed-Amir Marashi]], [[Reihaneh Sadat Mirhassani]], [[Rouhallah RamezaniFard]] and [[Zahra Shojaie]].<br />
<br />
== Overview ==<br />
It has been suggested that the “visibility” of an article influences its citation count. More specifically, it is believed that the social media can influence article citations.Here authors tested the hypothesis that inclusion of scholarly references in [[Wikipedia]] affects the citation trends. To perform this analysis, authors introduced a citation “propensity” measure, which is inspired by the concept of amino acid propensity for protein secondary structures. Authors show that although citation counts generally increase during time, the citation “propensity” does not increase after inclusion of a reference in Wikipedia.</div>Violethttps://wikipediaquality.com/index.php?title=Wikipedia_Tools_for_Google_Spreadsheets&diff=22982Wikipedia Tools for Google Spreadsheets2019-12-20T06:29:21Z<p>Violet: Links</p>
<hr />
<div>'''Wikipedia Tools for Google Spreadsheets''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Thomas Steiner]].<br />
<br />
== Overview ==<br />
In this paper, authors introduce the [[Wikipedia]] Tools for [[Google]] Spreadsheets. Google Spreadsheets is part of a free, Web-based software office suite offered by Google within its Google Docs service. It allows users to create and edit spreadsheets online, while collaborating with other users in realtime. Wikipedia is a free-access, free-content Internet encyclopedia, whose content and data is available, among other means, through an API. With the Wikipedia Tools for Google Spreadsheets, authors have created a toolkit that facilitates working with Wikipedia data from within a spreadsheet context. Authors make these tools available as [[open-source]] on GitHub [https://github.com/tomayac/wikipedia-tools-for-google-spreadsheets], released under the permissive Apache 2.0 license.</div>Violethttps://wikipediaquality.com/index.php?title=Readers%27_Demanded_Hyperlink_Prediction_in_Wikipedia&diff=22981Readers' Demanded Hyperlink Prediction in Wikipedia2019-12-20T06:26:35Z<p>Violet: + category</p>
<hr />
<div>{{Infobox work<br />
| title = Readers' Demanded Hyperlink Prediction in Wikipedia<br />
| date = 2018<br />
| authors = [[Laxmi Amulya Gundala]]<br />[[Francesca Spezzano]]<br />
| doi = 10.1145/3184558.3191644<br />
| link = https://dl.acm.org/citation.cfm?doid=3184558.3191644<br />
}}<br />
'''Readers' Demanded Hyperlink Prediction in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Laxmi Amulya Gundala]] and [[Francesca Spezzano]].<br />
<br />
== Overview ==<br />
In this paper, authors describe on-going research on the problem of predicting needed hyperlinks between pairs of [[Wikipedia]] pages (u,v) that are not connected, yet show readers' search navigation from u to v. Authors propose a solution that first estimates how long will these searches last and then predicts new hyperlinks according to descending order of duration. Authors initial experimental results show that best solution achieves an AUROC of 0.77 on the Wikipedia Clickstream dataset and a [email protected]% of 1.0 and significantly beats the baselines.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Gundala, Laxmi Amulya; Spezzano, Francesca. (2018). "[[Readers' Demanded Hyperlink Prediction in Wikipedia]]". International World Wide Web Conferences Steering Committee. DOI: 10.1145/3184558.3191644. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Gundala |first1=Laxmi Amulya |last2=Spezzano |first2=Francesca |title=Readers' Demanded Hyperlink Prediction in Wikipedia |date=2018 |doi=10.1145/3184558.3191644 |url=https://wikipediaquality.com/wiki/Readers'_Demanded_Hyperlink_Prediction_in_Wikipedia |journal=International World Wide Web Conferences Steering Committee}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Gundala, Laxmi Amulya; Spezzano, Francesca. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/Readers'_Demanded_Hyperlink_Prediction_in_Wikipedia">Readers' Demanded Hyperlink Prediction in Wikipedia</a>&amp;quot;. International World Wide Web Conferences Steering Committee. DOI: 10.1145/3184558.3191644. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Violethttps://wikipediaquality.com/index.php?title=Librarian_Perception_of_Wikipedia:_Threats_or_Opportunities_for_Librarianship%3F&diff=22980Librarian Perception of Wikipedia: Threats or Opportunities for Librarianship?2019-12-20T06:25:14Z<p>Violet: + Embed</p>
<hr />
<div>{{Infobox work<br />
| title = Librarian Perception of Wikipedia: Threats or Opportunities for Librarianship?<br />
| date = 2010<br />
| authors = [[Brendan Luyt]]<br />[[Yasmin Ally]]<br />[[Nur Hakim Low]]<br />[[Norah Binte Ismail]]<br />
| doi = 10.1515/libr.2010.005<br />
| link = http://cat.inist.fr/?aModele=afficheN&amp;cpsidt=22561789<br />
}}<br />
'''Librarian Perception of Wikipedia: Threats or Opportunities for Librarianship?''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Brendan Luyt]], [[Yasmin Ally]], [[Nur Hakim Low]] and [[Norah Binte Ismail]].<br />
<br />
== Overview ==<br />
The rapid rise of [[Wikipedia]] as an information source has placed the traditional role of librarians as information gatekeepers and guardians under scrutiny with much of the professional literature suggesting that librarians are polarized over the issue of whether Wikipedia is a useful reference tool. This qualitative study examines the perceptions and behaviours of National Library Board (NLB) of Singapore librarians with regards to information seeking and usage of Wikipedia. It finds that instead of polarized attitudes, most librarians, although cautious about using Wikipedia in their professional capacity, hold a range of generally positive attitudes towards the online encyclopaedia, believing that it has a valid role to play in the information seeking of patrons today. This is heartening because it suggests the existence within the librarian population of attitudes that can be tapped to engage constructively with Wikipedia. Three of these in particular are briefly discussed at the end of the article: Wikipedia's ability to appeal to the so-called "digital natives," its role as a source of non-Western information, and its potential to enable a revitalization of the role of librarians as public intellectuals contributing to a democratic information commons.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Luyt, Brendan; Ally, Yasmin; Low, Nur Hakim; Ismail, Norah Binte. (2010). "[[Librarian Perception of Wikipedia: Threats or Opportunities for Librarianship?]]". De Gruyter. DOI: 10.1515/libr.2010.005. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Luyt |first1=Brendan |last2=Ally |first2=Yasmin |last3=Low |first3=Nur Hakim |last4=Ismail |first4=Norah Binte |title=Librarian Perception of Wikipedia: Threats or Opportunities for Librarianship? |date=2010 |doi=10.1515/libr.2010.005 |url=https://wikipediaquality.com/wiki/Librarian_Perception_of_Wikipedia:_Threats_or_Opportunities_for_Librarianship? |journal=De Gruyter}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Luyt, Brendan; Ally, Yasmin; Low, Nur Hakim; Ismail, Norah Binte. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Librarian_Perception_of_Wikipedia:_Threats_or_Opportunities_for_Librarianship?">Librarian Perception of Wikipedia: Threats or Opportunities for Librarianship?</a>&amp;quot;. De Gruyter. DOI: 10.1515/libr.2010.005. <br />
</nowiki><br />
</code></div>Violethttps://wikipediaquality.com/index.php?title=Context-Aware_Category_Ranking_for_Wikipedia_Concepts&diff=22979Context-Aware Category Ranking for Wikipedia Concepts2019-12-20T06:22:35Z<p>Violet: Infobox work</p>
<hr />
<div>{{Infobox work<br />
| title = Context-Aware Category Ranking for Wikipedia Concepts<br />
| date = 2012<br />
| authors = [[Huiman Hou]]<br />[[Lijiang Chen]]<br />[[Shimin Chen]]<br />[[Peng Jiang]]<br />
| link = https://patents.google.com/patent/WO2014019126A1/en<br />
}}<br />
'''Context-Aware Category Ranking for Wikipedia Concepts''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Huiman Hou]], [[Lijiang Chen]], [[Shimin Chen]] and [[Peng Jiang]].<br />
<br />
== Overview ==<br />
Systems, methods, and computer-readable and executable instructions are provided for categorizing a concept. Categorizing a concept can include selecting a target concept with a number of surrounding textual contexts. Categorizing a concept can also include determining a number of candidate [[categories]] for the target concept based on the number of surrounding textual contexts. Categorizing a concept can also include selecting a predefined number of articles, each with a desired [[relatedness]] to the number of candidate categories. Furthermore, categorizing a concept can include calculating a relatedness score for each of the number of candidate categories based on a relatedness with the number of articles.</div>Violethttps://wikipediaquality.com/index.php?title=Ontology_Evaluation_Using_Wikipedia_Categories_for_Browsing&diff=22978Ontology Evaluation Using Wikipedia Categories for Browsing2019-12-20T06:20:32Z<p>Violet: + Embed</p>
<hr />
<div>{{Infobox work<br />
| title = Ontology Evaluation Using Wikipedia Categories for Browsing<br />
| date = 2007<br />
| authors = [[Jonathan Yu]]<br />[[James A. Thom]]<br />[[Audrey M. Tam]]<br />
| doi = 10.1145/1321440.1321474<br />
| link = http://dl.acm.org/ft_gateway.cfm?id=1321474&amp;type=pdf<br />
}}<br />
'''Ontology Evaluation Using Wikipedia Categories for Browsing''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Jonathan Yu]], [[James A. Thom]] and [[Audrey M. Tam]].<br />
<br />
== Overview ==<br />
Ontology evaluation is a maturing discipline with methodologies and [[measures]] being developed and proposed. However, evaluation methods that have been proposed have not been applied to specific examples. In this paper, authors present the state-of-the-art in [[ontology]] evaluation - current methodologies, criteria and measures, analyse appropriate evaluations that are important to application - browsing in [[Wikipedia]], and apply these evaluations in the context of ontologies with varied properties. Specifically, authors seek to evaluate ontologies based on [[categories]] found in Wikipedia.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Yu, Jonathan; Thom, James A.; Tam, Audrey M.. (2007). "[[Ontology Evaluation Using Wikipedia Categories for Browsing]]".DOI: 10.1145/1321440.1321474. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Yu |first1=Jonathan |last2=Thom |first2=James A. |last3=Tam |first3=Audrey M. |title=Ontology Evaluation Using Wikipedia Categories for Browsing |date=2007 |doi=10.1145/1321440.1321474 |url=https://wikipediaquality.com/wiki/Ontology_Evaluation_Using_Wikipedia_Categories_for_Browsing}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Yu, Jonathan; Thom, James A.; Tam, Audrey M.. (2007). &amp;quot;<a href="https://wikipediaquality.com/wiki/Ontology_Evaluation_Using_Wikipedia_Categories_for_Browsing">Ontology Evaluation Using Wikipedia Categories for Browsing</a>&amp;quot;.DOI: 10.1145/1321440.1321474. <br />
</nowiki><br />
</code></div>Violethttps://wikipediaquality.com/index.php?title=Comparing_Semantic_Relatedness_Between_Word_Pairs_in_Portuguese_Using_Wikipedia&diff=22977Comparing Semantic Relatedness Between Word Pairs in Portuguese Using Wikipedia2019-12-20T06:18:40Z<p>Violet: + embed code</p>
<hr />
<div>{{Infobox work<br />
| title = Comparing Semantic Relatedness Between Word Pairs in Portuguese Using Wikipedia<br />
| date = 2014<br />
| authors = [[Roger Granada]]<br />[[Cássia Trojahn]]<br />[[Renata Vieira]]<br />
| doi = 10.1007/978-3-319-09761-9_17<br />
| link = https://link.springer.com/content/pdf/10.1007%2F978-3-319-09761-9_17.pdf<br />
}}<br />
'''Comparing Semantic Relatedness Between Word Pairs in Portuguese Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Roger Granada]], [[Cássia Trojahn]] and [[Renata Vieira]].<br />
<br />
== Overview ==<br />
The growth of available data in digital format has been facilitating the development of new models to automatically infer the [[semantic similarity]] between word pairs. However, there are still many natural languages without sufficient resources to evaluate [[measures]] of semantic [[relatedness]]. In this paper authors translated word pairs from a well-known baseline for evaluating semantic relatedness measures into Portuguese and performed a manual evaluation of each pair. Authors compared the correlation with similar datasets in other languages and generated LSA models from [[Wikipedia]] articles in order to verify the pertinence of each dataset and how semantic similarity conveys across languages.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Granada, Roger; Trojahn, Cássia; Vieira, Renata. (2014). "[[Comparing Semantic Relatedness Between Word Pairs in Portuguese Using Wikipedia]]". Springer, Cham. DOI: 10.1007/978-3-319-09761-9_17. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Granada |first1=Roger |last2=Trojahn |first2=Cássia |last3=Vieira |first3=Renata |title=Comparing Semantic Relatedness Between Word Pairs in Portuguese Using Wikipedia |date=2014 |doi=10.1007/978-3-319-09761-9_17 |url=https://wikipediaquality.com/wiki/Comparing_Semantic_Relatedness_Between_Word_Pairs_in_Portuguese_Using_Wikipedia |journal=Springer, Cham}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Granada, Roger; Trojahn, Cássia; Vieira, Renata. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Comparing_Semantic_Relatedness_Between_Word_Pairs_in_Portuguese_Using_Wikipedia">Comparing Semantic Relatedness Between Word Pairs in Portuguese Using Wikipedia</a>&amp;quot;. Springer, Cham. DOI: 10.1007/978-3-319-09761-9_17. <br />
</nowiki><br />
</code></div>Violethttps://wikipediaquality.com/index.php?title=Entity-Relationship_Queries_over_Wikipedia&diff=22976Entity-Relationship Queries over Wikipedia2019-12-20T06:16:39Z<p>Violet: Links</p>
<hr />
<div>'''Entity-Relationship Queries over Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Xiaonan Li]], [[Chengkai Li]] and [[Cong Yu]].<br />
<br />
== Overview ==<br />
Wikipedia is the largest user-generated knowledge base. Authors propose a structured query mechanism, entity-relationship query , for searching entities in [[Wikipedia]] corpus by their properties and inter-relationships. An entity-relationship query consists of arbitrary number of predicates on desired entities. The semantics of each predicate is specified with keywords. Entity-relationship query searches entities directly over text rather than pre-extracted structured data stores. This characteristic brings two benefits: (1) Query semantics can be intuitively expressed by keywords; (2) It avoids information loss that happens during extraction. Authors present a ranking framework for general entity-relationship queries and a position-based Bounded Cumulative Model for accurate ranking of query answers. Experiments on INEX benchmark queries and own crafted queries show the effectiveness and accuracy of ranking method.</div>Violethttps://wikipediaquality.com/index.php?title=Libguides:_Human_Rights_Wikipedia_Edit-A-Thon:_Home&diff=22975Libguides: Human Rights Wikipedia Edit-A-Thon: Home2019-12-20T06:15:05Z<p>Violet: + infobox</p>
<hr />
<div>{{Infobox work<br />
| title = Libguides: Human Rights Wikipedia Edit-A-Thon: Home<br />
| date = 2018<br />
| authors = [[Angela Pratesi]]<br />
| link = https://guides.lib.uni.edu/Wikipedia_Editathon<br />
}}<br />
'''Libguides: Human Rights Wikipedia Edit-A-Thon: Home''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Angela Pratesi]].<br />
<br />
== Overview ==<br />
This guide is to help participants in the Human Rights [[Wikipedia]] Edit-a-thon learn how to edit the free encyclopedia and improve the quality for information on human rights and social justice issues on the free web.</div>Violethttps://wikipediaquality.com/index.php?title=From_Dbpedia_to_Wikipedia:_Filling_the_Gap_by_Discovering_Wikipedia_Conventions&diff=22974From Dbpedia to Wikipedia: Filling the Gap by Discovering Wikipedia Conventions2019-12-20T06:13:22Z<p>Violet: + embed code</p>
<hr />
<div>{{Infobox work<br />
| title = From Dbpedia to Wikipedia: Filling the Gap by Discovering Wikipedia Conventions<br />
| date = 2012<br />
| authors = [[D. F. Torres]]<br />[[Pascal Molli]]<br />[[Hala Skaf-Molli]]<br />[[Alicia Díaz]]<br />
| doi = 10.1109/WI-IAT.2012.227<br />
| link = http://dl.acm.org/citation.cfm?id=2457524.2457642<br />
}}<br />
'''From Dbpedia to Wikipedia: Filling the Gap by Discovering Wikipedia Conventions''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[D. F. Torres]], [[Pascal Molli]], [[Hala Skaf-Molli]] and [[Alicia Díaz]].<br />
<br />
== Overview ==<br />
Many relations existing in [[DBpedia]] are missing in [[Wikipedia]] yielding up an information gap between the semantic web and the social web. Inserting these missing relations requires to automatically discover Wikipedia conventions. From pairs linked by a property p in DBpedia, authors find path queries that link the same pairs in Wikipedia. Authors make the hypothesis that the shortest path query with maximal containment captures the Wikipedia convention for p. Authors computed missing links and conventions for different DBpedia queries. Next, authors inserted some missing links according to computed conventions in Wikipedia and evaluated [[Wikipedians]] feedback. Nearly all contributions has been accepted. In this paper, authors detail the path indexing algorithms, the results of evaluations and give some details about social feedback.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Torres, D. F.; Molli, Pascal; Skaf-Molli, Hala; Díaz, Alicia. (2012). "[[From Dbpedia to Wikipedia: Filling the Gap by Discovering Wikipedia Conventions]]".DOI: 10.1109/WI-IAT.2012.227. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Torres |first1=D. F. |last2=Molli |first2=Pascal |last3=Skaf-Molli |first3=Hala |last4=Díaz |first4=Alicia |title=From Dbpedia to Wikipedia: Filling the Gap by Discovering Wikipedia Conventions |date=2012 |doi=10.1109/WI-IAT.2012.227 |url=https://wikipediaquality.com/wiki/From_Dbpedia_to_Wikipedia:_Filling_the_Gap_by_Discovering_Wikipedia_Conventions}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Torres, D. F.; Molli, Pascal; Skaf-Molli, Hala; Díaz, Alicia. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/From_Dbpedia_to_Wikipedia:_Filling_the_Gap_by_Discovering_Wikipedia_Conventions">From Dbpedia to Wikipedia: Filling the Gap by Discovering Wikipedia Conventions</a>&amp;quot;.DOI: 10.1109/WI-IAT.2012.227. <br />
</nowiki><br />
</code></div>Violethttps://wikipediaquality.com/index.php?title=%E2%80%9CA_Wound_That_Has_Been_Festering_Since_2007%E2%80%9D:_the_Burma/Myanmar_Naming_Controversy_and_the_Problem_of_Rarely_Challenged_Assumptions_on_Wikipedia&diff=22973“A Wound That Has Been Festering Since 2007”: the Burma/Myanmar Naming Controversy and the Problem of Rarely Challenged Assumptions on Wikipedia2019-12-20T06:12:10Z<p>Violet: + links</p>
<hr />
<div>'''“A Wound That Has Been Festering Since 2007”: the Burma/Myanmar Naming Controversy and the Problem of Rarely Challenged Assumptions on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Brendan Luyt]].<br />
<br />
== Overview ==<br />
Purpose</div>Violethttps://wikipediaquality.com/index.php?title=Why_We_Read_Wikipedia&diff=22972Why We Read Wikipedia2019-12-20T06:09:27Z<p>Violet: Links</p>
<hr />
<div>'''Why We Read Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Philipp Singer]], [[Florian Lemmerich]], [[Robert West]], [[Leila Zia]], [[Ellery Wulczyn]], [[Markus Strohmaier]] and [[Jure Leskovec]].<br />
<br />
== Overview ==<br />
Wikipedia is one of the most popular sites on the Web, with millions of users relying on it to satisfy a broad range of information needs every day. Although it is crucial to understand what exactly these needs are in order to be able to meet them, little is currently known about why users visit [[Wikipedia]]. The goal of this paper is to fill this gap by combining a survey of Wikipedia readers with a log-based analysis of user activity. Based on an initial series of user surveys, authors build a taxonomy of Wikipedia use cases along several dimensions, capturing users' motivations to visit Wikipedia, the depth of knowledge they are seeking, and their knowledge of the topic of interest prior to visiting Wikipedia. Then, authors quantify the prevalence of these use cases via a large-scale user survey conducted on live Wikipedia with almost 30,000 responses. Authors analyses highlight the variety of factors driving users to Wikipedia, such as current events, media coverage of a topic, personal curiosity, work or school assignments, or boredom. Finally, authors match survey responses to the respondents' digital traces in Wikipedia's server logs, enabling the discovery of behavioral patterns associated with specific use cases. For instance, authors observe long and fast-paced page sequences across topics for users who are bored or exploring randomly, whereas those using Wikipedia for work or school spend more time on individual articles focused on topics such as science. Authors findings advance understanding of reader motivations and behavior on Wikipedia and can have implications for developers aiming to improve Wikipedia's user experience, editors striving to cater to their readers' needs, third-party services (such as search engines) providing access to Wikipedia content, and researchers aiming to build tools such as recommendation engines.</div>Violethttps://wikipediaquality.com/index.php?title=Collective_Remembering_of_Organizations:_Co-Construction_of_Organizational_Pasts_in_Wikipedia&diff=22971Collective Remembering of Organizations: Co-Construction of Organizational Pasts in Wikipedia2019-12-20T06:05:04Z<p>Violet: infobox</p>
<hr />
<div>{{Infobox work<br />
| title = Collective Remembering of Organizations: Co-Construction of Organizational Pasts in Wikipedia<br />
| date = 2015<br />
| authors = [[Michael Etter]]<br />[[Finn Årup Nielsen]]<br />
| doi = 10.1108/CCIJ-09-2014-0059<br />
| link = http://www.emeraldinsight.com/doi/abs/10.1108/CCIJ-09-2014-0059<br />
}}<br />
'''Collective Remembering of Organizations: Co-Construction of Organizational Pasts in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Michael Etter]] and [[Finn Årup Nielsen]].<br />
<br />
== Overview ==<br />
Purpose – How organizations’ pasts are presented to the public is crucial, because this presentation shapes corporate [[reputation]]s. Increasingly, various actors contribute to the public remembering of organizations with new information and communication technologies (ICTs). The purpose of this paper is to investigate the online encyclopedia [[Wikipedia]] as a global memory place, where the pasts of organizations are communicatively co-constructed by actors of a loosely connected community. Design/methodology/approach – The authors analyze 1,459 edits of Wikipedia pages of ten organizations from various industries. Quantitative content analysis detects Wikipedia edits for their reputational relevance and reference to formal sources, such as corporate communication or newspapers. Furthermore, the authors investigate to which degree current corporate communication in form of 177 press releases has an influence on the remembering process in Wikipedia. Findings – The analysis shows how the continuous construction o...</div>Violet