https://wikipediaquality.com/api.php?action=feedcontributions&user=Evelyn&feedformat=atomWikipedia Quality - User contributions [en]2024-03-29T12:32:48ZUser contributionsMediaWiki 1.30.0https://wikipediaquality.com/index.php?title=Writing_for_Wikipedia:_Co-Constructing_Knowledge_and_Writing_for_a_Public_Audience&diff=25739Writing for Wikipedia: Co-Constructing Knowledge and Writing for a Public Audience2020-10-28T20:00:23Z<p>Evelyn: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Writing for Wikipedia: Co-Constructing Knowledge and Writing for a Public Audience<br />
| date = 2013<br />
| authors = [[Lori L. Britt]]<br />
| doi = 10.1016/B978-1-84334-694-4.50001-6<br />
| link = http://www.sciencedirect.com/science/article/pii/B9781843346944500016<br />
}}<br />
'''Writing for Wikipedia: Co-Constructing Knowledge and Writing for a Public Audience''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Lori L. Britt]].<br />
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== Overview ==<br />
Abstract: This assignment allows students to research topics in depth and become skilled at communicating academic knowledge for a public audience. The assignment draws attention to the collaborative construction of knowledge and the forces that shape what counts as knowledge and what gets disseminated. It also encourages students to consider how to organize information to be useful and illuminating to others, and how to consider connections between topics and concepts. The assignment engages students in critique, as they are more willing to critique and revise their writing when that writing will be accessible to the public. The assignment also exposes students to a social media information-sharing medium, [[Wikipedia]], and encourages their critical consideration of the strengths and limitations of this online encyclopedic resource.<br />
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Britt, Lori L.. (2013). "[[Writing for Wikipedia: Co-Constructing Knowledge and Writing for a Public Audience]]". Chandos Publishing. DOI: 10.1016/B978-1-84334-694-4.50001-6. <br />
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{{cite journal |last1=Britt |first1=Lori L. |title=Writing for Wikipedia: Co-Constructing Knowledge and Writing for a Public Audience |date=2013 |doi=10.1016/B978-1-84334-694-4.50001-6 |url=https://wikipediaquality.com/wiki/Writing_for_Wikipedia:_Co-Constructing_Knowledge_and_Writing_for_a_Public_Audience |journal=Chandos Publishing}}<br />
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Britt, Lori L.. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Writing_for_Wikipedia:_Co-Constructing_Knowledge_and_Writing_for_a_Public_Audience">Writing for Wikipedia: Co-Constructing Knowledge and Writing for a Public Audience</a>&amp;quot;. Chandos Publishing. DOI: 10.1016/B978-1-84334-694-4.50001-6. <br />
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</code></div>Evelynhttps://wikipediaquality.com/index.php?title=Semantic_Similarity_Computing_Method_based_on_Wikipedia&diff=25738Semantic Similarity Computing Method based on Wikipedia2020-10-28T19:58:28Z<p>Evelyn: + embed code</p>
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<div>{{Infobox work<br />
| title = Semantic Similarity Computing Method based on Wikipedia<br />
| date = 2011<br />
| authors = [[]]<br />
| link = http://en.cnki.com.cn/Article_en/CJFDTOTAL-JSJC201107067.htm<br />
}}<br />
'''Semantic Similarity Computing Method based on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[]].<br />
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== Overview ==<br />
Aiming at the low accuracy and poor intelligibility of current algorithms for semantic analysis,a [[semantic similarity]] computing method based on [[Wikipedia]] is proposed.Different from computing word's semantic similarity by category information,this method uses link information to calculate the similarity of different words in a way like human thinking.Result can be easily understood and the accuracy rate can be increased with semantic category.Experiment compared with current algorithms proves its advantage.<br />
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. (2011). "[[Semantic Similarity Computing Method based on Wikipedia]]".<br />
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{{cite journal |last1= |title=Semantic Similarity Computing Method based on Wikipedia |date=2011 |url=https://wikipediaquality.com/wiki/Semantic_Similarity_Computing_Method_based_on_Wikipedia}}<br />
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. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Semantic_Similarity_Computing_Method_based_on_Wikipedia">Semantic Similarity Computing Method based on Wikipedia</a>&amp;quot;.<br />
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</code></div>Evelynhttps://wikipediaquality.com/index.php?title=Enriching_Multilingual_Language_Resources_by_Discovering_Missing_Cross-Language_Links_in_Wikipedia&diff=25737Enriching Multilingual Language Resources by Discovering Missing Cross-Language Links in Wikipedia2020-10-28T19:57:15Z<p>Evelyn: + wikilinks</p>
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<div>'''Enriching Multilingual Language Resources by Discovering Missing Cross-Language Links in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Jong-Hoon Oh]], [[Daisuke Kawahara]], [[Kiyotaka Uchimoto]], [[Jun’ichi Kazama]] and [[Kentaro Torisawa]].<br />
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== Overview ==<br />
Authors present a novel method for discovering missing cross-language links between English and Japanese [[Wikipedia]] articles. Authors collect candidates of missing cross-language links -- a pair of English and Japanese Wikipedia articles, which could be connected by cross-language links. Then authors select the correct cross-language links among the candidates by using a classifier trained with various types of [[features]]. Authors method has three desirable characteristics for discovering missing links. First, method can discover cross-language links with high accuracy (92% precision with 78% recall rates). Second, the features used in a classifier are language-independent. Third, without relying on any external knowledge, authors generate the features based on resources automatically obtained from Wikipedia. In this work, authors discover approximately $10^5$ missing cross-language links from Wikipedia, which are almost two-thirds as many as the existing cross-language links in Wikipedia.</div>Evelynhttps://wikipediaquality.com/index.php?title=Wikipedia%27s_Economic_Value&diff=25736Wikipedia's Economic Value2020-10-28T19:54:48Z<p>Evelyn: cat.</p>
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<div>{{Infobox work<br />
| title = Wikipedia's Economic Value<br />
| date = 2013<br />
| authors = [[Jonathan Band]]<br />[[Jonathan Gerafi]]<br />
| doi = 10.2139/ssrn.2338563<br />
| link = https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2338563_code1754101.pdf?abstractid=2338563&amp;mirid=4<br />
}}<br />
'''Wikipedia's Economic Value''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Jonathan Band]] and [[Jonathan Gerafi]].<br />
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== Overview ==<br />
In the copyright policy debate, proponents of strong copyright protection tend to be dismissive of the quality of freely available content. In response to counter-examples such as open access scholarly publications and advertising-supported business models (e.g., newspaper websites and the over-the-air television broadcasts viewed by 50 million Americans), the strong copyright proponents center their attack on amateur content. In this narrative, YouTube is for cat videos and [[Wikipedia]] is a wildly unreliable source of information.Recent studies, however, indicate that the volunteer-written and -edited Wikipedia is no less reliable than professionally edited encyclopedias such as the Encyclopedia Britannica. Moreover, Wikipedia has far broader coverage. Britannica, which discontinued its print edition in 2012 and now appears only online, contains 120,000 articles, all in English. Wikipedia, by contrast, has 4.3 million articles in English and a total of 22 million articles in 285 languages. Wikipedia attracts more than 470 million unique visitors a month who view over 19 billion pages. According to Alexa, it is the sixth most visited website in the world.Wikipedia, therefore, is a shining example of valuable content created by non-professionals. Is there a way to measure the economic value of this content? Because Wikipedia is created by volunteers, is administered by a non-profit foundation, and is distributed for free, the normal means of measuring value — such as revenue, market capitalization, and book value — do not directly apply. Nonetheless, there are a variety of methods for estimating its value in terms of its market value, its replacement cost, and the value it creates for its users. These methods suggest a valuation in the tens of billions of dollars, a one-time replacement cost of $6.6 billion with an annual updating cost of $630 million, and consumer benefit in the hundreds of billions of dollars.<br />
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Band, Jonathan; Gerafi, Jonathan. (2013). "[[Wikipedia's Economic Value]]".DOI: 10.2139/ssrn.2338563. <br />
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Band, Jonathan; Gerafi, Jonathan. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia's_Economic_Value">Wikipedia's Economic Value</a>&amp;quot;.DOI: 10.2139/ssrn.2338563. <br />
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[[Category:English Wikipedia]]</div>Evelynhttps://wikipediaquality.com/index.php?title=Topic_Modeling_for_Wikipedia_Link_Disambiguation&diff=25735Topic Modeling for Wikipedia Link Disambiguation2020-10-28T19:52:53Z<p>Evelyn: + categories</p>
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<div>{{Infobox work<br />
| title = Topic Modeling for Wikipedia Link Disambiguation<br />
| date = 2014<br />
| authors = [[Bradley Skaggs]]<br />[[Lise Getoor]]<br />
| doi = 10.1145/2633044<br />
| link = http://dl.acm.org/ft_gateway.cfm?id=2633044&amp;type=pdf<br />
}}<br />
'''Topic Modeling for Wikipedia Link Disambiguation''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Bradley Skaggs]] and [[Lise Getoor]].<br />
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== Overview ==<br />
Many articles in the online encyclopedia [[Wikipedia]] have hyperlinks to ambiguous article titles; these ambiguous links should be replaced with links to unambiguous articles, a process known as disambiguation. Authors propose a novel statistical topic model based on link text, which authors refer to as the Link Text Topic Model (LTTM), that authors use to suggest new link targets for ambiguous links. To evaluate model, authors describe a method for extracting ground truth for this link disambiguation task from edits made to Wikipedia in a specific time period. Authors use this ground truth to demonstrate the superiority of LTTM over other existing link- and content-based approaches to disambiguating links in Wikipedia. Finally, authors build a web service that uses LTTM to make suggestions to human editors wanting to fix ambiguous links in Wikipedia.<br />
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{{cite journal |last1=Skaggs |first1=Bradley |last2=Getoor |first2=Lise |title=Topic Modeling for Wikipedia Link Disambiguation |date=2014 |doi=10.1145/2633044 |url=https://wikipediaquality.com/wiki/Topic_Modeling_for_Wikipedia_Link_Disambiguation}}<br />
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Skaggs, Bradley; Getoor, Lise. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Topic_Modeling_for_Wikipedia_Link_Disambiguation">Topic Modeling for Wikipedia Link Disambiguation</a>&amp;quot;.DOI: 10.1145/2633044. <br />
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[[Category:Scientific works]]</div>Evelynhttps://wikipediaquality.com/index.php?title=Trust_of_High_School_Students_in_Wikipedia&diff=25734Trust of High School Students in Wikipedia2020-10-28T19:51:44Z<p>Evelyn: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Trust of High School Students in Wikipedia<br />
| date = 2010<br />
| authors = [[A. Bremer]]<br />
| link = http://essay.utwente.nl/59861/1/BSc_A_Bremer.pdf<br />
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'''Trust of High School Students in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[A. Bremer]].<br />
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== Overview ==<br />
Wikipedia is an open encyclopedia: Nearly everyone can edit most of its content. Having been a key element for [[Wikipedia]]’s striking success, the open character poses a challenge to its users: How can they form an opinion about whether to trust the information? Lucassen and Schraagen (2010) have made an approach to define the criteria that are<br />
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{{cite journal |last1=Bremer |first1=A. |title=Trust of High School Students in Wikipedia |date=2010 |url=https://wikipediaquality.com/wiki/Trust_of_High_School_Students_in_Wikipedia}}<br />
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</code></div>Evelynhttps://wikipediaquality.com/index.php?title=Wikipedia_%E2%80%93_Amat%C3%B8renes_Inntog&diff=25733Wikipedia – Amatørenes Inntog2020-10-28T19:50:10Z<p>Evelyn: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Wikipedia – Amatørenes Inntog<br />
| date = 2007<br />
| authors = [[Chris Nyborg]]<br />
| link = https://tidsskrift.dk/index.php/lexn/article/view/18537<br />
}}<br />
'''Wikipedia – Amatørenes Inntog''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Chris Nyborg]].<br />
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== Overview ==<br />
Wikipedia is the entry of amateurs into the world of encyclopaedias. Volunteers with no background in lexicography are building a massive project from scratch. The article, based on a presentation given in January 2007, outlines the mechanisms and processes involved in [[Wikipedia]]’s explosive growth. By trial and error, the contributors work out the project’s rules as they go along. Many [[Wikipedians]] become interested in exploring the theory and practice of creating an encyclopaedia, combining traditional lexicographical concepts with Wikipedia’s focus on freedom of information and the ability of anyone to contribute.<br />
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{{cite journal |last1=Nyborg |first1=Chris |title=Wikipedia – Amatørenes Inntog |date=2007 |url=https://wikipediaquality.com/wiki/Wikipedia_–_Amatørenes_Inntog}}<br />
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Nyborg, Chris. (2007). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia_–_Amatørenes_Inntog">Wikipedia – Amatørenes Inntog</a>&amp;quot;.<br />
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<div>{{Infobox work<br />
| title = Learning by Comparing with Wikipedia: the Value to Students' Learning<br />
| date = 2014<br />
| authors = [[Antoni Meseguer-Artola]]<br />
| doi = 10.7238/rusc.v11i2.2042<br />
| link = https://link.springer.com/content/pdf/10.7238%2Frusc.v11i2.2042.pdf<br />
}}<br />
'''Learning by Comparing with Wikipedia: the Value to Students' Learning''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Antoni Meseguer-Artola]].<br />
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== Overview ==<br />
The main purpose of this research work is to describe and evaluate a learning technique that actively uses [[Wikipedia]] in an online master’s degree course in Statistics. It is based on the comparison between Wikipedia content and standard academic learning materials. Authors define this technique as ‘learning by comparing’. In order to evaluate the performance of this learning technique, data from different academic semesters was collected. Through different hypothesis tests, the academic performance of the students following a learning-by-comparing strategy is compared with the case where Wikipedia is not used. Additionally, during the course the students are asked about the [[reliability]], currentness, [[completeness]] and usefulness of Wikipedia, as rated on a 5-point Likert scale. This data is used to analyse the perceived quality of Wikipedia, for each statistical concept of the course, and to discover its relationship with academic performance. To that end, descriptive statistics, dependence tests, and contrasts of means have been performed.</div>Evelynhttps://wikipediaquality.com/index.php?title=Robust_Systems_for_Preposition_Error_Correction_Using_Wikipedia_Revisions&diff=22428Robust Systems for Preposition Error Correction Using Wikipedia Revisions2019-11-28T04:35:36Z<p>Evelyn: + wikilinks</p>
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<div>'''Robust Systems for Preposition Error Correction Using Wikipedia Revisions''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Aoife Cahill]], [[Nitin Madnani]], [[Joel R. Tetreault]] and [[Diane Napolitano]].<br />
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== Overview ==<br />
Authors show that existing methods for training preposition error correction systems, whether using well-edited text or error-annotated corpora, do not generalize across very different test sets. Authors present a new, large errorannotated corpus and use it to train systems that generalize across three different test sets, each from a different domain and with different error characteristics. This new corpus is automatically extracted from [[Wikipedia]] revisions and contains over one million instances of preposition corrections.</div>Evelynhttps://wikipediaquality.com/index.php?title=Readability_of_Wikipedia_Pages_on_Autoimmune_Disorders:_Systematic_Quantitative_Assessment&diff=22427Readability of Wikipedia Pages on Autoimmune Disorders: Systematic Quantitative Assessment2019-11-28T04:34:22Z<p>Evelyn: Links</p>
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<div>'''Readability of Wikipedia Pages on Autoimmune Disorders: Systematic Quantitative Assessment''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Abdulla Watad]].<br />
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== Overview ==<br />
Background: In the era of new information and communication technologies, the Internet is being increasingly accessed for health-related information. Indeed, recently published patient surveys of people with autoimmune disorders confirmed that the Internet was reported as one of the most important health information sources. [[Wikipedia]], a free online encyclopedia launched in 2001, is generally one of the most visited websites worldwide and is often consulted for health-related information. Objective: The main objective of this investigation was to quantitatively assess whether the Wikipedia pages related to autoimmune disorders can be easily accessed by patients and their families, in terms of [[readability]]. Methods: Authors obtained and downloaded a list of autoimmune disorders from the American Autoimmune Related Diseases Association (AARDA) website. Authors analyzed Wikipedia articles for their overall level of readability with 6 different quantitative readability scales: (1) the Flesch Reading Ease, (2) the Gunning Fog Index, (3) the Coleman-Liau Index, (4) the Flesch-Kincaid Grade Level, (5) the Automated Readability Index (ARI), and (6) the Simple Measure of Gobbledygook (SMOG). Further, authors investigated the correlation between readability and clinical, pathological, and epidemiological parameters. Moreover, each Wikipedia analysis was assessed according to its content, breaking down the readability indices by main topic of each part (namely, pathogenesis, treatment, diagnosis, and prognosis plus a section containing paragraphs not falling into any of the previous [[categories]]). Results: Authors retrieved 134 diseases from the AARDA website. The Flesch Reading Ease yielded a mean score of 24.34 (SD 10.73), indicating that the sites were very difficult to read and best understood by university graduates, while mean Gunning Fog Index and ARI scores were 16.87 (SD 2.03) and 14.06 (SD 2.12), respectively. The Coleman-Liau Index and the Flesch-Kincaid Grade Level yielded mean scores of 14.48 (SD 1.57) and 14.86 (1.95), respectively, while the mean SMOG score was 15.38 (SD 1.37). All the readability indices confirmed that the sites were suitable for a university graduate reading level. Authors found no correlation between readability and clinical, pathological, and epidemiological parameters. Differences among the different sections of the Wikipedia pages were statistically significant. Conclusions: Wikipedia pages related to autoimmune disorders are characterized by a low level of readability. The onus is, therefore, on physicians and health authorities to improve the health literacy skills of patients and their families and to create, together with patients themselves, disease-specific readable sites, disseminating highly accessible health-related online information, in terms of both clarity and conciseness. [J Med Internet Res 2017;19(7):e260]</div>Evelynhttps://wikipediaquality.com/index.php?title=Italian_Wikipedia_and_Epilepsy:_an_Infodemiological_Study_of_Online_Information-Seeking_Behavior&diff=22426Italian Wikipedia and Epilepsy: an Infodemiological Study of Online Information-Seeking Behavior2019-11-28T04:32:48Z<p>Evelyn: Embed</p>
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<div>{{Infobox work<br />
| title = Italian Wikipedia and Epilepsy: an Infodemiological Study of Online Information-Seeking Behavior<br />
| date = 2018<br />
| authors = [[Francesco Brigo]]<br />[[Simona Lattanzi]]<br />[[Giorgia Giussani]]<br />[[Laura Tassi]]<br />[[Nicola Pietrafusa]]<br />[[Carlo Andrea Galimberti]]<br />[[Raffaele Nardone]]<br />[[Nicola Luigi Bragazzi]]<br />[[Oriano Mecarelli]]<br />
| doi = 10.1016/j.yebeh.2018.01.037<br />
| link = https://www.sciencedirect.com/science/article/pii/S1525505018300702<br />
}}<br />
'''Italian Wikipedia and Epilepsy: an Infodemiological Study of Online Information-Seeking Behavior''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Francesco Brigo]], [[Simona Lattanzi]], [[Giorgia Giussani]], [[Laura Tassi]], [[Nicola Pietrafusa]], [[Carlo Andrea Galimberti]], [[Raffaele Nardone]], [[Nicola Luigi Bragazzi]] and [[Oriano Mecarelli]].<br />
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== Overview ==<br />
Abstract [[Wikipedia]] is the most commonly accessed source of health information by both healthcare professionals and the lay public worldwide. Authors aimed to evaluate information-seeking behavior of Internet users searching the Italian Wikipedia for articles related to epilepsy and its treatment. Using Pageviews Analysis, authors assessed the total and mean monthly views of articles from the Italian Wikipedia devoted to epilepsy, epileptic syndromes, seizure type, and antiepileptic drugs (AEDs) from January 1, 2015 to October 31, 2017. Authors compared the views of the article on epilepsy with those of articles focusing on Alzheimer's disease, migraine, multiple sclerosis, syncope, and stroke and adjusted all results for crude disease prevalence. With the only exception of the article on multiple sclerosis, the adjusted views for the Italian Wikipedia article on epilepsy were higher than those for the other neurological disorders. The most viewed articles on seizure type were devoted to tonic–clonic seizure, typical absence seizure, tonic convulsive seizures, and clonic convulsive seizures. The most frequently accessed articles on epilepsy syndromes were about temporal lobe epilepsy and Lennox–Gastaut syndrome. The most frequently viewed articles on AEDs were devoted to valproic acid, carbamazepine, and levetiracetam. Wikipedia searches seem to mirror patients' fears and worries about epilepsy more than its actual epidemiology. The ultimate reasons for searching online remain unknown. Epileptologists and epilepsy scientific societies should make greater efforts to work jointly with Wikipedia to convey more accurate and up-to-date information about epilepsy.<br />
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Brigo, Francesco; Lattanzi, Simona; Giussani, Giorgia; Tassi, Laura; Pietrafusa, Nicola; Galimberti, Carlo Andrea; Nardone, Raffaele; Bragazzi, Nicola Luigi; Mecarelli, Oriano. (2018). "[[Italian Wikipedia and Epilepsy: an Infodemiological Study of Online Information-Seeking Behavior]]". Academic Press Inc.. DOI: 10.1016/j.yebeh.2018.01.037. <br />
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{{cite journal |last1=Brigo |first1=Francesco |last2=Lattanzi |first2=Simona |last3=Giussani |first3=Giorgia |last4=Tassi |first4=Laura |last5=Pietrafusa |first5=Nicola |last6=Galimberti |first6=Carlo Andrea |last7=Nardone |first7=Raffaele |last8=Bragazzi |first8=Nicola Luigi |last9=Mecarelli |first9=Oriano |title=Italian Wikipedia and Epilepsy: an Infodemiological Study of Online Information-Seeking Behavior |date=2018 |doi=10.1016/j.yebeh.2018.01.037 |url=https://wikipediaquality.com/wiki/Italian_Wikipedia_and_Epilepsy:_an_Infodemiological_Study_of_Online_Information-Seeking_Behavior |journal=Academic Press Inc.}}<br />
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Brigo, Francesco; Lattanzi, Simona; Giussani, Giorgia; Tassi, Laura; Pietrafusa, Nicola; Galimberti, Carlo Andrea; Nardone, Raffaele; Bragazzi, Nicola Luigi; Mecarelli, Oriano. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/Italian_Wikipedia_and_Epilepsy:_an_Infodemiological_Study_of_Online_Information-Seeking_Behavior">Italian Wikipedia and Epilepsy: an Infodemiological Study of Online Information-Seeking Behavior</a>&amp;quot;. Academic Press Inc.. DOI: 10.1016/j.yebeh.2018.01.037. <br />
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<div>{{Infobox work<br />
| title = Why We’Re Editing Women Scientists Onto Wikipedia<br />
| date = 2018<br />
| authors = [[Jess Wade]]<br />[[Maryam Zaringhalam]]<br />
| doi = 10.1038/d41586-018-05947-8<br />
| link = https://www.nature.com/articles/d41586-018-05947-8?sf196191903=1<br />
}}<br />
'''Why We’Re Editing Women Scientists Onto Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Jess Wade]] and [[Maryam Zaringhalam]].<br />
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== Overview ==<br />
And here's why you should, too, say Jess Wade and Maryam Zaringhalam. And here's why you should, too, say Jess Wade and Maryam Zaringhalam.<br />
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Wade, Jess; Zaringhalam, Maryam. (2018). "[[Why We’Re Editing Women Scientists Onto Wikipedia]]". Nature Publishing Group. DOI: 10.1038/d41586-018-05947-8. <br />
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=== English Wikipedia ===<br />
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{{cite journal |last1=Wade |first1=Jess |last2=Zaringhalam |first2=Maryam |title=Why We’Re Editing Women Scientists Onto Wikipedia |date=2018 |doi=10.1038/d41586-018-05947-8 |url=https://wikipediaquality.com/wiki/Why_We’Re_Editing_Women_Scientists_Onto_Wikipedia |journal=Nature Publishing Group}}<br />
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Wade, Jess; Zaringhalam, Maryam. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/Why_We’Re_Editing_Women_Scientists_Onto_Wikipedia">Why We’Re Editing Women Scientists Onto Wikipedia</a>&amp;quot;. Nature Publishing Group. DOI: 10.1038/d41586-018-05947-8. <br />
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</code></div>Evelynhttps://wikipediaquality.com/index.php?title=Japanese_Sentence_Compression_Using_Simple_English_Wikipedia&diff=22424Japanese Sentence Compression Using Simple English Wikipedia2019-11-28T04:28:18Z<p>Evelyn: Embed</p>
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<div>{{Infobox work<br />
| title = Japanese Sentence Compression Using Simple English Wikipedia<br />
| date = 2015<br />
| authors = [[Shunsuke Takeno]]<br />[[Kazuhide Yamamoto]]<br />
| doi = 10.1109/IALP.2015.7451533<br />
| link = http://ieeexplore.ieee.org/iel7/7445295/7451516/07451533.pdf<br />
}}<br />
'''Japanese Sentence Compression Using Simple English Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Shunsuke Takeno]] and [[Kazuhide Yamamoto]].<br />
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== Overview ==<br />
Authors describe a [[cross-lingual]] approach for sentence compression of articles of Japanese [[Wikipedia]] using the correspondence of articles of Simple [[English Wikipedia]]. Taking advantages of the nature of the corpus, authors can find essential parts from encyclopedic description without highly depending on the statistical information which are noisy. Authors manually explored the correspondences between the articles of Japanese Wikipedia and those of Simple English Wikipedia and then proposed a cross-lingual alignment method using simple matching algorithm. Authors provide an analysis of the abovementioned correspondence and the preliminary result of sentence compression using Simple English Wikipedia.<br />
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Takeno, Shunsuke; Yamamoto, Kazuhide. (2015). "[[Japanese Sentence Compression Using Simple English Wikipedia]]".DOI: 10.1109/IALP.2015.7451533. <br />
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{{cite journal |last1=Takeno |first1=Shunsuke |last2=Yamamoto |first2=Kazuhide |title=Japanese Sentence Compression Using Simple English Wikipedia |date=2015 |doi=10.1109/IALP.2015.7451533 |url=https://wikipediaquality.com/wiki/Japanese_Sentence_Compression_Using_Simple_English_Wikipedia}}<br />
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Takeno, Shunsuke; Yamamoto, Kazuhide. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Japanese_Sentence_Compression_Using_Simple_English_Wikipedia">Japanese Sentence Compression Using Simple English Wikipedia</a>&amp;quot;.DOI: 10.1109/IALP.2015.7451533. <br />
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</code></div>Evelynhttps://wikipediaquality.com/index.php?title=Semantic_Relationship_Discovery_with_Wikipedia_Structure&diff=22423Semantic Relationship Discovery with Wikipedia Structure2019-11-28T04:26:24Z<p>Evelyn: Adding embed</p>
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<div>{{Infobox work<br />
| title = Semantic Relationship Discovery with Wikipedia Structure<br />
| date = 2011<br />
| authors = [[Fan Bu]]<br />[[Yu Hao]]<br />[[Xiaoyan Zhu]]<br />
| doi = 10.5591/978-1-57735-516-8/IJCAI11-297<br />
| link = https://dl.acm.org/citation.cfm?id=2283699<br />
| plink = https://www.researchgate.net/profile/Xiaoyan_Zhu10/publication/220814101_Semantic_Relationship_Discovery_with_Wikipedia_Structure/links/5450623a0cf249aa53da943f.pdf<br />
}}<br />
'''Semantic Relationship Discovery with Wikipedia Structure''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Fan Bu]], [[Yu Hao]] and [[Xiaoyan Zhu]].<br />
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== Overview ==<br />
Thanks to the idea of social collaboration, [[Wikipedia]] has accumulated vast amount of semi-structured knowledge in which the link structure reflects human's cognition on semantic relationship to some extent. In this paper, authors proposed a novel method RCRank to jointly compute concept-concept [[relatedness]] and concept-category relatedness base on the assumption that information carried in concept-concept links and concept-category links can mutually reinforce each other. Different from previous work, RCRank can not only find semantically related concepts but also interpret their relations by [[categories]]. Experimental results on concept recommendation and relation interpretation show that method substantially outperforms classical methods.<br />
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Bu, Fan; Hao, Yu; Zhu, Xiaoyan. (2011). "[[Semantic Relationship Discovery with Wikipedia Structure]]". International Joint Conference on Artificial Intelligence (IJCAI). DOI: 10.5591/978-1-57735-516-8/IJCAI11-297. <br />
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{{cite journal |last1=Bu |first1=Fan |last2=Hao |first2=Yu |last3=Zhu |first3=Xiaoyan |title=Semantic Relationship Discovery with Wikipedia Structure |date=2011 |doi=10.5591/978-1-57735-516-8/IJCAI11-297 |url=https://wikipediaquality.com/wiki/Semantic_Relationship_Discovery_with_Wikipedia_Structure |journal=International Joint Conference on Artificial Intelligence (IJCAI)}}<br />
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Bu, Fan; Hao, Yu; Zhu, Xiaoyan. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Semantic_Relationship_Discovery_with_Wikipedia_Structure">Semantic Relationship Discovery with Wikipedia Structure</a>&amp;quot;. International Joint Conference on Artificial Intelligence (IJCAI). DOI: 10.5591/978-1-57735-516-8/IJCAI11-297. <br />
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</code></div>Evelynhttps://wikipediaquality.com/index.php?title=Early_Response_to_False_Claims_in_Wikipedia&diff=22422Early Response to False Claims in Wikipedia2019-11-28T04:25:02Z<p>Evelyn: Infobox</p>
<hr />
<div>{{Infobox work<br />
| title = Early Response to False Claims in Wikipedia<br />
| date = 2008<br />
| authors = [[P. D. Magnus]]<br />
| doi = 10.5210/fm.v13i9.2115<br />
| link = http://firstmonday.org/ojs/index.php/fm/article/view/2115/2027<br />
}}<br />
'''Early Response to False Claims in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[P. D. Magnus]].<br />
<br />
== Overview ==<br />
A number of studies have assessed the [[reliability]] of entries in the [[Wikipedia]] at specific times. One important difference between the Wikipedia and traditional media, however, is the dynamic nature of its entries. An entry assessed today might be substantially extended or reworked tomorrow. This study paper assesses the frequency with which small, inaccurate changes are quickly corrected.</div>Evelynhttps://wikipediaquality.com/index.php?title=Wikipedia_Driven_Autonomous_Label_Assignment_in_Wrapper_Induced_Tables_with_Missing_Column_Names&diff=22421Wikipedia Driven Autonomous Label Assignment in Wrapper Induced Tables with Missing Column Names2019-11-28T04:22:52Z<p>Evelyn: wikilinks</p>
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<div>'''Wikipedia Driven Autonomous Label Assignment in Wrapper Induced Tables with Missing Column Names''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Mohammad Shafkat Amin]], [[Anupam Bhattacharjee]] and [[Hasan M. Jamil]].<br />
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== Overview ==<br />
As the volume of information available on the internet is growing exponentially, it is clear that most of this information will have to be processed and digested by computers to produce useful information for human consumption. Unfortunately, most web contents are currently designed for direct human consumption in which it is assumed that a human will decipher the information presented to him in some context and will be able to connect the missing dots, if any. In particular, information presented in some tabular form often does not accompany descriptive titles or column names similar to attribute names in tables. While such omissions are not really an issue for humans, it is truly hard to extract information in autonomous systems in which a machine is expected to understand the meaning of the table presented and extract the right information in the context of the query. It is even more difficult when the information needed is distributed across the globe and involve semantic heterogeneity. In this paper, goal is to address the issue of how to interpret tables with missing column names by developing a method for the assignment of attributes names in an arbitrary table extracted from the web in a fully autonomous manner. Authors propose a novel approach by leveraging [[Wikipedia]] for the first time for column name discovery for the purpose of table annotation. Authors show that this leads to an improved likelihood of capturing the context and interpretation of the table accurately and producing a semantically meaningful query response.</div>Evelynhttps://wikipediaquality.com/index.php?title=Contropedia_-_the_Analysis_and_Visualization_of_Controversies_in_Wikipedia_Articles&diff=22420Contropedia - the Analysis and Visualization of Controversies in Wikipedia Articles2019-11-28T04:20:48Z<p>Evelyn: Adding infobox</p>
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<div>{{Infobox work<br />
| title = Contropedia - the Analysis and Visualization of Controversies in Wikipedia Articles<br />
| date = 2014<br />
| authors = [[Erik Borra]]<br />[[Esther Weltevrede]]<br />[[Paolo Ciuccarelli]]<br />[[Andreas Kaltenbrunner]]<br />[[David Laniado]]<br />[[Giovanni Magni]]<br />[[Michele Mauri]]<br />[[Richard Rogers]]<br />[[Tommaso Venturini]]<br />
| doi = 10.1145/2641580.2641622<br />
| link = https://dl.acm.org/citation.cfm?id=2641622<br />
| plink = https://www.researchgate.net/profile/Tommaso_Venturini/publication/278030058_Contropedia_-_the_analysis_and_visualization_of_controversies_in_Wikipedia_articles/links/557dc48808ae26eada8db814.pdf<br />
}}<br />
'''Contropedia - the Analysis and Visualization of Controversies in Wikipedia Articles''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Erik Borra]], [[Esther Weltevrede]], [[Paolo Ciuccarelli]], [[Andreas Kaltenbrunner]], [[David Laniado]], [[Giovanni Magni]], [[Michele Mauri]], [[Richard Rogers]] and [[Tommaso Venturini]].<br />
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== Overview ==<br />
Collaborative content creation inevitably reaches situations where different points of view lead to conflict. In [[Wikipedia]], one of the most prominent examples of collaboration online, conflict is mediated by both policy and software, and conflicts often reflect larger societal debates. Contropedia is a platform for the analysis and visualization of such controversies in Wikipedia. Controversy metrics are extracted from activity streams generated by edits to, and discussions about, individual articles and groups of related articles. An article's revision history and its corresponding discussion pages constitute two parallel streams of user interactions that, taken together, fully describe the process of the collaborative creation of an article. Authors proposed platform, Contropedia, builds on state of the art techniques and extends current metrics for the analysis of both edit and discussion activity and visualizes these both as a layer on top of Wikipedia articles as well as a dashboard view presenting additional analytics. Furthermore, the combination of these two approaches allows for a deeper understanding of the substance, composition, actor alignment, trajectory and liveliness of controversies on Wikipedia. Authors research aims to provide a better understanding of socio-technical phenomena that take place on the web and to equip citizens with tools to fully deploy the complexity of controversies. Contropedia is useful for the general public as well as user groups with specific interests such as scientists, students, data journalists, decision makers and media communicators. Contropedia can be found at http://contropedia.net.</div>Evelynhttps://wikipediaquality.com/index.php?title=Students%27_Digital_Strategies_and_Shortcuts_-_Searching_for_Answers_on_Wikipedia_as_a_Core_Literacy_Practice_in_Upper_Secondary_School&diff=22419Students' Digital Strategies and Shortcuts - Searching for Answers on Wikipedia as a Core Literacy Practice in Upper Secondary School2019-11-28T04:18:01Z<p>Evelyn: infobox</p>
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<div>{{Infobox work<br />
| title = Students' Digital Strategies and Shortcuts - Searching for Answers on Wikipedia as a Core Literacy Practice in Upper Secondary School<br />
| date = 2013<br />
| authors = [[Marte Blikstad-Balas]]<br />[[Rita Hvistendahl]]<br />
| link = https://www.idunn.no/file/pdf/61221168/students_digital_strategies_and_shortcuts.pdf<br />
}}<br />
'''Students' Digital Strategies and Shortcuts - Searching for Answers on Wikipedia as a Core Literacy Practice in Upper Secondary School''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Marte Blikstad-Balas]] and [[Rita Hvistendahl]].<br />
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== Overview ==<br />
When the classroom is connected to the Internet, the number of possible sources of information is almost infinite. Nevertheless, students tend to systematically favor the online encyclopedia [[Wikipedia]] as a source for knowledge. The present study combines quantitative and qualitative data to investigate the role Wikipedia plays in the literacy practices of students working on school tasks. It also discusses how different tasks lead to different strategies.</div>Evelynhttps://wikipediaquality.com/index.php?title=Arabic_Named_Entity_Recognition_Process_Using_Transducer_Cascade_and_Arabic_Wikipedia&diff=22418Arabic Named Entity Recognition Process Using Transducer Cascade and Arabic Wikipedia2019-11-28T04:15:11Z<p>Evelyn: Basic information on Arabic Named Entity Recognition Process Using Transducer Cascade and Arabic Wikipedia</p>
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<div>'''Arabic Named Entity Recognition Process Using Transducer Cascade and Arabic Wikipedia''' - scientific work related to Wikipedia quality published in 2015, written by Fatma Ben Mesmia, Kais Haddar, Denis Maurel and Nathalie Friburger.<br />
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== Overview ==<br />
Transducers namely transducer cascades are used in several NLP-applications such as Arabic named entity recognition (ANER). To experiment and evaluate an ANER process, a weight coverage corpus is necessary. In this paper, authors propose an ANER method based on transducer cascade. The proposed transducer cascade is generated with the CasSys tool integrated in Unitex linguistic platform. The experimentation of method is done on a Wikipedia corpus. The Wikipedia text format is obtained with Kiwix tool. The experiment results are satisfactory based on calculated measures.</div>Evelynhttps://wikipediaquality.com/index.php?title=Latent_Groups_in_Online_Communities:_a_Longitudinal_Study_in_Wikipedia&diff=22417Latent Groups in Online Communities: a Longitudinal Study in Wikipedia2019-11-28T04:13:04Z<p>Evelyn: Basic information on Latent Groups in Online Communities: a Longitudinal Study in Wikipedia</p>
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<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 />
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== 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>Evelynhttps://wikipediaquality.com/index.php?title=Wikipedia_and_Medicine:_Quantifying_Readership,_Editors,_and_the_Significance_of_Natural_Language&diff=22416Wikipedia and Medicine: Quantifying Readership, Editors, and the Significance of Natural Language2019-11-28T04:11:49Z<p>Evelyn: Adding categories</p>
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<div>{{Infobox work<br />
| title = Wikipedia and Medicine: Quantifying Readership, Editors, and the Significance of Natural Language<br />
| date = 2015<br />
| authors = [[James Heilman]]<br />[[Andrew West]]<br />
| doi = 10.2196/jmir.4069<br />
| link = http://www.jmir.org/article/viewFile/jmir_v17i3e62/2<br />
}}<br />
'''Wikipedia and Medicine: Quantifying Readership, Editors, and the Significance of Natural Language''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[James Heilman]] and [[Andrew West]].<br />
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== Overview ==<br />
Background: [[Wikipedia]] is a collaboratively edited encyclopedia. One of the most popular websites on the Internet, it is known to be a frequently used source of health care information by both professionals and the lay public. Objective: This paper quantifies the production and consumption of Wikipedia’s medical content along 4 dimensions. First, authors measured the amount of medical content in both articles and bytes and, second, the citations that supported that content. Third, authors analyzed the medical readership against that of other health care websites between Wikipedia’s natural language editions and its relationship with disease prevalence. Fourth, authors surveyed the quantity/characteristics of Wikipedia’s medical contributors, including year-over-year participation trends and editor demographics. Methods: Using a well-defined categorization infrastructure, authors identified medically pertinent English-language Wikipedia articles and links to their foreign language equivalents. With these, Wikipedia can be queried to produce metadata and full texts for entire article histories. Wikipedia also makes available hourly reports that aggregate reader traffic at per-article granularity. An online survey was used to determine the background of contributors. Standard mining and visualization techniques (eg, aggregation queries, cumulative distribution functions, and/or correlation metrics) were applied to each of these datasets. Analysis focused on year-end 2013, but historical data permitted some longitudinal analysis. Results: Wikipedia’s medical content (at the end of 2013) was made up of more than 155,000 articles and 1 billion bytes of text across more than 255 languages. This content was supported by more than 950,000 references. Content was viewed more than 4.88 billion times in 2013. This makes it one of if not the most viewed medical resource(s) globally. The core editor community numbered less than 300 and declined over the past 5 years. The members of this community were half health care providers and 85.5% (100/117) had a university education. Conclusions: Although Wikipedia has a considerable volume of [[multilingual]] medical content that is extensively read and well-referenced, the core group of editors that contribute and maintain that content is small and shrinking in size. [J Med Internet Res 2015;17(3):e62]<br />
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Heilman, James; West, Andrew. (2015). "[[Wikipedia and Medicine: Quantifying Readership, Editors, and the Significance of Natural Language]]". JMIR Publications Inc.. DOI: 10.2196/jmir.4069. <br />
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{{cite journal |last1=Heilman |first1=James |last2=West |first2=Andrew |title=Wikipedia and Medicine: Quantifying Readership, Editors, and the Significance of Natural Language |date=2015 |doi=10.2196/jmir.4069 |url=https://wikipediaquality.com/wiki/Wikipedia_and_Medicine:_Quantifying_Readership,_Editors,_and_the_Significance_of_Natural_Language |journal=JMIR Publications Inc.}}<br />
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Heilman, James; West, Andrew. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia_and_Medicine:_Quantifying_Readership,_Editors,_and_the_Significance_of_Natural_Language">Wikipedia and Medicine: Quantifying Readership, Editors, and the Significance of Natural Language</a>&amp;quot;. JMIR Publications Inc.. DOI: 10.2196/jmir.4069. <br />
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[[Category:English Wikipedia]]</div>Evelynhttps://wikipediaquality.com/index.php?title=The_Dynamics_of_Wikipedia_Article_Revisions:_an_Analysis_of_Revision_Activities_and_Patterns&diff=22415The Dynamics of Wikipedia Article Revisions: an Analysis of Revision Activities and Patterns2019-11-28T04:07:08Z<p>Evelyn: cats.</p>
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<div>{{Infobox work<br />
| title = The Dynamics of Wikipedia Article Revisions: an Analysis of Revision Activities and Patterns<br />
| date = 2017<br />
| authors = [[Zhongming Ma]]<br />[[Jie Tao]]<br />[[Jing Hu]]<br />
| doi = 10.1504/IJDMMM.2017.10009454<br />
| link = https://www.inderscienceonline.com/doi/abs/10.1504/IJDMMM.2017.088415<br />
}}<br />
'''The Dynamics of Wikipedia Article Revisions: an Analysis of Revision Activities and Patterns''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Zhongming Ma]], [[Jie Tao]] and [[Jing Hu]].<br />
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== Overview ==<br />
To study the dynamics of revision activities of [[Wikipedia]] articles, authors define 14 revision actions, annotate 6,950 revisions from 20 articles in four quality ranks (C, B, GA, and FA), and analyse revisions and revision actions in ten consecutive time periods. Authors identify four revision patterns: 1) revision actions at the sentence and link levels appear in similar paces; 2) the numbers of revision actions at sentence and link levels comparatively evenly grow with the article's age prior to the last time period; 3) the paces of media and reference-level actions tend to be lagged behind sentence and link-level actions; 4) before being promoted to the GA or FA rank, articles nominated to the GA or FA rank exhibit a significant rising pattern in amounts of revisions and revision actions. This pattern is validated with a larger set of 533 articles.<br />
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Ma, Zhongming; Tao, Jie; Hu, Jing. (2017). "[[The Dynamics of Wikipedia Article Revisions: an Analysis of Revision Activities and Patterns]]". Inderscience Publishers (IEL). DOI: 10.1504/IJDMMM.2017.10009454. <br />
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{{cite journal |last1=Ma |first1=Zhongming |last2=Tao |first2=Jie |last3=Hu |first3=Jing |title=The Dynamics of Wikipedia Article Revisions: an Analysis of Revision Activities and Patterns |date=2017 |doi=10.1504/IJDMMM.2017.10009454 |url=https://wikipediaquality.com/wiki/The_Dynamics_of_Wikipedia_Article_Revisions:_an_Analysis_of_Revision_Activities_and_Patterns |journal=Inderscience Publishers (IEL)}}<br />
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Ma, Zhongming; Tao, Jie; Hu, Jing. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/The_Dynamics_of_Wikipedia_Article_Revisions:_an_Analysis_of_Revision_Activities_and_Patterns">The Dynamics of Wikipedia Article Revisions: an Analysis of Revision Activities and Patterns</a>&amp;quot;. Inderscience Publishers (IEL). DOI: 10.1504/IJDMMM.2017.10009454. <br />
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[[Category:Scientific works]]</div>Evelynhttps://wikipediaquality.com/index.php?title=An_Approach_for_Using_Wikipedia_to_Measure_the_Flow_of_Trends_Across_Countries&diff=22414An Approach for Using Wikipedia to Measure the Flow of Trends Across Countries2019-11-28T04:05:23Z<p>Evelyn: cats.</p>
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<div>{{Infobox work<br />
| title = An Approach for Using Wikipedia to Measure the Flow of Trends Across Countries<br />
| date = 2013<br />
| authors = [[Ramine Tinati]]<br />[[Thanassis Tiropanis]]<br />[[Leslie Carr]]<br />
| doi = 10.1145/2487788.2488177<br />
| link = https://dl.acm.org/citation.cfm?id=2488177<br />
}}<br />
'''An Approach for Using Wikipedia to Measure the Flow of Trends Across Countries''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Ramine Tinati]], [[Thanassis Tiropanis]] and [[Leslie Carr]].<br />
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== Overview ==<br />
Wikipedia has grown to become the most successful online encyclopedia on the Web, containing over 24 million articles, offered in over 240 languages. In just over 10 years [[Wikipedia]] has transformed from being just an encyclopedia of knowledge, to a wealth of facts and information, from articles discussing trivia, political issues, geographies and demographics, to popular culture, news articles, and social events. In this paper authors explore the use of Wikipedia for identifying the flow of information and trends across the world. Authors start with the hypothesis that, given that Wikipedia is a resource that is globally available in [[different language]]s across countries, access to its articles could be a reflection human activity. To explore this hypothesis authors try to establish metrics on the use of Wikipedia in order to identify potential trends and to establish whether or how those trends flow from one county to another. Authors subsequently compare the outcome of this analysis to that of more established methods that are based on online social media or traditional media. Authors explore this hypothesis by applying approach to a subset of Wikipedia articles and also a specific worldwide social phenomenon that occurred during 2012; authors investigate whether access to relevant Wikipedia articles correlates to the viral success of the South Korean pop song, "Gangnam Style" and the associated artist "PSY" as evidenced by traditional and online social media. Authors analysis demonstrates that Wikipedia can indeed provide a useful measure for detecting social trends and events, and in the case that authors studied; it could have been possible to identify the specific trend quicker in comparison to other established trend identification services such as [[Google]] Trends.<br />
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Tinati, Ramine; Tiropanis, Thanassis; Carr, Leslie. (2013). "[[An Approach for Using Wikipedia to Measure the Flow of Trends Across Countries]]".DOI: 10.1145/2487788.2488177. <br />
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{{cite journal |last1=Tinati |first1=Ramine |last2=Tiropanis |first2=Thanassis |last3=Carr |first3=Leslie |title=An Approach for Using Wikipedia to Measure the Flow of Trends Across Countries |date=2013 |doi=10.1145/2487788.2488177 |url=https://wikipediaquality.com/wiki/An_Approach_for_Using_Wikipedia_to_Measure_the_Flow_of_Trends_Across_Countries}}<br />
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Tinati, Ramine; Tiropanis, Thanassis; Carr, Leslie. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/An_Approach_for_Using_Wikipedia_to_Measure_the_Flow_of_Trends_Across_Countries">An Approach for Using Wikipedia to Measure the Flow of Trends Across Countries</a>&amp;quot;.DOI: 10.1145/2487788.2488177. <br />
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[[Category:Scientific works]]<br />
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[[Category:Gan Wikipedia]]</div>Evelynhttps://wikipediaquality.com/index.php?title=An_Inside_View:_Credibility_in_Wikipedia_from_the_Perspective_of_Editors&diff=22413An Inside View: Credibility in Wikipedia from the Perspective of Editors2019-11-28T04:03:06Z<p>Evelyn: + Embed</p>
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<div>{{Infobox work<br />
| title = An Inside View: Credibility in Wikipedia from the Perspective of Editors<br />
| date = 2010<br />
| authors = [[Helena Francke]]<br />[[Olof Sundin]]<br />
| link = http://www.diva-portal.org/smash/record.jsf?pid=diva2:887070<br />
}}<br />
'''An Inside View: Credibility in Wikipedia from the Perspective of Editors''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Helena Francke]] and [[Olof Sundin]].<br />
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== Overview ==<br />
Introduction. The question of [[credibility]] in participatory information environments, particularly [[Wikipedia]], has been much debated. This paper investigates how editors on Swedish Wikipedia consider credibility when they edit and read Wikipedia articles. Method. The study builds on interviews with 11 editors on Swedish Wikipedia, supported by a document analysis of policies on Swedish Wikipedia. Analysis. The interview transcripts have been coded qualitatively according to the participants' use of Wikipedia and what they take into consideration in making credibility assessments. Results. The participants use Wikipedia for purposes where it is not vital that the information is correct. Their credibility assessments are mainly based on authorship, verifiability, and the editing history of an article. Conclusions. The situations and purposes for which the editors use Wikipedia are similar to other user groups, but they draw on their knowledge as members of the network of practice of wikipedians to make credibility assessments, including knowledge of certain editors and of the [[MediaWiki]] architecture. Their assessments have more similarities to those used in traditional media than to assessments springing from the wisdom of crowds.<br />
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Francke, Helena; Sundin, Olof. (2010). "[[An Inside View: Credibility in Wikipedia from the Perspective of Editors]]". Professor Tom Wilson. <br />
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{{cite journal |last1=Francke |first1=Helena |last2=Sundin |first2=Olof |title=An Inside View: Credibility in Wikipedia from the Perspective of Editors |date=2010 |url=https://wikipediaquality.com/wiki/An_Inside_View:_Credibility_in_Wikipedia_from_the_Perspective_of_Editors |journal=Professor Tom Wilson}}<br />
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Francke, Helena; Sundin, Olof. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/An_Inside_View:_Credibility_in_Wikipedia_from_the_Perspective_of_Editors">An Inside View: Credibility in Wikipedia from the Perspective of Editors</a>&amp;quot;. Professor Tom Wilson. <br />
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</code></div>Evelynhttps://wikipediaquality.com/index.php?title=A_Bookmark_Recommender_System_based_on_Social_Bookmarking_Services_and_Wikipedia_Categories&diff=22412A Bookmark Recommender System based on Social Bookmarking Services and Wikipedia Categories2019-11-28T03:58:33Z<p>Evelyn: Adding wikilinks</p>
<hr />
<div>'''A Bookmark Recommender System based on Social Bookmarking Services and Wikipedia Categories''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Takumi Yoshida]] and [[Ushio Inoue]].<br />
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== Overview ==<br />
Social book marking services allow users to add bookmarks of web pages with freely chosen keywords as tags. Personalized recommender systems recommend new and useful bookmarks added by other users. Authors propose a new method to find similar users and to select relevant bookmarks in a social book marking service. Authors method is lightweight, because it uses a small set of important tags for each user to find useful bookmarks to recommend. Authors method is also powerful, because it employs the [[Wikipedia]] category database to deal with the diversity of tags among users. The evaluation using the Hatena bookmark service in Japan shows that method significantly increases the number of relevant bookmarks recommended without notable increase of irrelevant bookmarks.</div>Evelynhttps://wikipediaquality.com/index.php?title=Domain-Specific_Semantic_Relatedness_from_Wikipedia:_Can_a_Course_Be_Transferred%3F&diff=22411Domain-Specific Semantic Relatedness from Wikipedia: Can a Course Be Transferred?2019-11-28T03:56:05Z<p>Evelyn: cat.</p>
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<div>{{Infobox work<br />
| title = Domain-Specific Semantic Relatedness from Wikipedia: Can a Course Be Transferred?<br />
| date = 2012<br />
| authors = [[Beibei Yang]]<br />[[Jesse M. Heines]]<br />
| link = http://dl.acm.org/ft_gateway.cfm?id=2385744&amp;type=pdf<br />
}}<br />
'''Domain-Specific Semantic Relatedness from Wikipedia: Can a Course Be Transferred?''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Beibei Yang]] and [[Jesse M. Heines]].<br />
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== Overview ==<br />
Semantic [[relatedness]], or its inverse, semantic distance, [[measures]] the degree of closeness between two pieces of text determined by their meaning. Related work typically measures semantics based on a sparse knowledge base such as [[WordNet]] or CYC that requires intensive manual efforts to build and maintain. Other work is based on the Brown corpus, or more recently, [[Wikipedia]]. Wikipedia-based measures, however, typically do not take into account the rapid growth of that resource, which exponentially increases the time to prepare and query the knowledge base. Furthermore, the generalized knowledge domain may be difficult to adapt to a specific domain. To address these problems, this paper proposes a domain-specific semantic relatedness measure based on part of Wikipedia that analyzes course descriptions to suggest whether a course can be transferred from one institution to another. Authors show that results perform well when compared to previous work.<br />
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Yang, Beibei; Heines, Jesse M.. (2012). "[[Domain-Specific Semantic Relatedness from Wikipedia: Can a Course Be Transferred?]]". Association for Computational Linguistics. <br />
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{{cite journal |last1=Yang |first1=Beibei |last2=Heines |first2=Jesse M. |title=Domain-Specific Semantic Relatedness from Wikipedia: Can a Course Be Transferred? |date=2012 |url=https://wikipediaquality.com/wiki/Domain-Specific_Semantic_Relatedness_from_Wikipedia:_Can_a_Course_Be_Transferred? |journal=Association for Computational Linguistics}}<br />
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Yang, Beibei; Heines, Jesse M.. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Domain-Specific_Semantic_Relatedness_from_Wikipedia:_Can_a_Course_Be_Transferred?">Domain-Specific Semantic Relatedness from Wikipedia: Can a Course Be Transferred?</a>&amp;quot;. Association for Computational Linguistics. <br />
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[[Category:Scientific works]]</div>Evelynhttps://wikipediaquality.com/index.php?title=Nonhuman_Language_Agents_in_Online_Collaborative_Communities:_Comparing_Hebrew_Wikipedia_and_Facebook_Translations&diff=22410Nonhuman Language Agents in Online Collaborative Communities: Comparing Hebrew Wikipedia and Facebook Translations2019-11-28T03:54:51Z<p>Evelyn: Adding infobox</p>
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<div>{{Infobox work<br />
| title = Nonhuman Language Agents in Online Collaborative Communities: Comparing Hebrew Wikipedia and Facebook Translations<br />
| date = 2018<br />
| authors = [[Carmel L. Vaisman]]<br />[[Illan Gonen]]<br />[[Yuval Pinter]]<br />
| doi = 10.1016/j.dcm.2017.10.002<br />
| link = http://www.sciencedirect.com/science/article/pii/S2211695817301848<br />
}}<br />
'''Nonhuman Language Agents in Online Collaborative Communities: Comparing Hebrew Wikipedia and Facebook Translations''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Carmel L. Vaisman]], [[Illan Gonen]] and [[Yuval Pinter]].<br />
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== Overview ==<br />
Abstract Online mass collaborations act as unregulated superdiverse language spaces, however, grassroots policing may impose uniformity and reproduce hegemony. This study compared language policies in Hebrew [[Wikipedia]] and the Hebrew [[Facebook]] translation app. Hebrew Wikipedia designed a strict linguistic guide that promotes a neutral Hebrew register, rejecting both colloquial and high registers, enforced by an algorithm post factum. The Hebrew Facebook translators' community maintained a decentralized approach, lacking the affordances for hierarchies of expertise, focusing on the practicality of the language and the speed of project completion. The comparative design within the same speech community stressed the role of affordances as nonhuman language agents in the social process of language policy.</div>Evelynhttps://wikipediaquality.com/index.php?title=A_Generic_Method_for_Multi_Word_Extraction_from_Wikipedia&diff=22409A Generic Method for Multi Word Extraction from Wikipedia2019-11-28T03:52:17Z<p>Evelyn: Infobox</p>
<hr />
<div>{{Infobox work<br />
| title = A Generic Method for Multi Word Extraction from Wikipedia<br />
| date = 2008<br />
| authors = [[Bozo Bekavac]]<br />[[Marko Tadić]]<br />
| doi = 10.1109/ITI.2008.4588490<br />
| link = http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&amp;arnumber=4588490<br />
}}<br />
'''A Generic Method for Multi Word Extraction from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Bozo Bekavac]] and [[Marko Tadić]].<br />
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== Overview ==<br />
This paper presents the generic method for multiword expression extraction from [[Wikipedia]]. The method is using the properties of this specific encyclopedic genre in its HTML format and it relies on the intention of the authors of articles to link to other articles. The relevant links were processed by applying local regular grammars within the NooJ development environment. Authors tested the method on a Croatian version of Wikipedia and authors present the results obtained.</div>Evelynhttps://wikipediaquality.com/index.php?title=Short_Text_Classification_based_on_Wikipedia_and_Word2Vec&diff=22408Short Text Classification based on Wikipedia and Word2Vec2019-11-28T03:50:18Z<p>Evelyn: + wikilinks</p>
<hr />
<div>'''Short Text Classification based on Wikipedia and Word2Vec''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Liu Wensen]], [[Cao Zewen]], [[Wang Jun]] and [[Wang Xiao-yi]].<br />
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== Overview ==<br />
Different from long texts, the [[features]] of Chinese short texts is much sparse, which is the primary cause of the low accuracy in the classification of short texts by using traditional classification methods. In this paper, a novel method was proposed to tackle the problem by expanding the features of short text based on [[Wikipedia]] and Word2vec. Firstly, build the semantic relevant concept sets of Wikipedia. Authors get the articles that have high relevancy with Wikipedia concepts and use the word2vec tools to measure the semantic [[relatedness]] between target concepts and related concepts. And then authors use the relevant concept sets to extend the short texts. Compared to traditional similarity measurement between concepts using statistical method, this method can get more accurate semantic relatedness. The experimental results show that by expanding the features of short texts, the classification accuracy can be improved. Specifically, method appeared to be more effective.</div>Evelynhttps://wikipediaquality.com/index.php?title=Wikipedia_and_the_Two-Faced_Professoriate&diff=22407Wikipedia and the Two-Faced Professoriate2019-11-28T03:48:02Z<p>Evelyn: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Wikipedia and the Two-Faced Professoriate<br />
| date = 2010<br />
| authors = [[Patricia L. Dooley]]<br />
| doi = 10.1145/1832772.1832803<br />
| link = http://dl.acm.org/citation.cfm?id=1832803<br />
}}<br />
'''Wikipedia and the Two-Faced Professoriate''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Patricia L. Dooley]].<br />
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== Overview ==<br />
A primary responsibility of university teachers is to guide their students in the process of using only the most accurate research resources in their completion of assignments. Thus, it is not surprising to hear that faculty routinely coach their students to use [[Wikipedia]] carefully. Even more pronounced anti-Wikipedia backlashes have developed on some campuses, leading faculty to forbid their students to use the popular on-line compendium of information. Within this context, but directing the spotlight away from students, this pilot study uses survey and content analysis research methods to explore how faculty at U.S. universities and colleges regard Wikipedia's [[credibility]] as an information source, as well as how they use Wikipedia in their academic work. The results of the survey reveal that while none of the university faculty who completed it regard Wikipedia as an extremely credible source of information, more than half stated it has moderate to high credibility, and many use it in both their teaching and research. The results of the content analysis component of the study demonstrates that academic researchers from across the disciplines are citing Wikipedia as a source of scholarly information in their peer-reviewed research reports. Although the study's research findings are not generalizable, they are surprising considering the professoriate's oft-stated lack of trust in Wikipedia.<br />
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Dooley, Patricia L.. (2010). "[[Wikipedia and the Two-Faced Professoriate]]".DOI: 10.1145/1832772.1832803. <br />
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{{cite journal |last1=Dooley |first1=Patricia L. |title=Wikipedia and the Two-Faced Professoriate |date=2010 |doi=10.1145/1832772.1832803 |url=https://wikipediaquality.com/wiki/Wikipedia_and_the_Two-Faced_Professoriate}}<br />
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Dooley, Patricia L.. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia_and_the_Two-Faced_Professoriate">Wikipedia and the Two-Faced Professoriate</a>&amp;quot;.DOI: 10.1145/1832772.1832803. <br />
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</code></div>Evelynhttps://wikipediaquality.com/index.php?title=Wikitology:_a_Novel_Hybrid_Knowledge_Base_Derived_from_Wikipedia&diff=22406Wikitology: a Novel Hybrid Knowledge Base Derived from Wikipedia2019-11-28T03:46:25Z<p>Evelyn: + links</p>
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<div>'''Wikitology: a Novel Hybrid Knowledge Base Derived from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Tim Finin]] and [[Zareen Syed]].<br />
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== Overview ==<br />
World knowledge may be available in different forms such as relational databases, triple stores, link graphs, meta-data and free text. Human minds are capable of understanding and reasoning over knowledge represented in different ways and are influenced by different social, contextual and environmental factors. By following a similar model, authors have integrated a variety of knowledge sources in a novel way to produce a single hybrid knowledge base i.e., Wikitology, enabling applications to better access and exploit knowledge hidden in different forms.</div>Evelynhttps://wikipediaquality.com/index.php?title=Exploiting_Wikipedia_for_Cross-Lingual_and_Multilingual_Information_Retrieval&diff=22405Exploiting Wikipedia for Cross-Lingual and Multilingual Information Retrieval2019-11-28T03:43:47Z<p>Evelyn: Infobox work</p>
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<div>{{Infobox work<br />
| title = Exploiting Wikipedia for Cross-Lingual and Multilingual Information Retrieval<br />
| date = 2012<br />
| authors = [[Philipp Sorg]]<br />[[Philipp Cimiano]]<br />
| doi = 10.1016/j.datak.2012.02.003<br />
| link = http://www.sciencedirect.com/science/article/pii/S0169023X12000213<br />
}}<br />
'''Exploiting Wikipedia for Cross-Lingual and Multilingual Information Retrieval''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Philipp Sorg]] and [[Philipp Cimiano]].<br />
<br />
== Overview ==<br />
In this article authors show how [[Wikipedia]] as a [[multilingual]] knowledge resource can be exploited for Cross-Language and Multilingual Information Retrieval (CLIR/MLIR). Authors describe an approach authors call Cross-Language Explicit Semantic Analysis (CL-ESA) which indexes documents with respect to explicit interlingual concepts. These concepts are considered as interlingual and universal and in case correspond either to Wikipedia articles or [[categories]]. Each concept is associated to a text signature in each language which can be used to estimate language-specific term distributions for each concept. This knowledge can then be used to calculate the strength of association between a term and a concept which is used to map documents into the concept space. With CL-ESA authors are thus moving from a Bag-Of-Words model to a Bag-Of-Concepts model that allows language-independent document representations in the vector space spanned by interlingual and universal concepts. Authors show how different vector-based retrieval models and term weighting strategies can be used in conjunction with CL-ESA and experimentally analyze the performance of the different choices. Authors evaluate the approach on a mate retrieval task on two datasets: JRC-Acquis and Multext. Authors show that in the MLIR settings, CL-ESA benefits from a certain level of abstraction in the sense that using categories instead of articles as in the original ESA model delivers better results.</div>Evelynhttps://wikipediaquality.com/index.php?title=Mining_and_Ranking_Biomedical_Synonym_Candidates_from_Wikipedia&diff=22404Mining and Ranking Biomedical Synonym Candidates from Wikipedia2019-11-28T03:41:53Z<p>Evelyn: Mining and Ranking Biomedical Synonym Candidates from Wikipedia -- new article</p>
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<div>'''Mining and Ranking Biomedical Synonym Candidates from Wikipedia''' - scientific work related to Wikipedia quality published in 2015, written by Abhyuday N Jagannatha, Jinying Chen and Hong Yu.<br />
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== Overview ==<br />
Biomedical synonyms are important resources for Natural Language Processing in Biomedical domain. Existing synonym resources (e.g., the UMLS) are not complete. Manual efforts for expanding and enriching these resources are prohibitively expensive. Authors therefore develop and evaluate approaches for automated synonym extraction from Wikipedia. Using the inter-wiki links, authors extracted the candidate synonyms (anchor-text e.g., “increased thirst”) in a Wikipedia page and the title (e.g., “polyuria”) of its corresponding linked page. Authors rank synonym candidates with word embedding and pseudo-relevance feedback (PRF). Authors results show that PRF-based reranking outperformed word embedding based approach and a strong baseline using interwiki link frequency. A hybrid method, Rank Score Combination, achieved the best results. Authors analysis also suggests that medical synonyms mined from Wikipedia can increase the coverage of existing synonym resources such as UMLS.</div>Evelynhttps://wikipediaquality.com/index.php?title=Poisson_Statistics_of_Pagerank_Probabilities_of_Twitter_and_Wikipedia_Networks&diff=22403Poisson Statistics of Pagerank Probabilities of Twitter and Wikipedia Networks2019-11-28T03:40:04Z<p>Evelyn: + Infobox work</p>
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<div>{{Infobox work<br />
| title = Poisson Statistics of Pagerank Probabilities of Twitter and Wikipedia Networks<br />
| date = 2014<br />
| authors = [[Klaus M. Frahm]]<br />[[Dima L. Shepelyansky]]<br />
| doi = 10.1140/epjb/e2014-50123-4<br />
| link = https://link.springer.com/content/pdf/10.1140%2Fepjb%2Fe2014-50123-4.pdf<br />
| plink = https://arxiv.org/abs/1402.5839<br />
}}<br />
'''Poisson Statistics of Pagerank Probabilities of Twitter and Wikipedia Networks''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Klaus M. Frahm]] and [[Dima L. Shepelyansky]].<br />
<br />
== Overview ==<br />
Authors use the methods of quantum chaos and Random Matrix Theory for analysis of statistical fluctuations of PageRank probabilities in directed networks. In this approach the effective energy levels are given by a logarithm of PageRank probability at a given node. After the standard energy level unfolding procedure authors establish that the nearest spacing distribution of PageRank probabilities is described by the Poisson law typical for integrable quantum systems. Authors studies are done for the [[Twitter]] network and three networks of [[Wikipedia]] editions in English, French and German. Authors argue that due to absence of level repulsion the PageRank order of nearby nodes can be easily interchanged. The obtained Poisson law implies that the nearby PageRank probabilities fluctuate as random independent variables.</div>Evelynhttps://wikipediaquality.com/index.php?title=Wikikreator:_Improving_Wikipedia_Stubs_Automatically&diff=21750Wikikreator: Improving Wikipedia Stubs Automatically2019-11-02T10:16:14Z<p>Evelyn: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Wikikreator: Improving Wikipedia Stubs Automatically<br />
| date = 2015<br />
| authors = [[Siddhartha Banerjee]]<br />[[Prasenjit Mitra]]<br />
| doi = 10.3115/v1/P15-1084<br />
| link = http://aclweb.org/anthology/P15-1084<br />
}}<br />
'''Wikikreator: Improving Wikipedia Stubs Automatically''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Siddhartha Banerjee]] and [[Prasenjit Mitra]].<br />
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== Overview ==<br />
Stubs on [[Wikipedia]] often lack comprehensive information. The huge cost of editing Wikipedia and the presence of only a limited number of active contributors curb the consistent growth of Wikipedia. In this work, authors present WikiKreator, a system that is capable of generating content automatically to improve existing stubs on Wikipedia. The system has two components. First, a text classifier built using topic distribution vectors is used to assign content from the web to various sections on a Wikipedia article. Second, authors propose a novel abstractive summarization technique based on an optimization framework that generates section-specific summaries for Wikipedia stubs. Experiments show that WikiKreator is capable of generating well-formed informative content. Further, automatically generated content from system have been appended to Wikipedia stubs and the content has been retained successfully proving the effectiveness of approach.<br />
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Banerjee, Siddhartha; Mitra, Prasenjit. (2015). "[[Wikikreator: Improving Wikipedia Stubs Automatically]]". Association for Computational Linguistics (ACL). DOI: 10.3115/v1/P15-1084. <br />
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{{cite journal |last1=Banerjee |first1=Siddhartha |last2=Mitra |first2=Prasenjit |title=Wikikreator: Improving Wikipedia Stubs Automatically |date=2015 |doi=10.3115/v1/P15-1084 |url=https://wikipediaquality.com/wiki/Wikikreator:_Improving_Wikipedia_Stubs_Automatically |journal=Association for Computational Linguistics (ACL)}}<br />
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Banerjee, Siddhartha; Mitra, Prasenjit. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikikreator:_Improving_Wikipedia_Stubs_Automatically">Wikikreator: Improving Wikipedia Stubs Automatically</a>&amp;quot;. Association for Computational Linguistics (ACL). DOI: 10.3115/v1/P15-1084. <br />
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</code></div>Evelynhttps://wikipediaquality.com/index.php?title=Visualizing_the_Overlap_Between_the_100_Most_Visited_Pages_on_Wikipedia_for_September_2006_to_January_2007&diff=21749Visualizing the Overlap Between the 100 Most Visited Pages on Wikipedia for September 2006 to January 20072019-11-02T10:15:11Z<p>Evelyn: Links</p>
<hr />
<div>'''Visualizing the Overlap Between the 100 Most Visited Pages on Wikipedia for September 2006 to January 2007''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Anselm Spoerri]].<br />
<br />
== Overview ==<br />
This paper compares the monthly lists of the 100 most visited [[Wikipedia]] pages for the period of September 2006 to January 2007. searchCrystal is used to visualize the overlap between the five monthly Top 100 lists to show which pages are highly visited in all five months; which pages in four of the five months and so on. It is shown that almost 40 percent of a month’s top 100 pages are visited in all five months, whereas 25 percent are highly visited only in a single month. The presented visualizations make it possible to gain quick insights into the overlap and topical relationships between the monthly lists.</div>Evelynhttps://wikipediaquality.com/index.php?title=Connecting_Wikipedia_and_the_Archive&diff=21748Connecting Wikipedia and the Archive2019-11-02T10:12:40Z<p>Evelyn: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Connecting Wikipedia and the Archive<br />
| date = 2017<br />
| authors = [[Ann Matsuuchi]]<br />
| link = http://wikistudies.org/index.php?journal=wikistudies&amp;page=article&amp;op=view&amp;path%5B%5D=2<br />
}}<br />
'''Connecting Wikipedia and the Archive''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Ann Matsuuchi]].<br />
<br />
== Overview ==<br />
In this essay, Author illustrate a particular instance of how the construction of knowledge can be democratized in a way that simultaneously preserves shared ideas of trust and [[reliability]], via effective collaborations of students, scholars and archivists. The described project that was started in 2015, was collaboratively designed by archivists and historians with the La Guardia & Wagner Archives (“the Archives”) and LaGuardia Community College’s faculty and librarians, and involves beginning college students in the production of a needed public history of the outbreak and impact of HIV/AIDS in New York City. This works demonstrates how community college students can become junior scholars working with primary source archival collections in a manner similar to researchers working on projects as part of institutional fellowships. Utilization of a [[Wikipedia]] as a non-commercial, public, open access information source also succeeds in raising web traffic, visibility and accessibility for unique and valuable archival collections. Collaborations across disciplines and departments and partnerships between people can allow for libraries and archives to take on new roles as new conductors of the inclusive creation of public histories.<br />
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{{cite journal |last1=Matsuuchi |first1=Ann |title=Connecting Wikipedia and the Archive |date=2017 |url=https://wikipediaquality.com/wiki/Connecting_Wikipedia_and_the_Archive}}<br />
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Matsuuchi, Ann. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Connecting_Wikipedia_and_the_Archive">Connecting Wikipedia and the Archive</a>&amp;quot;.<br />
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</code></div>Evelynhttps://wikipediaquality.com/index.php?title=Constructing_a_Chinese%C3%A2%E2%82%AC%E2%80%A2Japanese_Parallel_Corpus_from_Wikipedia&diff=21747Constructing a Chinese―Japanese Parallel Corpus from Wikipedia2019-11-02T10:10:33Z<p>Evelyn: cats.</p>
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<div>{{Infobox work<br />
| title = Constructing a Chinese―Japanese Parallel Corpus from Wikipedia<br />
| date = 2014<br />
| authors = [[Chenhui Chu]]<br />[[Toshiaki Nakazawa]]<br />[[Sadao Kurohashi]]<br />
| link = http://www.lrec-conf.org/proceedings/lrec2014/pdf/21_Paper.pdf<br />
}}<br />
'''Constructing a Chinese―Japanese Parallel Corpus from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Chenhui Chu]], [[Toshiaki Nakazawa]] and [[Sadao Kurohashi]].<br />
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== Overview ==<br />
Graduate School of Informatics, Kyoto University Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan E-mail: {chu, nakazawa}@nlp.ist.i.kyoto-u.ac.jp, kuro@i.kyoto-u.ac.jp Abstract Parallel corpora are crucial for statistical [[machine translation]] (SMT). However, they are quite scarce for most language pairs, such as Chinese–Japanese. As comparable corpora are far more available, many studies have been conducted to automatically construct parallel corpora from comparable corpora. This paper presents a robust parallel sentence extraction system for constructing a Chinese–Japanese parallel corpus from [[Wikipedia]]. The system is inspired by previous studies that mainly consist of a parallel sentence candidate filter and a binary classifier for parallel sentence identification. Authors improve the system by using the common Chinese characters for filtering and two novel feature sets for classification. Experiments show that system performs significantly better than the previous studies for both accuracy in parallel sentence extraction and SMT performance. Using the system, authors construct a Chinese–Japanese parallel corpus with more than 126k highly accurate parallel sentences from Wikipedia. The constructed parallel corpus is freely available at http://orchid.kuee.kyoto-u.ac.jp/ ̃chu/resource/wiki_zh_ja.tgz.<br />
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{{cite journal |last1=Chu |first1=Chenhui |last2=Nakazawa |first2=Toshiaki |last3=Kurohashi |first3=Sadao |title=Constructing a Chinese―Japanese Parallel Corpus from Wikipedia |date=2014 |url=https://wikipediaquality.com/wiki/Constructing_a_Chinese―Japanese_Parallel_Corpus_from_Wikipedia}}<br />
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Chu, Chenhui; Nakazawa, Toshiaki; Kurohashi, Sadao. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Constructing_a_Chinese―Japanese_Parallel_Corpus_from_Wikipedia">Constructing a Chinese―Japanese Parallel Corpus from Wikipedia</a>&amp;quot;.<br />
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[[Category:Scientific works]]<br />
[[Category:Japanese Wikipedia]]<br />
[[Category:Chinese Wikipedia]]</div>Evelynhttps://wikipediaquality.com/index.php?title=Information_Arbitrage_Across_Multi-Lingual_Wikipedia&diff=21746Information Arbitrage Across Multi-Lingual Wikipedia2019-11-02T10:07:54Z<p>Evelyn: Adding infobox</p>
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<div>{{Infobox work<br />
| title = Information Arbitrage Across Multi-Lingual Wikipedia<br />
| date = 2009<br />
| authors = [[Eytan Adar]]<br />[[Michael Skinner]]<br />[[Daniel S. Weld]]<br />
| doi = 10.1145/1498759.1498813<br />
| link = http://dl.acm.org/citation.cfm?doid=1498759.1498813<br />
}}<br />
'''Information Arbitrage Across Multi-Lingual Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Eytan Adar]], [[Michael Skinner]] and [[Daniel S. Weld]].<br />
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== Overview ==<br />
The rapid globalization of [[Wikipedia]] is generating a parallel, multi-lingual corpus of unprecedented scale. Pages for the same topic in many [[different language]]s emerge both as a result of manual translation and independent development. Unfortunately, these pages may appear at different times, vary in size, scope, and quality. Furthermore, differential growth rates cause the conceptual mapping between articles in different languages to be both complex and dynamic. These disparities provide the opportunity for a powerful form of information arbitrage --leveraging articles in one or more languages to improve the content in another. Analyzing four large language domains (English, Spanish, French, and German), authors present Ziggurat , an automated system for aligning Wikipedia [[infoboxes]], creating new infoboxes as necessary, filling in missing information, and detecting discrepancies between parallel pages. Authors method uses self-supervised learning and experiments demonstrate the method's feasibility, even in the absence of dictionaries.</div>Evelynhttps://wikipediaquality.com/index.php?title=Bootstrapping_Domain_Knowledge_Exploration_Using_Conceptual_Mapping_of_Wikipedia&diff=21745Bootstrapping Domain Knowledge Exploration Using Conceptual Mapping of Wikipedia2019-11-02T10:06:06Z<p>Evelyn: Embed</p>
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<div>{{Infobox work<br />
| title = Bootstrapping Domain Knowledge Exploration Using Conceptual Mapping of Wikipedia<br />
| date = 2013<br />
| authors = [[Mai Eldefrawi]]<br />[[Ahmed Sharaf eldin Ahmed]]<br />[[Adel Elsayed]]<br />
| doi = 10.14569/IJACSA.2013.040813<br />
| link = http://thesai.org/Downloads/Volume4No8/Paper_13-Bootstrapping_Domain_Knowledge_Exploration_using_Conceptual_Mapping_of_Wikipedia.pdf<br />
}}<br />
'''Bootstrapping Domain Knowledge Exploration Using Conceptual Mapping of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Mai Eldefrawi]], [[Ahmed Sharaf eldin Ahmed]] and [[Adel Elsayed]].<br />
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== Overview ==<br />
Wikipedia is one of the largest online encyclopedias that exist in a hypertext form. This nature prevents [[Wikipedia]]’s potential to be fully discovered. Therefore the focus of this paper is on the role of domain knowledge in supporting the exploration of classical encyclopedic content, which in this case is Wikipedia. A main contribution provided by the author of this work is a methodology for identifying the nature, the form and the role of domain knowledge expressed in conceptual form. It’s also a method of representation and analysis for describing the domain knowledge and for the extraction of the logical representation of a raw form of the domain knowledge. Such logical representation is of limited value in describing the real nature of domain knowledge. Hence authors transform it into an adequate graphical representation, mostly of an arc-node form which is called conceptual representation.<br />
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Eldefrawi, Mai; Ahmed, Ahmed Sharaf eldin; Elsayed, Adel. (2013). "[[Bootstrapping Domain Knowledge Exploration Using Conceptual Mapping of Wikipedia]]". The Science and Information (SAI) Organization Limited. DOI: 10.14569/IJACSA.2013.040813. <br />
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{{cite journal |last1=Eldefrawi |first1=Mai |last2=Ahmed |first2=Ahmed Sharaf eldin |last3=Elsayed |first3=Adel |title=Bootstrapping Domain Knowledge Exploration Using Conceptual Mapping of Wikipedia |date=2013 |doi=10.14569/IJACSA.2013.040813 |url=https://wikipediaquality.com/wiki/Bootstrapping_Domain_Knowledge_Exploration_Using_Conceptual_Mapping_of_Wikipedia |journal=The Science and Information (SAI) Organization Limited}}<br />
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Eldefrawi, Mai; Ahmed, Ahmed Sharaf eldin; Elsayed, Adel. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Bootstrapping_Domain_Knowledge_Exploration_Using_Conceptual_Mapping_of_Wikipedia">Bootstrapping Domain Knowledge Exploration Using Conceptual Mapping of Wikipedia</a>&amp;quot;. The Science and Information (SAI) Organization Limited. DOI: 10.14569/IJACSA.2013.040813. <br />
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<div>{{Infobox work<br />
| title = Using Wikipedia for Co-Clustering based Cross-Domain Text Classification<br />
| date = 2008<br />
| authors = [[Pu Wang]]<br />[[Carlotta Domeniconi]]<br />[[Jian Hu]]<br />
| doi = 10.1109/ICDM.2008.136<br />
| link = http://dl.acm.org/citation.cfm?id=1510528.1511383<br />
}}<br />
'''Using Wikipedia for Co-Clustering based Cross-Domain Text Classification''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Pu Wang]], [[Carlotta Domeniconi]] and [[Jian Hu]].<br />
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== Overview ==<br />
Traditional approaches to document classification requires labeled data in order to construct reliable and accurate classifiers. Unfortunately, labeled data are seldom available, and often too expensive to obtain. Given a learning task for which training data are not available, abundant labeled data may exist for a different but related domain. One would like to use the related labeled data as auxiliary information to accomplish the classification task in the target domain. Recently, the paradigm of transfer learning has been introduced to enable effective learning strategies when auxiliary data obey a different probability distribution. A co-clustering based classification algorithm has been previously proposed to tackle cross-domain text classification. In this work, authors extend the idea underlying this approach by making the latent semantic relationship between the two domains explicit. This goal is achieved with the use of [[Wikipedia]]. As a result, the pathway that allows to propagate labels between the two domains not only captures common words, but also semantic concepts based on the content of documents. Authors empirically demonstrate the efficacy of semantic-based approach to cross-domain classification using a variety of real data.</div>Evelynhttps://wikipediaquality.com/index.php?title=Liberating_Epistemology:_Wikipedia_and_the_Social_Construction_of_Knowledge&diff=21743Liberating Epistemology: Wikipedia and the Social Construction of Knowledge2019-11-02T10:02:46Z<p>Evelyn: Int.links</p>
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<div>'''Liberating Epistemology: Wikipedia and the Social Construction of Knowledge''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Rubén Rosario Rodríguez]].<br />
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== Overview ==<br />
This investigation contends that postfoundationalist models of rationality</div>Evelynhttps://wikipediaquality.com/index.php?title=Decision_Making_in_the_Self-Evolved_Collegiate_Court:_Wikipedia%E2%80%99s_Arbitration_Committee_and_Its_Implications_for_Self-Governance_and_Judiciary_in_Cyberspace:&diff=21742Decision Making in the Self-Evolved Collegiate Court: Wikipedia’s Arbitration Committee and Its Implications for Self-Governance and Judiciary in Cyberspace:2019-11-02T10:01:29Z<p>Evelyn: Adding wikilinks</p>
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<div>'''Decision Making in the Self-Evolved Collegiate Court: Wikipedia’s Arbitration Committee and Its Implications for Self-Governance and Judiciary in Cyberspace:''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Piotr Konieczny]].<br />
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== Overview ==<br />
This article considers the extent to which non-legal factors (nationality, activity/experience, conflict avoidance, and time constraints) affect decision making within collegiate courts, through the study of the [[Wikipedia]]’s Arbitration Committee. That body is a self-evolved collegiate court of the Internet’s fifth most popular website, whose judges (known as arbitrators) are volunteers. This study shows that the decision-making process of this body seems mostly unaffected by the demographic factors studied and the acclimatization bias. Some evidence of conflict avoidance is found. Despite the professed equality of members of the Committee, there is clear evidence that some are much more active (and thus, influential) than others. Compared to most traditional court settings, in the volunteer collegiate court studied here, time constraints play a much more significant role than previously suggested in the literature.</div>Evelynhttps://wikipediaquality.com/index.php?title=Quantitative_Analysis_of_Thewikipedia_Community_of_Users&diff=21741Quantitative Analysis of Thewikipedia Community of Users2019-11-02T09:58:30Z<p>Evelyn: Adding categories</p>
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<div>{{Infobox work<br />
| title = Quantitative Analysis of Thewikipedia Community of Users<br />
| date = 2007<br />
| authors = [[Felipe Ortega]]<br />[[Jesús M. González Barahona]]<br />
| doi = 10.1145/1296951.1296960<br />
| link = https://dl.acm.org/citation.cfm?id=1296960<br />
}}<br />
'''Quantitative Analysis of Thewikipedia Community of Users''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Felipe Ortega]] and [[Jesús M. González Barahona]].<br />
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== Overview ==<br />
Many activities of editors in [[Wikipedia]] can be traced using its database dumps, which register detailed information about every single change to every article. Several researchers have used this information to gain knowledge about the production process of articles, and about activity patterns of authors. In this analysis, authors have focused onone of those previous works, by Kittur et al. First, authors have followed the same methodology with more recent and comprehensive data. Then, authors have extended this methodology to precisely identify which fraction of authors are producing most of the changes in Wikipedia's articles, and how the behaviour of these authors evolves over time. This enabled us not only to validate some of the previous results, but also to find new interesting evidences. Authors have found that the analysis of sysops is not a good method for estimating different levels of contributions, since it is dependent on the policy for electing them (which changes over time and for each language). Moreover, authors have found new activity patterns classifying authors by their contributions during specific periods of time, instead of using their total number of contributions over the whole life of Wikipedia. Finally, authors present a tool that automates this extended methodology, implementing a quick and complete quantitative analysis ofevery language edition in Wikipedia.<br />
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Ortega, Felipe; Barahona, Jesús M. González. (2007). "[[Quantitative Analysis of Thewikipedia Community of Users]]".DOI: 10.1145/1296951.1296960. <br />
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{{cite journal |last1=Ortega |first1=Felipe |last2=Barahona |first2=Jesús M. González |title=Quantitative Analysis of Thewikipedia Community of Users |date=2007 |doi=10.1145/1296951.1296960 |url=https://wikipediaquality.com/wiki/Quantitative_Analysis_of_Thewikipedia_Community_of_Users}}<br />
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Ortega, Felipe; Barahona, Jesús M. González. (2007). &amp;quot;<a href="https://wikipediaquality.com/wiki/Quantitative_Analysis_of_Thewikipedia_Community_of_Users">Quantitative Analysis of Thewikipedia Community of Users</a>&amp;quot;.DOI: 10.1145/1296951.1296960. <br />
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[[Category:Scientific works]]</div>Evelynhttps://wikipediaquality.com/index.php?title=Expediency%E2%80%90Based_Practice%3F_Medical_Students%27_Reliance_on_Google_and_Wikipedia_for_Biomedical_Inquiries&diff=21740Expediency‐Based Practice? Medical Students' Reliance on Google and Wikipedia for Biomedical Inquiries2019-11-02T09:57:03Z<p>Evelyn: Adding infobox</p>
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<div>{{Infobox work<br />
| title = Expediency‐Based Practice? Medical Students' Reliance on Google and Wikipedia for Biomedical Inquiries<br />
| date = 2011<br />
| authors = [[Terry Judd]]<br />[[Gregor Kennedy]]<br />
| doi = 10.1111/j.1467-8535.2009.01019.x<br />
| link = http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8535.2009.01019.x/full<br />
}}<br />
'''Expediency‐Based Practice? Medical Students' Reliance on Google and Wikipedia for Biomedical Inquiries''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Terry Judd]] and [[Gregor Kennedy]].<br />
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== Overview ==<br />
Internet usage logs captured during self-directed learning sessions were used to determine how undergraduate medical students used five popular sites to locate and access biomedical resources. Students' perceptions of each site's usefulness and [[reliability]] were determined through a survey. [[Google]] and [[Wikipedia]] were the most frequently used sites despite students rating them as the least reliable of the five sites investigated. The library-the students' primary point of access to online journals-was the least used site, and when using Google less than 40% of pages or resources located by students were from 'high' quality sources. Students' use of all sites' search tools was unsophisticated. Despite being avid users of online information and search tools, the students targeted in this study appeared to lack the requisite information-seeking skills to make the most of online resources. Although there is evidence that these skills improved over time, a greater emphasis on information literacy skills training may be required to ensure that graduates are able to locate the best available evidence to support their professional practice. [ABSTRACT FROM AUTHOR]</div>Evelynhttps://wikipediaquality.com/index.php?title=Search_Your_Interests_Everywhere!:_Wikipedia-Based_Keyphrase_Extraction_from_Web_Browsing_History&diff=21739Search Your Interests Everywhere!: Wikipedia-Based Keyphrase Extraction from Web Browsing History2019-11-02T09:55:50Z<p>Evelyn: Category</p>
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<div>{{Infobox work<br />
| title = Search Your Interests Everywhere!: Wikipedia-Based Keyphrase Extraction from Web Browsing History<br />
| date = 2010<br />
| authors = [[Mitsumasa Kondo]]<br />[[Akimichi Tanaka]]<br />[[Tadasu Uchiyama]]<br />
| doi = 10.1145/1810617.1810682<br />
| link = https://dl.acm.org/citation.cfm?doid=1810617.1810682<br />
}}<br />
'''Search Your Interests Everywhere!: Wikipedia-Based Keyphrase Extraction from Web Browsing History''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Mitsumasa Kondo]], [[Akimichi Tanaka]] and [[Tadasu Uchiyama]].<br />
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== Overview ==<br />
This paper proposes a method that can extract user interests from the user's Web browsing history. Authors method allows easy access to multiple content domains such as blogs, movies, QA sites, etc. since the user does not need to input a separate search query in each domain/site. To extract user interests, the method first extracts candidate keyphrases from the user's web browsed documents. Second, important keyphrases obtained from a link structure analysis of [[Wikipedia]] content is extracted from the main contents of web documents. This technique is based on the idea that important keyphrases in Wikipedia are important keyphrases in the real world. Finally, keyphrases contained in the documents important to the user are set in order as user interests. An experiment shows that method offers improvements over a conventional method and can recommend interests attractive to the user.<br />
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Kondo, Mitsumasa; Tanaka, Akimichi; Uchiyama, Tadasu. (2010). "[[Search Your Interests Everywhere!: Wikipedia-Based Keyphrase Extraction from Web Browsing History]]".DOI: 10.1145/1810617.1810682. <br />
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{{cite journal |last1=Kondo |first1=Mitsumasa |last2=Tanaka |first2=Akimichi |last3=Uchiyama |first3=Tadasu |title=Search Your Interests Everywhere!: Wikipedia-Based Keyphrase Extraction from Web Browsing History |date=2010 |doi=10.1145/1810617.1810682 |url=https://wikipediaquality.com/wiki/Search_Your_Interests_Everywhere!:_Wikipedia-Based_Keyphrase_Extraction_from_Web_Browsing_History}}<br />
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Kondo, Mitsumasa; Tanaka, Akimichi; Uchiyama, Tadasu. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Search_Your_Interests_Everywhere!:_Wikipedia-Based_Keyphrase_Extraction_from_Web_Browsing_History">Search Your Interests Everywhere!: Wikipedia-Based Keyphrase Extraction from Web Browsing History</a>&amp;quot;.DOI: 10.1145/1810617.1810682. <br />
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[[Category:Scientific works]]</div>Evelynhttps://wikipediaquality.com/index.php?title=Impact_of_Wikipedia_on_Citation_Trends&diff=21738Impact of Wikipedia on Citation Trends2019-11-02T09:53:17Z<p>Evelyn: Creating a new page - Impact of Wikipedia on Citation Trends</p>
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<div>'''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 />
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== 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>Evelynhttps://wikipediaquality.com/index.php?title=Dcu_at_Wikipediamm_2009:_Document_Expansion_from_Wikipedia_Abstracts&diff=21737Dcu at Wikipediamm 2009: Document Expansion from Wikipedia Abstracts2019-11-02T09:51:36Z<p>Evelyn: + wikilinks</p>
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<div>'''Dcu at Wikipediamm 2009: Document Expansion from Wikipedia Abstracts''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Jinming Min]], [[Peter Wilkins]], [[Johannes Leveling]] and [[Gareth J. F. Jones]].<br />
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== Overview ==<br />
In this paper, authors describe participation in the [[Wikipedia]]MM task at CLEF 2009. Authors main efforts concern the expansion of the image metadata from the Wikipedia abstracts collection [[DBpedia]]. Since the metadata is short for retrieval by query words, authors decided to expand the metadata using a typical query expansion method. In our</div>Evelynhttps://wikipediaquality.com/index.php?title=Learning_to_Integrate_Relational_Databases_with_Wikipedia&diff=21736Learning to Integrate Relational Databases with Wikipedia2019-11-02T09:50:15Z<p>Evelyn: + Embed</p>
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<div>{{Infobox work<br />
| title = Learning to Integrate Relational Databases with Wikipedia<br />
| date = 2009<br />
| authors = [[Doug Downey]]<br />[[Arun Ahuja]]<br />[[Michael R. Anderson]]<br />
| link = http://web.eecs.umich.edu/~mrander/pubs/downey_wikiai09.pdf<br />
}}<br />
'''Learning to Integrate Relational Databases with Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Doug Downey]], [[Arun Ahuja]] and [[Michael R. Anderson]].<br />
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== Overview ==<br />
Wikipedia is a general encyclopedia of unprecedented breadth and popularity. However, much of the Web’s factual information still lies within relational databases, each focused on a specific topic. While many database entities are described by corresponding [[Wikipedia]] pages, in general this correspondence is unknown unless it has been manually specified. As a result, Web databases cannot leverage the relevant rich descriptions and interrelationships captured in Wikipedia, and Wikipedia readers miss the extensive coverage that a database typically provides on its specific topic. In this paper, authors present ETOW, a system that automatically integrates relational databases with Wikipedia. ETOW uses machine learning techniques to identify the correspondences between database entities and Wikipedia pages. In experiments with two distinct Web databases, authors demonstrate that ETOW outperforms baseline techniques, reducing error overall by an average of 19%, and reducing false positive rate by 50%. In one experiment, ETOW is able to identify approximately 13,000 correct matches at a precision of 0.97. Authors also present evidence suggesting that ETOW can substantially improve the coverage and utility of both the relational databases and Wikipedia.<br />
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{{cite journal |last1=Downey |first1=Doug |last2=Ahuja |first2=Arun |last3=Anderson |first3=Michael R. |title=Learning to Integrate Relational Databases with Wikipedia |date=2009 |url=https://wikipediaquality.com/wiki/Learning_to_Integrate_Relational_Databases_with_Wikipedia}}<br />
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Downey, Doug; Ahuja, Arun; Anderson, Michael R.. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Learning_to_Integrate_Relational_Databases_with_Wikipedia">Learning to Integrate Relational Databases with Wikipedia</a>&amp;quot;.<br />
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<div>'''Wikipedia's Role in Science Education and Outreach''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Mark B. Moldwin]], [[N. Gross]] and [[T. Miller]].<br />
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== Overview ==<br />
Where do most students get information about space science topics? An informal survey of 300 University of California, Los Angeles freshmen indicates that [[Wikipedia]] is their first stop when researching essentially any topic. One third of student pre-service science teachers in a Boston University natural sciences course cited Wikipedia as a reference in their class reports on planetary exploration. A survey of [[Google]]® Web-page rankings of 20 major topics in space physics (including aurora, space weather, solar wind, etc.) found that the Wikipedia entry was on the first page for 16, was in the top three for 11, and was the highest-ranked site for six topics (magnetic reconnection, solar variation, Van Allen Radiation Belts, ionosphere, sudden ionospheric disturbance, and magnetometer).</div>Evelyn