https://wikipediaquality.com/api.php?action=feedcontributions&user=Amelia&feedformat=atomWikipedia Quality - User contributions [en]2024-03-28T12:08:25ZUser contributionsMediaWiki 1.30.0https://wikipediaquality.com/index.php?title=Academics_Can_Help_Shape_Wikipedia&diff=25529Academics Can Help Shape Wikipedia2020-10-11T07:29:43Z<p>Amelia: Academics Can Help Shape Wikipedia -- new article</p>
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<div>'''Academics Can Help Shape Wikipedia''' - scientific work related to Wikipedia quality published in 2017, written by Thomas Shafee, Daniel Mietchen and Andrew I. Su.<br />
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== Overview ==<br />
Public understanding of science is increasingly important. Wikipedia is widely used by students, educators, researchers, doctors, journalists, and policy-makers. The online, crowd-sourced encyclopedia site is perceived as increasingly trustworthy, making it a key public engagement platform with</div>Ameliahttps://wikipediaquality.com/index.php?title=%E2%80%9CBe_Nice%E2%80%9D:_Wikipedia_Norms_for_Supportive_Communication&diff=25528“Be Nice”: Wikipedia Norms for Supportive Communication2020-10-11T07:27:01Z<p>Amelia: + infobox</p>
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<div>{{Infobox work<br />
| title = “Be Nice”: Wikipedia Norms for Supportive Communication<br />
| date = 2010<br />
| authors = [[Joseph M. Reagle]]<br />
| doi = 10.1080/13614568.2010.498528<br />
| link = https://dl.acm.org/citation.cfm?id=1839852<br />
}}<br />
'''“Be Nice”: Wikipedia Norms for Supportive Communication''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Joseph M. Reagle]].<br />
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== Overview ==<br />
Wikipedia is acknowledged to have been home to “some bitter disputes.” Indeed, conflict at [[Wikipedia]] is said to be “as addictive as cocaine.” Yet, such observations are not cynical commentary but motivation for a collection of social norms. These norms speak to the intentional stance and communicative behaviors [[Wikipedians]] should adopt when interacting with one another. In the following pages, Author provide a survey of these norms on the [[English Wikipedia]] and argue that they can be characterized as supportive based on Jack Gibb's classic communication article “Defensive Communication.”</div>Ameliahttps://wikipediaquality.com/index.php?title=A_Distant_Learning_Approach_for_Extracting_Hypernym_Relations_from_Wikipedia_Disambiguation_Pages&diff=25527A Distant Learning Approach for Extracting Hypernym Relations from Wikipedia Disambiguation Pages2020-10-11T07:25:21Z<p>Amelia: Category</p>
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<div>{{Infobox work<br />
| title = A Distant Learning Approach for Extracting Hypernym Relations from Wikipedia Disambiguation Pages<br />
| date = 2017<br />
| authors = [[Mouna Kamel]]<br />[[Cássia Trojahn]]<br />[[Adel Ghamnia]]<br />[[Nathalie Aussenac-Gilles]]<br />[[Cécile Fabre]]<br />
| doi = 10.1016/j.procs.2017.08.208<br />
| link = http://www.sciencedirect.com/science/article/pii/S1877050917316046<br />
}}<br />
'''A Distant Learning Approach for Extracting Hypernym Relations from Wikipedia Disambiguation Pages''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Mouna Kamel]], [[Cássia Trojahn]], [[Adel Ghamnia]], [[Nathalie Aussenac-Gilles]] and [[Cécile Fabre]].<br />
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== Overview ==<br />
Abstract Extracting hypernym relations from text is one of the key steps in the automated construction and enrichment of semantic resources. The state of the art offers a large varierty of methods (linguistic, statistical, learning based, hybrid). This variety could be an answer to the need to process each corpus or text fragment according to its specificities (e.g. domain granularity, nature, language, or target semantic resource). Moreover, hypernym relation may take different linguistic forms. The aim of this paper is to study the behaviour of a supervised learning approach to extract hypernym relations whatever the way they are expressed, and to evaluate its ability to capture regularities from the corpus, without human intervention. Authors apply a distant supervised learning algorithm on a sub-set of [[Wikipedia]] in French made of disambiguation pages where authors manually annotated hypernym relations. The learned model obtained a F-measure of 0.67, outperforming lexico-syntactic pattern matching used as baseline.<br />
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Kamel, Mouna; Trojahn, Cássia; Ghamnia, Adel; Aussenac-Gilles, Nathalie; Fabre, Cécile. (2017). "[[A Distant Learning Approach for Extracting Hypernym Relations from Wikipedia Disambiguation Pages]]". Elsevier. DOI: 10.1016/j.procs.2017.08.208. <br />
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{{cite journal |last1=Kamel |first1=Mouna |last2=Trojahn |first2=Cássia |last3=Ghamnia |first3=Adel |last4=Aussenac-Gilles |first4=Nathalie |last5=Fabre |first5=Cécile |title=A Distant Learning Approach for Extracting Hypernym Relations from Wikipedia Disambiguation Pages |date=2017 |doi=10.1016/j.procs.2017.08.208 |url=https://wikipediaquality.com/wiki/A_Distant_Learning_Approach_for_Extracting_Hypernym_Relations_from_Wikipedia_Disambiguation_Pages |journal=Elsevier}}<br />
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Kamel, Mouna; Trojahn, Cássia; Ghamnia, Adel; Aussenac-Gilles, Nathalie; Fabre, Cécile. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/A_Distant_Learning_Approach_for_Extracting_Hypernym_Relations_from_Wikipedia_Disambiguation_Pages">A Distant Learning Approach for Extracting Hypernym Relations from Wikipedia Disambiguation Pages</a>&amp;quot;. Elsevier. DOI: 10.1016/j.procs.2017.08.208. <br />
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[[Category:Scientific works]]<br />
[[Category:French Wikipedia]]</div>Ameliahttps://wikipediaquality.com/index.php?title=Daedalus_at_Imageclef_Wikipedia_Retrieval_2010:_Expanding_with_Semantic_Information_from_Context&diff=25526Daedalus at Imageclef Wikipedia Retrieval 2010: Expanding with Semantic Information from Context2020-10-11T07:23:18Z<p>Amelia: Embed</p>
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<div>{{Infobox work<br />
| title = Daedalus at Imageclef Wikipedia Retrieval 2010: Expanding with Semantic Information from Context<br />
| date = 2010<br />
| authors = [[Sara Lana-Serrano]]<br />[[Julio Villena-Román]]<br />[[José Carlos González Cristóbal]]<br />
| link = http://ceur-ws.org/Vol-1176/CLEF2010wn-ImageCLEF-Lana-SerranoEt2010.pdf<br />
}}<br />
'''Daedalus at Imageclef Wikipedia Retrieval 2010: Expanding with Semantic Information from Context''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Sara Lana-Serrano]], [[Julio Villena-Román]] and [[José Carlos González Cristóbal]].<br />
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== Overview ==<br />
This paper describes the participation of DAEDALUS at the ImageCLEF 2010 [[Wikipedia]] Retrieval task. The main focus of experiments is to evaluate the impact in the image retrieval pr ocess of the incorporation of [[semantic information]] extracted only from the textua l information provided as metadata of the image itself, as compared to expand ing with contextual information gathered from the document where the image is referred. For the semantic annotation, [[DBpedia]] [[ontology]] and YAGO classification schema are used. As expected, the obtained results show that, in general, the textual information attached to a given image is not able t o fully represent certain [[features]] of the image. Furthermore, the use of sema ntic information in the process of multimedia [[information extraction]] poses two hard challenges still to solve: how to automatically extract the high level features associated to a multimedia resource, and, once the resource has bee n semantically tagged, which features must be used in the retrieval proces s to best model the actual and complete meaning of the user query.<br />
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Lana-Serrano, Sara; Villena-Román, Julio; Cristóbal, José Carlos González. (2010). "[[Daedalus at Imageclef Wikipedia Retrieval 2010: Expanding with Semantic Information from Context]]".<br />
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{{cite journal |last1=Lana-Serrano |first1=Sara |last2=Villena-Román |first2=Julio |last3=Cristóbal |first3=José Carlos González |title=Daedalus at Imageclef Wikipedia Retrieval 2010: Expanding with Semantic Information from Context |date=2010 |url=https://wikipediaquality.com/wiki/Daedalus_at_Imageclef_Wikipedia_Retrieval_2010:_Expanding_with_Semantic_Information_from_Context}}<br />
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Lana-Serrano, Sara; Villena-Román, Julio; Cristóbal, José Carlos González. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Daedalus_at_Imageclef_Wikipedia_Retrieval_2010:_Expanding_with_Semantic_Information_from_Context">Daedalus at Imageclef Wikipedia Retrieval 2010: Expanding with Semantic Information from Context</a>&amp;quot;.<br />
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</code></div>Ameliahttps://wikipediaquality.com/index.php?title=Lifecycle-Based_Evolution_of_Features_in_Collaborative_Open_Production_Communities:_the_Case_of_Wikipedia&diff=25525Lifecycle-Based Evolution of Features in Collaborative Open Production Communities: the Case of Wikipedia2020-10-11T07:21:20Z<p>Amelia: Adding embed</p>
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<div>{{Infobox work<br />
| title = Lifecycle-Based Evolution of Features in Collaborative Open Production Communities: the Case of Wikipedia<br />
| date = 2013<br />
| authors = [[Pujan Ziaie]]<br />[[Medin Imamovic]]<br />
| link = http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1360&amp;context=ecis2013_cr<br />
}}<br />
'''Lifecycle-Based Evolution of Features in Collaborative Open Production Communities: the Case of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Pujan Ziaie]] and [[Medin Imamovic]].<br />
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== Overview ==<br />
In the last decade, collaborative open production communities have provided an effective platform for geographically dispersed users to collaborate and generate content in a well-structured and consistent form. [[Wikipedia]] is a prominent example in this area. What is of great importance in production communities is the prioritization and evolution of [[features]] with regards to the community lifecycle. Users are the cornerstone of such communities and their needs and attitudes constantly change as communities grow. The increasing amount and versatility of content and users requires modifications in areas ranging from user roles and access levels to content quality standards and community policies and goals. In this paper, authors draw on two pertinent theories in terms of the lifecycle of online communities and open collaborative communities in particular by focusing on the case of Wikipedia. Authors conceptualize three general stages (Rising, Organizing, and Stabilizing) within the lifecycle of collaborative open production communities. The salient factors, features and focus of attention in each stage are provided and the chronology of features is visualized. These findings, if properly generalized, can help designers of other types of open production communities effectively allocate their resources and introduce new features based on the needs of both community and users.<br />
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Ziaie, Pujan; Imamovic, Medin. (2013). "[[Lifecycle-Based Evolution of Features in Collaborative Open Production Communities: the Case of Wikipedia]]".<br />
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{{cite journal |last1=Ziaie |first1=Pujan |last2=Imamovic |first2=Medin |title=Lifecycle-Based Evolution of Features in Collaborative Open Production Communities: the Case of Wikipedia |date=2013 |url=https://wikipediaquality.com/wiki/Lifecycle-Based_Evolution_of_Features_in_Collaborative_Open_Production_Communities:_the_Case_of_Wikipedia}}<br />
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Ziaie, Pujan; Imamovic, Medin. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Lifecycle-Based_Evolution_of_Features_in_Collaborative_Open_Production_Communities:_the_Case_of_Wikipedia">Lifecycle-Based Evolution of Features in Collaborative Open Production Communities: the Case of Wikipedia</a>&amp;quot;.<br />
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</code></div>Ameliahttps://wikipediaquality.com/index.php?title=Measuring_Author_Contributions_to_the_Wikipedia&diff=25524Measuring Author Contributions to the Wikipedia2020-10-11T07:18:29Z<p>Amelia: + Infobox work</p>
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<div>{{Infobox work<br />
| title = Measuring Author Contributions to the Wikipedia<br />
| date = 2008<br />
| authors = [[B. Thomas Adler]]<br />[[Luca de Alfaro]]<br />[[Ian Pye]]<br />[[Vishwanath Raman]]<br />
| doi = 10.1145/1822258.1822279<br />
| link = http://dl.acm.org/citation.cfm?doid=1822258.1822279<br />
}}<br />
'''Measuring Author Contributions to the Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[B. Thomas Adler]], [[Luca de Alfaro]], [[Ian Pye]] and [[Vishwanath Raman]].<br />
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== Overview ==<br />
Authors consider the problem of measuring user contributions to versioned, collaborative bodies of information, such as wikis. Measuring the contributions of individual authors can be used to divide revenue, to recognize merit, to award status promotions, and to choose the order of authors when citing the content. In the context of the [[Wikipedia]], previous works on author contribution estimation have focused on two criteria: the total text created, and the total number of edits performed. Authors show that neither of these criteria work well: both techniques are vulnerable to manipulation, and the total-text criterion fails to reward people who polish or re-arrange the content. Authors consider and compare various alternative criteria that take into account the quality of a contribution, in addition to the quantity, and authors analyze how the criteria differ in the way they rank authors according to their contributions. As an outcome of this study, authors propose to adopt total edit longevity as a measure of author contribution. Edit longevity is resistant to simple attacks, since edits are counted towards an author's contribution only if other authors accept the contribution. Edit longevity equally rewards people who create content, and people who rearrange or polish the content. Finally, edit longevity distinguishes the people who contribute little (who have contribution close to zero) from spammers or vandals, whose contribution quickly grows negative.</div>Ameliahttps://wikipediaquality.com/index.php?title=Impact_of_Wikipedia_on_Market_Information_Environment:_Evidence_on_Management_Disclosure_and_Investor_Reaction&diff=23109Impact of Wikipedia on Market Information Environment: Evidence on Management Disclosure and Investor Reaction2020-01-04T11:23:55Z<p>Amelia: + embed code</p>
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<div>{{Infobox work<br />
| title = Impact of Wikipedia on Market Information Environment: Evidence on Management Disclosure and Investor Reaction<br />
| date = 2013<br />
| authors = [[Sean Xin Xu]]<br />[[Xiaoquan Zhang]]<br />
| doi = 10.25300/MISQ/2013/37.4.03<br />
| link = http://dl.acm.org/citation.cfm?id=2600484<br />
}}<br />
'''Impact of Wikipedia on Market Information Environment: Evidence on Management Disclosure and Investor Reaction''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Sean Xin Xu]] and [[Xiaoquan Zhang]].<br />
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== Overview ==<br />
In this paper, authors seek to determine whether a typical social media platform, [[Wikipedia]], improves the information environment for investors in the financial market. Authors theoretical lens leads us to expect that information aggregation about public companies on Wikipedia may influence how management's voluntary information disclosure reacts to market uncertainty with respect to investors' information about these companies. Authors empirical analysis is based on a unique data set collected from financial records, management disclosure records, news article coverage, and a Wikipedia modification history of public companies. On the supply side of information, authors find that information aggregation on Wikipedia can moderate the timing of managers' voluntary disclosure of companies' earnings disappointments, or bad news. On the demand side of information, authors find that Wikipedia's information aggregation moderates investors' negative reaction to bad news. Taken together, these findings support the view that Wikipedia improves the information environment in the financial market and underscore the value of information aggregation through the use of information technology.<br />
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Xu, Sean Xin; Zhang, Xiaoquan. (2013). "[[Impact of Wikipedia on Market Information Environment: Evidence on Management Disclosure and Investor Reaction]]". Society for Information Management and The Management Information Systems Research Center. DOI: 10.25300/MISQ/2013/37.4.03. <br />
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{{cite journal |last1=Xu |first1=Sean Xin |last2=Zhang |first2=Xiaoquan |title=Impact of Wikipedia on Market Information Environment: Evidence on Management Disclosure and Investor Reaction |date=2013 |doi=10.25300/MISQ/2013/37.4.03 |url=https://wikipediaquality.com/wiki/Impact_of_Wikipedia_on_Market_Information_Environment:_Evidence_on_Management_Disclosure_and_Investor_Reaction |journal=Society for Information Management and The Management Information Systems Research Center}}<br />
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Xu, Sean Xin; Zhang, Xiaoquan. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Impact_of_Wikipedia_on_Market_Information_Environment:_Evidence_on_Management_Disclosure_and_Investor_Reaction">Impact of Wikipedia on Market Information Environment: Evidence on Management Disclosure and Investor Reaction</a>&amp;quot;. Society for Information Management and The Management Information Systems Research Center. DOI: 10.25300/MISQ/2013/37.4.03. <br />
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<div>{{Infobox work<br />
| title = From Wikipedia to the Classroom: Exploring Online Publication and Learning<br />
| date = 2006<br />
| authors = [[Andrea Forte]]<br />[[Amy Bruckman]]<br />
| link = http://dl.acm.org/citation.cfm?id=1150061<br />
}}<br />
'''From Wikipedia to the Classroom: Exploring Online Publication and Learning''' - scientific work related to [[Wikipedia quality]] published in 2006, written by [[Andrea Forte]] and [[Amy Bruckman]].<br />
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== Overview ==<br />
Wikipedia represents an intriguing new publishing paradigm---can it be used to engage students in authentic collaborative writing activities? How can authors design wiki publishing tools and curricula to support learning among student authors? Authors suggest that wiki publishing environments can create learning opportunities that address four dimensions of authenticity: personal, real world, disciplinary, and assessment. Authors have begun a series of design studies to investigate links between wiki publishing experiences and writing-to-learn. The results of an initial study in an undergraduate government course indicate that perceived audience plays an important role in helping students monitor the quality of writing; however, students' perception of audience on the Internet is not straightforward. This preliminary iteration resulted in several guidelines that are shaping efforts to design and implement new wiki publishing tools and curricula for students and teachers.</div>Ameliahttps://wikipediaquality.com/index.php?title=Complex_Interactive_Question_Answering_Enhanced_with_Wikipedia&diff=23107Complex Interactive Question Answering Enhanced with Wikipedia2020-01-04T11:19:45Z<p>Amelia: Creating a page: Complex Interactive Question Answering Enhanced with Wikipedia</p>
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<div>'''Complex Interactive Question Answering Enhanced with Wikipedia''' - scientific work related to Wikipedia quality published in 2007, written by Ian MacKinnon and Olga Vechtomova.<br />
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== Overview ==<br />
In ciQA, templates are used with several bracketed items authors call ”facets” which are the basis of information being sought. This information is returned in the form of nuggets. Due to the concepts being sought having multiple terms to describe them, it becomes difficult to determine which sentences in the AQUAINT-2 news articles contain the query terms being sought, as they may be represented in the parent document by a variety of different phrases still making reference to the query term. For example, if the term ”John McCain” were being sought, it might appear in an article, however, the sentence which is the vital nugget may simply contain ”Senator McCain”; an imperfect match. Traditional query expansion[5] of facets would introduce new terms which are related but do not necessarily mean the same as the original facet. This does not always help the problem of query terms appearing in relevant documents but not relevant sentences within documents, it only introduces related terms which cannot be considered synonymous with the facet being retrieved. In this year’s TREC, authors hope to overcome some of this problem by looking for synonyms for facets using Wikipedia. Many of the ciQA facets are proper nouns and most thesauri, such as WordNet, do not contain entries for these. Thus, a new manner of finding synonyms must be found. In recent years, several new approaches have been proposed to use Wikipedia as a source of lexical information[2, 7], as it can be downloaded in its entirety, and contains relatively high quality articles[3]. 2 Wikipedia</div>Ameliahttps://wikipediaquality.com/index.php?title=Wikipedia_Customization_Through_Web_Augmentation_Techniques&diff=23106Wikipedia Customization Through Web Augmentation Techniques2020-01-04T11:17:17Z<p>Amelia: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Wikipedia Customization Through Web Augmentation Techniques<br />
| date = 2012<br />
| authors = [[Oscar Díaz]]<br />[[Cristóbal Arellano]]<br />[[Gorka Puente]]<br />
| doi = 10.1145/2462932.2462947<br />
| link = http://dl.acm.org/citation.cfm?id=2462932.2462947<br />
}}<br />
'''Wikipedia Customization Through Web Augmentation Techniques''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Oscar Díaz]], [[Cristóbal Arellano]] and [[Gorka Puente]].<br />
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== Overview ==<br />
Wikipedia is a successful example of collaborative knowledge construction. This can be synergistically complemented with personal knowledge construction whereby individuals are supported in their sharing, experimenting and building of information in a more private setting, without the scrutiny of the whole community. Ideally, both approaches should be seamlessly integrated so that wikipedians can easily transit from the public sphere to the private sphere, and vice versa. To this end, authors introduce WikiLayer , a plugin for [[Wikipedia]] that permits wikipedians locally supplement Wikipedia articles with their own content (i.e. a layer ). Layering additional content is achieved locally by seamlessly interspersing Wikipedia content with custom content. WikiLayer is driven by three main wiki principles: affordability (i.e., if you know how to edit articles, you know how to layer), organic growth (i.e., layers evolve in synchrony with the underlying articles) and shareability (i.e., layers can be shared in confidence through the wikipedian's [[social network]], e.g., [[Facebook]] ). The paper provides motivating scenarios for readers, contributors and editors. WikiLayer is available for download at http://webaugmentation.org/wikilayer.xpi.<br />
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{{cite journal |last1=Díaz |first1=Oscar |last2=Arellano |first2=Cristóbal |last3=Puente |first3=Gorka |title=Wikipedia Customization Through Web Augmentation Techniques |date=2012 |doi=10.1145/2462932.2462947 |url=https://wikipediaquality.com/wiki/Wikipedia_Customization_Through_Web_Augmentation_Techniques}}<br />
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Díaz, Oscar; Arellano, Cristóbal; Puente, Gorka. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia_Customization_Through_Web_Augmentation_Techniques">Wikipedia Customization Through Web Augmentation Techniques</a>&amp;quot;.DOI: 10.1145/2462932.2462947. <br />
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<div>{{Infobox work<br />
| title = Classroom Wikipedia Participation Effects on Future Intentions to Contribute<br />
| date = 2012<br />
| authors = [[Cliff Lampe]]<br />[[Jonathan A. Obar]]<br />[[Elif Yilmaz Ozkaya]]<br />[[Paul Zube]]<br />[[Alcides Velasquez]]<br />
| doi = 10.1145/2145204.2145267<br />
| link = http://dl.acm.org/citation.cfm?id=2145267<br />
}}<br />
'''Classroom Wikipedia Participation Effects on Future Intentions to Contribute''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Cliff Lampe]], [[Jonathan A. Obar]], [[Elif Yilmaz Ozkaya]], [[Paul Zube]] and [[Alcides Velasquez]].<br />
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== Overview ==<br />
One of the biggest challenges faced by social media sites like [[Wikipedia]] is how to motivate users to contribute content. Research continues to demonstrate that only a small percentage of users contribute to user-generated content sites. In this study authors assess the results of a [[Wikimedia Foundation]] initiative, which had graduate and undergraduate students from 22 U.S. universities contribute content to Wikipedia articles as part of their coursework. 185 students were asked about their participation in the initiative and their intention to participate on Wikipedia in the future. Results suggest that intentions to continue contributing are influenced by the initial attitude towards the class, and the degree to which students perceived they were writing for a global audience.</div>Ameliahttps://wikipediaquality.com/index.php?title=Wiki-Thanks:_Cultural_Differences_in_Thanks_Networks_by_Analysing_Who_Thanks_Whom_in_Different-Language_Wikipedias&diff=23104Wiki-Thanks: Cultural Differences in Thanks Networks by Analysing Who Thanks Whom in Different-Language Wikipedias2020-01-04T11:12:03Z<p>Amelia: Adding categories</p>
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| title = Wiki-Thanks: Cultural Differences in Thanks Networks by Analysing Who Thanks Whom in Different-Language Wikipedias<br />
| date = 2016<br />
| authors = [[Keiichi Nemoto]]<br />[[Ken-ichi Okada]]<br />
| doi = 10.1504/IJODE.2016.10001020<br />
| link = http://www.inderscience.com/link.php?id=10001020<br />
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'''Wiki-Thanks: Cultural Differences in Thanks Networks by Analysing Who Thanks Whom in Different-Language Wikipedias''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Keiichi Nemoto]] and [[Ken-ichi Okada]].<br />
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== Overview ==<br />
Wikipedia is one of the world's largest social production platforms, featuring high quality articles without a central control. Many scholars have investigated how people in creating articles for the online encyclopaedia collaborate with other authors. [[Wikipedia]] is available in 288 languages, among which are Finish, Korean, and Japanese, languages which are not spoken outside of the countries in which they originated. Therefore, Wikipedia offers a type of microscope for analysing how people in these local cultures work together. In May 2013, the [[English Wikipedia]] introduced a new social function - Wiki-Thanks. This facility enables authors to send thanks to other Wikipedia users who have contributed to or edited their articles. In this paper, authors aim to evaluate this new social tool from different cultural perspectives. To achieve this goal, authors analyse Wiki-Thanks log events and compare [[different language]] editions of Wikipedia - English, German, Spanish, Chinese, Japanese, Korean, and Finnish.<br />
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{{cite journal |last1=Nemoto |first1=Keiichi |last2=Okada |first2=Ken-ichi |title=Wiki-Thanks: Cultural Differences in Thanks Networks by Analysing Who Thanks Whom in Different-Language Wikipedias |date=2016 |doi=10.1504/IJODE.2016.10001020 |url=https://wikipediaquality.com/wiki/Wiki-Thanks:_Cultural_Differences_in_Thanks_Networks_by_Analysing_Who_Thanks_Whom_in_Different-Language_Wikipedias |journal=Inderscience Publishers (IEL)}}<br />
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Nemoto, Keiichi; Okada, Ken-ichi. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wiki-Thanks:_Cultural_Differences_in_Thanks_Networks_by_Analysing_Who_Thanks_Whom_in_Different-Language_Wikipedias">Wiki-Thanks: Cultural Differences in Thanks Networks by Analysing Who Thanks Whom in Different-Language Wikipedias</a>&amp;quot;. Inderscience Publishers (IEL). DOI: 10.1504/IJODE.2016.10001020. <br />
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[[Category:Scientific works]]<br />
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[[Category:Korean Wikipedia]]</div>Ameliahttps://wikipediaquality.com/index.php?title=Fact_Discovery_in_Wikipedia&diff=23103Fact Discovery in Wikipedia2020-01-04T11:10:42Z<p>Amelia: + embed code</p>
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<div>{{Infobox work<br />
| title = Fact Discovery in Wikipedia<br />
| date = 2007<br />
| authors = [[Sisay Fissaha Adafre]]<br />[[Valentin Jijkoun]]<br />[[M. de Rijke]]<br />
| doi = 10.1109/WI.2007.57<br />
| link = http://ieeexplore.ieee.org/document/4427085/<br />
}}<br />
'''Fact Discovery in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Sisay Fissaha Adafre]], [[Valentin Jijkoun]] and [[M. de Rijke]].<br />
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== Overview ==<br />
Authors address the task of extracting focused salient information items, relevant and important for a given topic, from a large encyclopedic resource. Specifically, for a given topic (a [[Wikipedia]] article) authors identify snippets from other articles in Wikipedia that contain important information for the topic of the original article, without duplicates. Authors compare several methods for addressing the task, and find that a mixture of content-based, link-based, and layout-based [[features]] outperforms other methods, especially in combination with the use of so-called reference corpora that capture the key properties of entities of a common type.<br />
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Adafre, Sisay Fissaha; Jijkoun, Valentin; Rijke, M. de. (2007). "[[Fact Discovery in Wikipedia]]". IEEE Computer Society. DOI: 10.1109/WI.2007.57. <br />
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{{cite journal |last1=Adafre |first1=Sisay Fissaha |last2=Jijkoun |first2=Valentin |last3=Rijke |first3=M. de |title=Fact Discovery in Wikipedia |date=2007 |doi=10.1109/WI.2007.57 |url=https://wikipediaquality.com/wiki/Fact_Discovery_in_Wikipedia |journal=IEEE Computer Society}}<br />
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Adafre, Sisay Fissaha; Jijkoun, Valentin; Rijke, M. de. (2007). &amp;quot;<a href="https://wikipediaquality.com/wiki/Fact_Discovery_in_Wikipedia">Fact Discovery in Wikipedia</a>&amp;quot;. IEEE Computer Society. DOI: 10.1109/WI.2007.57. <br />
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</code></div>Ameliahttps://wikipediaquality.com/index.php?title=Use_of_Wikipedia_Categories_on_Information_Retrieval_Research:_a_Brief_Review&diff=23102Use of Wikipedia Categories on Information Retrieval Research: a Brief Review2020-01-04T11:09:31Z<p>Amelia: Links</p>
<hr />
<div>'''Use of Wikipedia Categories on Information Retrieval Research: a Brief Review''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Jesús Tramullas]], [[Piedad Garrido-Picazo]] and [[Ana I. Sánchez-Casabón]].<br />
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== Overview ==<br />
Wikipedia [[categories]], a classification scheme built for organizing and describing Wikpedia articles, are being applied in computer science research. This paper adopts a systematic literature review approach, in order to identify different approaches and uses of [[Wikipedia categories]] in [[information retrieval]] research. Several types of work are identified, depending on the intrinsic study of the categories structure, or its use as a tool for the processing and analysis of other documentary corpus different to [[Wikipedia]]. Information retrieval is identified as one of the major areas of use, in particular its application in the refinement and improvement of search expressions, and the construction of textual corpus. However, the set of available works shows that in many cases research approaches applied and results obtained can be integrated into a comprehensive and inclusive concept of information retrieval.</div>Ameliahttps://wikipediaquality.com/index.php?title=Exploiting_Wikipedia_for_Cross-Lingual_and_Multilingual_Information_Retrieval&diff=23101Exploiting Wikipedia for Cross-Lingual and Multilingual Information Retrieval2020-01-04T11:06:48Z<p>Amelia: + category</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 />
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== 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.<br />
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Sorg, Philipp; Cimiano, Philipp. (2012). "[[Exploiting Wikipedia for Cross-Lingual and Multilingual Information Retrieval]]". Elsevier Science Publishers B. V.. DOI: 10.1016/j.datak.2012.02.003. <br />
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{{cite journal |last1=Sorg |first1=Philipp |last2=Cimiano |first2=Philipp |title=Exploiting Wikipedia for Cross-Lingual and Multilingual Information Retrieval |date=2012 |doi=10.1016/j.datak.2012.02.003 |url=https://wikipediaquality.com/wiki/Exploiting_Wikipedia_for_Cross-Lingual_and_Multilingual_Information_Retrieval |journal=Elsevier Science Publishers B. V.}}<br />
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Sorg, Philipp; Cimiano, Philipp. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Exploiting_Wikipedia_for_Cross-Lingual_and_Multilingual_Information_Retrieval">Exploiting Wikipedia for Cross-Lingual and Multilingual Information Retrieval</a>&amp;quot;. Elsevier Science Publishers B. V.. DOI: 10.1016/j.datak.2012.02.003. <br />
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[[Category:Scientific works]]</div>Ameliahttps://wikipediaquality.com/index.php?title=From_Wikipedia_to_the_Classroom:_Exploring_Online_Publication_and_Learning&diff=23100From Wikipedia to the Classroom: Exploring Online Publication and Learning2020-01-04T11:05:04Z<p>Amelia: Adding wikilinks</p>
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<div>'''From Wikipedia to the Classroom: Exploring Online Publication and Learning''' - scientific work related to [[Wikipedia quality]] published in 2006, written by [[Andrea Forte]] and [[Amy Bruckman]].<br />
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== Overview ==<br />
Wikipedia represents an intriguing new publishing paradigm---can it be used to engage students in authentic collaborative writing activities? How can authors design wiki publishing tools and curricula to support learning among student authors? Authors suggest that wiki publishing environments can create learning opportunities that address four dimensions of authenticity: personal, real world, disciplinary, and assessment. Authors have begun a series of design studies to investigate links between wiki publishing experiences and writing-to-learn. The results of an initial study in an undergraduate government course indicate that perceived audience plays an important role in helping students monitor the quality of writing; however, students' perception of audience on the Internet is not straightforward. This preliminary iteration resulted in several guidelines that are shaping efforts to design and implement new wiki publishing tools and curricula for students and teachers.</div>Ameliahttps://wikipediaquality.com/index.php?title=Sense_and_Reference_Disambiguation_in_Wikipedia&diff=23099Sense and Reference Disambiguation in Wikipedia2020-01-04T11:02:33Z<p>Amelia: + Infobox work</p>
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<div>{{Infobox work<br />
| title = Sense and Reference Disambiguation in Wikipedia<br />
| date = 2012<br />
| authors = [[Hui Shen]]<br />[[Razvan C. Bunescu]]<br />[[Rada Mihalcea]]<br />
| link = http://www.aclweb.org/anthology/C12-2108<br />
}}<br />
'''Sense and Reference Disambiguation in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Hui Shen]], [[Razvan C. Bunescu]] and [[Rada Mihalcea]].<br />
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== Overview ==<br />
Wikipedia articles are annotated by volunteer contributors with numerous links that connect words and phrases to relevant titles in [[Wikipedia]]. In this paper, authors identify inconsistencies in the user annotation of links and show that they can have a substantial impact on the performance of word sense disambiguation systems that are trained on Wikipedia links. Authors describe two major types of link annotations ‐ sense and reference ‐ that are frequently used without being explicitly distinguished in Wikipedia, and present an approach to training sense and reference disambiguation systems in the presence of such annotation inconsistencies. Experimental results demonstrate that accounting for annotation ambiguity in Wikipedia links leads to significant improvements in disambiguation accuracy.</div>Ameliahttps://wikipediaquality.com/index.php?title=On_Using_Wikipedia_to_Build_Knowledge_Bases_for_Information_Extraction_by_Text_Segmentation&diff=23098On Using Wikipedia to Build Knowledge Bases for Information Extraction by Text Segmentation2020-01-04T11:00:08Z<p>Amelia: Embed</p>
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<div>{{Infobox work<br />
| title = On Using Wikipedia to Build Knowledge Bases for Information Extraction by Text Segmentation<br />
| date = 2011<br />
| authors = [[Elton Serra]]<br />[[Eli Cortez]]<br />[[Altigran Soares da Silva]]<br />[[Edleno Silva de Moura]]<br />
| link = https://seer.ufmg.br/index.php/jidm/article/download/140/96<br />
}}<br />
'''On Using Wikipedia to Build Knowledge Bases for Information Extraction by Text Segmentation''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Elton Serra]], [[Eli Cortez]], [[Altigran Soares da Silva]] and [[Edleno Silva de Moura]].<br />
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== Overview ==<br />
Authors propose a strategy for automatically obtaining datasets from [[Wikipedia]] to support unsupervised Information Extraction by Text Segmentation (IETS) methods. Despite the importance of preexisting datasets to unsupervised IETS methods, there has been no proper discussion in the literature on how such datasets can be effectively obtained or built. Authors report experiments in which three state-of-the-art unsupervised IETS methods use datasets obtained using proposed strategy under several configurations, involving IETS tasks on three different domains. The results suggest that strategy is valid and effective, and that IETS methods can achieve a very good performance if the datasets generated have a reasonable number of representative values on the domain of the data to be extracted.<br />
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Serra, Elton; Cortez, Eli; Silva, Altigran Soares da; Moura, Edleno Silva de. (2011). "[[On Using Wikipedia to Build Knowledge Bases for Information Extraction by Text Segmentation]]".<br />
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{{cite journal |last1=Serra |first1=Elton |last2=Cortez |first2=Eli |last3=Silva |first3=Altigran Soares da |last4=Moura |first4=Edleno Silva de |title=On Using Wikipedia to Build Knowledge Bases for Information Extraction by Text Segmentation |date=2011 |url=https://wikipediaquality.com/wiki/On_Using_Wikipedia_to_Build_Knowledge_Bases_for_Information_Extraction_by_Text_Segmentation}}<br />
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Serra, Elton; Cortez, Eli; Silva, Altigran Soares da; Moura, Edleno Silva de. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/On_Using_Wikipedia_to_Build_Knowledge_Bases_for_Information_Extraction_by_Text_Segmentation">On Using Wikipedia to Build Knowledge Bases for Information Extraction by Text Segmentation</a>&amp;quot;.<br />
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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 />
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== 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.<br />
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Frahm, Klaus M.; Shepelyansky, Dima L.. (2014). "[[Poisson Statistics of Pagerank Probabilities of Twitter and Wikipedia Networks]]". Springer Berlin Heidelberg. DOI: 10.1140/epjb/e2014-50123-4. <br />
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{{cite journal |last1=Frahm |first1=Klaus M. |last2=Shepelyansky |first2=Dima L. |title=Poisson Statistics of Pagerank Probabilities of Twitter and Wikipedia Networks |date=2014 |doi=10.1140/epjb/e2014-50123-4 |url=https://wikipediaquality.com/wiki/Poisson_Statistics_of_Pagerank_Probabilities_of_Twitter_and_Wikipedia_Networks |journal=Springer Berlin Heidelberg}}<br />
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Frahm, Klaus M.; Shepelyansky, Dima L.. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Poisson_Statistics_of_Pagerank_Probabilities_of_Twitter_and_Wikipedia_Networks">Poisson Statistics of Pagerank Probabilities of Twitter and Wikipedia Networks</a>&amp;quot;. Springer Berlin Heidelberg. DOI: 10.1140/epjb/e2014-50123-4. <br />
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<div>'''Project Management in the Wikipedia Community''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Hang Ung]] and [[Jean-Michel Dalle]].<br />
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== Overview ==<br />
A feature of online communities and notably [[Wikipedia]] is the increasing use of managerial techniques to coordinate the efforts of volunteers. In this short paper, authors explore the influence of the organization of Wikipedia in so-called projects. Authors examine the project-based coordination activity and find bursts of activity, which appear to be related to individual leadership. Using time series, authors show that coordination activity is positively correlated with contributions on articles. Finally, authors bring evidence that this positive correlation is relying on two types of coordination: group coordination, with project leadership and articles editors strongly coinciding, and directed coordination, with differentiated online roles.</div>Ameliahttps://wikipediaquality.com/index.php?title=Amplifying_the_Impact_of_Open_Access:_Wikipedia_and_the_Diffusion_of_Science&diff=23095Amplifying the Impact of Open Access: Wikipedia and the Diffusion of Science2020-01-04T10:54:30Z<p>Amelia: + embed code</p>
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<div>{{Infobox work<br />
| title = Amplifying the Impact of Open Access: Wikipedia and the Diffusion of Science<br />
| date = 2017<br />
| authors = [[Misha Teplitskiy]]<br />[[Grace Lu]]<br />[[Eamon Duede]]<br />
| doi = 10.1002/asi.23687<br />
| link = http://onlinelibrary.wiley.com/doi/10.1002/asi.23687/full<br />
| plink = http://arxiv.org/pdf/1506.07608.pdf<br />
}}<br />
'''Amplifying the Impact of Open Access: Wikipedia and the Diffusion of Science''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Misha Teplitskiy]], [[Grace Lu]] and [[Eamon Duede]].<br />
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== Overview ==<br />
With the rise of [[Wikipedia]] as a first-stop source for scientific information, it is important to understand whether Wikipedia draws upon the research that scientists value most. Here authors identify the 250 most heavily used journals in each of 26 research fields (4,721 journals, 19.4M articles) indexed by the Scopus database, and test whether topic, academic status, and accessibility make articles from these journals more or less likely to be referenced on Wikipedia. Authors find that a journal's academic status (impact factor) and accessibility (open access policy) both strongly increase the probability of it being referenced on Wikipedia. Controlling for field and impact factor, the odds that an open access journal is referenced on the [[English Wikipedia]] are 47% higher compared to paywall journals. These findings provide evidence is that a major consequence of open access policies is to significantly amplify the diffusion of science, through an intermediary like Wikipedia, to a broad audience.<br />
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Teplitskiy, Misha; Lu, Grace; Duede, Eamon. (2017). "[[Amplifying the Impact of Open Access: Wikipedia and the Diffusion of Science]]".DOI: 10.1002/asi.23687. <br />
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{{cite journal |last1=Teplitskiy |first1=Misha |last2=Lu |first2=Grace |last3=Duede |first3=Eamon |title=Amplifying the Impact of Open Access: Wikipedia and the Diffusion of Science |date=2017 |doi=10.1002/asi.23687 |url=https://wikipediaquality.com/wiki/Amplifying_the_Impact_of_Open_Access:_Wikipedia_and_the_Diffusion_of_Science}}<br />
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Teplitskiy, Misha; Lu, Grace; Duede, Eamon. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Amplifying_the_Impact_of_Open_Access:_Wikipedia_and_the_Diffusion_of_Science">Amplifying the Impact of Open Access: Wikipedia and the Diffusion of Science</a>&amp;quot;.DOI: 10.1002/asi.23687. <br />
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<div>{{Infobox work<br />
| title = Media on Wikipedia: a Discourse Analysis of National Professional Journals<br />
| date = 2011<br />
| authors = [[Bodil Almroth]]<br />[[Sofia Tenglin]]<br />[[Helena Francke]]<br />
| link = http://bada.hb.se:80/bitstream/2320/7995/1/11-11.pdf<br />
}}<br />
'''Media on Wikipedia: a Discourse Analysis of National Professional Journals''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Bodil Almroth]], [[Sofia Tenglin]] and [[Helena Francke]].<br />
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== Overview ==<br />
The aim of this Master’s thesis is to examine how librarians, information specialists and teachers discuss [[Wikipedia]] within national (Swedish) professional journals. Questions asked in the study are: How is Wikipedia perceived in the professional journals? What different positions do writers and commentators take in relation to Wikipedia? 133 articles from 31 different professional journals in the period from 2001 to the middle of 2010 were analysed. The theory and method used is Laclau’s and Mouffe’s discourse theory from which authors created own model with sex different steps.<br />
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Almroth, Bodil; Tenglin, Sofia; Francke, Helena. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Media_on_Wikipedia:_a_Discourse_Analysis_of_National_Professional_Journals">Media on Wikipedia: a Discourse Analysis of National Professional Journals</a>&amp;quot;.<br />
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<div>{{Infobox work<br />
| title = Towards Tailored Semantic Annotation Systems from Wikipedia<br />
| date = 2011<br />
| authors = [[Shahad Kudama]]<br />[[Rafael Berlanga Llavori]]<br />[[Lisette García-Moya]]<br />[[Victoria Nebot]]<br />[[María José Aramburu Cabo]]<br />
| doi = 10.1109/DEXA.2011.82<br />
| link = http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6059863<br />
}}<br />
'''Towards Tailored Semantic Annotation Systems from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Shahad Kudama]], [[Rafael Berlanga Llavori]], [[Lisette García-Moya]], [[Victoria Nebot]] and [[María José Aramburu Cabo]].<br />
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== Overview ==<br />
The annotation of texts in natural language links some terms of the text to an external information source that gives us more detailed information about them. Most of the approaches made in this field get any text and annotate it by trying to find out the context of each term, as there are terms that have different meanings depending on the topic treated. In this article, authors propose a variant of this process that annotates a text knowing in advance its context. The external source of information used is [[Wikipedia]] and authors extract and use a fragment of it that embraces all the terms related to the context known beforehand.</div>Ameliahttps://wikipediaquality.com/index.php?title=Miracle_at_the_Spanish_Wiqa_Pilot:_Using_Named_Entities_and_Cosine_Similarity_to_Extend_Wikipedia_Articles&diff=23092Miracle at the Spanish Wiqa Pilot: Using Named Entities and Cosine Similarity to Extend Wikipedia Articles2020-01-04T10:48:55Z<p>Amelia: New study: Miracle at the Spanish Wiqa Pilot: Using Named Entities and Cosine Similarity to Extend Wikipedia Articles</p>
<hr />
<div>'''Miracle at the Spanish Wiqa Pilot: Using Named Entities and Cosine Similarity to Extend Wikipedia Articles''' - scientific work related to Wikipedia quality published in 2006, written by César de Pablo-Sánchez, José Luis Martínez-Fernández and Paloma Martínez.<br />
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== Overview ==<br />
The WiQA pilot task explores how to select new and useful information that could be included in Wikipedia articles. Authors system explores how the combination of NE and cosine similarity allow to detect new and repeated information. Authors have submitted two runs for the Spanish subtask wich differ in the way they select candidate sentences using the link structure in the WikipediaXML corpus. Authors approach obtains results that provide at least a new snippet per topic in average. The main limitation was found in the candidate selection strategy that results in some topics being not answered or in other cases providing too much noisy candidates.</div>Ameliahttps://wikipediaquality.com/index.php?title=Clustering_of_Scientific_Citations_in_Wikipedia&diff=23091Clustering of Scientific Citations in Wikipedia2020-01-04T10:46:51Z<p>Amelia: Adding infobox</p>
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<div>{{Infobox work<br />
| title = Clustering of Scientific Citations in Wikipedia<br />
| date = 2008<br />
| authors = [[Finn Aarup Nielsen]]<br />
| link = http://www.jmir.org/article/viewFile/jmir_v17i3e62/2<br />
| plink = https://arxiv.org/abs/0805.1154<br />
}}<br />
'''Clustering of Scientific Citations in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Finn Aarup Nielsen]].<br />
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== Overview ==<br />
The instances of templates in [[Wikipedia]] form an interesting data set of [[structured information]]. Here Author focus on the cite journal template that is primarily used for citation to articles in scientific journals. These citations can be extracted and analyzed: Non-negative matrix factorization is performed on a (article x journal) matrix resulting in a soft clustering of Wikipedia articles and scientific journals, each cluster more or less representing a scientific topic.</div>Ameliahttps://wikipediaquality.com/index.php?title=A_Comparison_of_Automatic_Search_Query_Enhancement_Algorithms_That_Utilise_Wikipedia_as_a_Source_of_a_Priori_Knowledge&diff=23090A Comparison of Automatic Search Query Enhancement Algorithms That Utilise Wikipedia as a Source of a Priori Knowledge2020-01-04T10:45:36Z<p>Amelia: Creating a page: A Comparison of Automatic Search Query Enhancement Algorithms That Utilise Wikipedia as a Source of a Priori Knowledge</p>
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<div>'''A Comparison of Automatic Search Query Enhancement Algorithms That Utilise Wikipedia as a Source of a Priori Knowledge''' - scientific work related to Wikipedia quality published in 2017, written by Kyle Goslin and Markus Hofmann.<br />
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== Overview ==<br />
This paper describes the benchmarking and analysis of five Automatic Search Query Enhancement (ASQE) algorithms that utilise Wikipedia as the sole source for a priori knowledge. The contributions of this paper include: 1) A comprehensive review into current ASQE algorithms that utilise Wikipedia as the sole source for a priori knowledge; 2) benchmarking of five existing ASQE algorithms using the TREC-9 Web Topics on the ClueWeb12 data set and 3) analysis of the results from the benchmarking process to identify the strengths and weaknesses each algorithm. During the benchmarking process, 2,500 relevance assessments were performed. Results of these tests are analysed using the Average Precision @10 per query and Mean Average Precision @10 per algorithm. From this analysis authors show that the scope of a priori knowledge utilised during enhancement and the available term weighting methods available from Wikipedia can further aid the ASQE process. Although approaches taken by the algorithms are still relevant, an over dependence on weighting schemes and data sources used can easily impact results of an ASQE algorithm.</div>Ameliahttps://wikipediaquality.com/index.php?title=Searching_for_Interestingness_in_Wikipedia_and_Yahoo!:_Answers&diff=23089Searching for Interestingness in Wikipedia and Yahoo!: Answers2020-01-04T10:42:36Z<p>Amelia: Int.links</p>
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<div>'''Searching for Interestingness in Wikipedia and Yahoo!: Answers''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Yelena Mejova]], [[Ilaria Bordino]], [[Mounia Lalmas]] and [[Aristides Gionis]].<br />
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== Overview ==<br />
In many cases, when browsing the Web, users are searching for specific information. Sometimes, though, users are also looking for something interesting, surprising, or entertaining. Serendipitous search puts interestingness on par with relevance. Authors investigate how interesting are the results one can obtain via serendipitous search, and what makes them so, by comparing entity networks extracted from two prominent social media sites, [[Wikipedia]] and [[Yahoo]]! Answers.</div>Ameliahttps://wikipediaquality.com/index.php?title=Computing_Semantic_Relatedness_Using_Wikipedia-Based_Explicit_Semantic_Analysis&diff=23088Computing Semantic Relatedness Using Wikipedia-Based Explicit Semantic Analysis2020-01-04T10:40:41Z<p>Amelia: Adding categories</p>
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<div>{{Infobox work<br />
| title = Computing Semantic Relatedness Using Wikipedia-Based Explicit Semantic Analysis<br />
| date = 2007<br />
| authors = [[Evgeniy Gabrilovich]]<br />[[Shaul Markovitch]]<br />
| link = http://dl.acm.org/citation.cfm?id=1625275.1625535<br />
| plink = https://www.researchgate.net/profile/Shaul_Markovitch/publication/200042392_Computing_Semantic_Relatedness_using_Wikipedia-based_Explicit_Semantic_Analysis/links/54c508200cf219bbe4f22103.pdf<br />
}}<br />
'''Computing Semantic Relatedness Using Wikipedia-Based Explicit Semantic Analysis''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Evgeniy Gabrilovich]] and [[Shaul Markovitch]].<br />
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== Overview ==<br />
Computing semantic [[relatedness]] of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. Authors propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from [[Wikipedia]]. Authors use machine learning techniques to explicitly represent the meaning of any text as a weighted vector of Wikipedia-based concepts. Assessing the relatedness of texts in this space amounts to comparing the corresponding vectors using conventional metrics (e.g., cosine). Compared with the previous state of the art, using ESA results in substantial improvements in correlation of computed relatedness scores with human judgments: from r = 0.56 to 0.75 for individual words and from r = 0.60 to 0.72 for texts. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.<br />
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Gabrilovich, Evgeniy; Markovitch, Shaul. (2007). "[[Computing Semantic Relatedness Using Wikipedia-Based Explicit Semantic Analysis]]". Morgan Kaufmann Publishers Inc.. <br />
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{{cite journal |last1=Gabrilovich |first1=Evgeniy |last2=Markovitch |first2=Shaul |title=Computing Semantic Relatedness Using Wikipedia-Based Explicit Semantic Analysis |date=2007 |url=https://wikipediaquality.com/wiki/Computing_Semantic_Relatedness_Using_Wikipedia-Based_Explicit_Semantic_Analysis |journal=Morgan Kaufmann Publishers Inc.}}<br />
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Gabrilovich, Evgeniy; Markovitch, Shaul. (2007). &amp;quot;<a href="https://wikipediaquality.com/wiki/Computing_Semantic_Relatedness_Using_Wikipedia-Based_Explicit_Semantic_Analysis">Computing Semantic Relatedness Using Wikipedia-Based Explicit Semantic Analysis</a>&amp;quot;. Morgan Kaufmann Publishers Inc.. <br />
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[[Category:Scientific works]]</div>Ameliahttps://wikipediaquality.com/index.php?title=Agglomerationen_Mit_Nutzergenerierten_Inhalten_Neu_Definiert_Visualisierung_Der_Nordostschweiz_Mithilfe_Von_Wikipedia&diff=23087Agglomerationen Mit Nutzergenerierten Inhalten Neu Definiert Visualisierung Der Nordostschweiz Mithilfe Von Wikipedia2020-01-04T10:37:52Z<p>Amelia: Embed</p>
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<div>{{Infobox work<br />
| title = Agglomerationen Mit Nutzergenerierten Inhalten Neu Definiert Visualisierung Der Nordostschweiz Mithilfe Von Wikipedia<br />
| date = 2013<br />
| authors = [[André Bruggmann]]<br />[[Marco M. Salvini]]<br />[[Sara Irina Fabrikant]]<br />
| doi = 10.1080/02513625.2013.892789<br />
| link = http://www.tandfonline.com/doi/ref/10.1080/02513625.2013.892789<br />
}}<br />
'''Agglomerationen Mit Nutzergenerierten Inhalten Neu Definiert Visualisierung Der Nordostschweiz Mithilfe Von Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[André Bruggmann]], [[Marco M. Salvini]] and [[Sara Irina Fabrikant]].<br />
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== Overview ==<br />
Commuters have had an important role in shaping the spatial organization of Switzerland, as commuter flows have been one of the most significant criteria to delineate urban agglomeration zones. Even though urban areas and respective agglomerations have continuously gained in importance in Switzerland to this day, the Swiss national population census will no longer include commuter data at high spatial resolution. Hence, the definition of the rapidly evolving urban agglomeration concept will have to be modified for future urban research and planning purposes.Authors propose a crowdsourcing approach to overcome this data gap, and employ the open and web-based [[Wikipedia]] encyclopedia as a new resource to delineate agglomeration areas. Using the North Eastern parts of Switzerland in this case study, authors systematically evaluate whether user-generated content can serve as an option to fill the commuter data gap in future Swiss national population censuses to define agglomeration areas. In a second step, authors evaluate th...<br />
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Bruggmann, André; Salvini, Marco M.; Fabrikant, Sara Irina. (2013). "[[Agglomerationen Mit Nutzergenerierten Inhalten Neu Definiert Visualisierung Der Nordostschweiz Mithilfe Von Wikipedia]]". Routledge. DOI: 10.1080/02513625.2013.892789. <br />
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{{cite journal |last1=Bruggmann |first1=André |last2=Salvini |first2=Marco M. |last3=Fabrikant |first3=Sara Irina |title=Agglomerationen Mit Nutzergenerierten Inhalten Neu Definiert Visualisierung Der Nordostschweiz Mithilfe Von Wikipedia |date=2013 |doi=10.1080/02513625.2013.892789 |url=https://wikipediaquality.com/wiki/Agglomerationen_Mit_Nutzergenerierten_Inhalten_Neu_Definiert_Visualisierung_Der_Nordostschweiz_Mithilfe_Von_Wikipedia |journal=Routledge}}<br />
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Bruggmann, André; Salvini, Marco M.; Fabrikant, Sara Irina. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Agglomerationen_Mit_Nutzergenerierten_Inhalten_Neu_Definiert_Visualisierung_Der_Nordostschweiz_Mithilfe_Von_Wikipedia">Agglomerationen Mit Nutzergenerierten Inhalten Neu Definiert Visualisierung Der Nordostschweiz Mithilfe Von Wikipedia</a>&amp;quot;. Routledge. DOI: 10.1080/02513625.2013.892789. <br />
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</code></div>Ameliahttps://wikipediaquality.com/index.php?title=Research_Guides:_Art_%2B_Feminism_Wikipedia_Edit-A-Thon:_Welcome!&diff=23086Research Guides: Art + Feminism Wikipedia Edit-A-Thon: Welcome!2020-01-04T10:35:31Z<p>Amelia: + cat.</p>
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<div>{{Infobox work<br />
| title = Research Guides: Art + Feminism Wikipedia Edit-A-Thon: Welcome!<br />
| date = 2016<br />
| authors = [[Amanda Meeks]]<br />
| link = https://libraryguides.nau.edu/artplusfeminism<br />
}}<br />
'''Research Guides: Art + Feminism Wikipedia Edit-A-Thon: Welcome!''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Amanda Meeks]].<br />
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== Overview ==<br />
This guide provides resources related to [[Wikipedia]] editing, woman-identified artists, and art history in order to support the Art+Feminism efforts to address the gender gap on Wikipedia.<br />
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{{cite journal |last1=Meeks |first1=Amanda |title=Research Guides: Art + Feminism Wikipedia Edit-A-Thon: Welcome! |date=2016 |url=https://wikipediaquality.com/wiki/Research_Guides:_Art_+_Feminism_Wikipedia_Edit-A-Thon:_Welcome!}}<br />
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Meeks, Amanda. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Research_Guides:_Art_+_Feminism_Wikipedia_Edit-A-Thon:_Welcome!">Research Guides: Art + Feminism Wikipedia Edit-A-Thon: Welcome!</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Ameliahttps://wikipediaquality.com/index.php?title=Argumentation_and_Conflict_Management_in_Online_Epistemic_Communities:_a_Narrative_Approach_to_Wikipedia_Debates&diff=23085Argumentation and Conflict Management in Online Epistemic Communities: a Narrative Approach to Wikipedia Debates2020-01-04T10:34:13Z<p>Amelia: + embed code</p>
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<div>{{Infobox work<br />
| title = Argumentation and Conflict Management in Online Epistemic Communities: a Narrative Approach to Wikipedia Debates<br />
| date = 2017<br />
| authors = [[Michael Baker]]<br />[[Flore Barcellini]]<br />
| doi = 10.1007/978-3-319-59084-4_7<br />
| link = https://link.springer.com/content/pdf/10.1007%2F978-3-319-59084-4_7.pdf<br />
}}<br />
'''Argumentation and Conflict Management in Online Epistemic Communities: a Narrative Approach to Wikipedia Debates''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Michael Baker]] and [[Flore Barcellini]].<br />
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== Overview ==<br />
With the rise of Internet-based technologies, new web-based communities of practice have emerged, that authors term online epistemic communities, or “OECs”, whose raison d’etre is the co-creation of knowledge objects such as [[open-source]] programming languages or encyclopaedias (for example, [[Wikipedia]]). In this chapter authors focus on the case of Wikipedia, where general public participation has recently grown very quickly, in part due to egalitarian principles that encourage free participation by everyone. However, widespread participation, coupled with the principle of neutrality of viewpoint, has led to “editing wars” (repeated text deletions and “reverts”, now largely controlled by “(ro)bots”). The nature of participation has tended to change over time, with a migration of conflicts to discussion pages, especially in the case of articles on contentious issues (e.g. “The Turin Shroud”). Authors aim is to describe the characteristics of such OEC debates, in relation to their contexts and potential for effective knowledge elaboration. Authors describe an approach to studying argumentation practices in OECs based on articulating third-person (researcher) analyses, based on a pragma-dialectic model extended to include dimensions of knowledge elaboration and interpersonal relations, with a first-person (participant) perspective, where key contributors to controversial articles produced narratives on their ‘life cycles’. On the basis of two case-study discussions authors show that although debates are mostly epistemic, concerning article content and structure, the possibilities of anonymity and completely open participation also lead to disputes on an interpersonal (ad hominem) level, concerning expertise. Authors conclude with prospects for rendering OEC debates more constructive and productive.<br />
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Baker, Michael; Barcellini, Flore. (2017). "[[Argumentation and Conflict Management in Online Epistemic Communities: a Narrative Approach to Wikipedia Debates]]". Springer, Cham. DOI: 10.1007/978-3-319-59084-4_7. <br />
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{{cite journal |last1=Baker |first1=Michael |last2=Barcellini |first2=Flore |title=Argumentation and Conflict Management in Online Epistemic Communities: a Narrative Approach to Wikipedia Debates |date=2017 |doi=10.1007/978-3-319-59084-4_7 |url=https://wikipediaquality.com/wiki/Argumentation_and_Conflict_Management_in_Online_Epistemic_Communities:_a_Narrative_Approach_to_Wikipedia_Debates |journal=Springer, Cham}}<br />
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Baker, Michael; Barcellini, Flore. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Argumentation_and_Conflict_Management_in_Online_Epistemic_Communities:_a_Narrative_Approach_to_Wikipedia_Debates">Argumentation and Conflict Management in Online Epistemic Communities: a Narrative Approach to Wikipedia Debates</a>&amp;quot;. Springer, Cham. DOI: 10.1007/978-3-319-59084-4_7. <br />
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</code></div>Ameliahttps://wikipediaquality.com/index.php?title=Networked_Expertise_in_the_Era_of_Many-To-Many_Communication:_on_Wikipedia_and_Invention&diff=22748Networked Expertise in the Era of Many-To-Many Communication: on Wikipedia and Invention2019-12-09T06:05:48Z<p>Amelia: Wikilinks</p>
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<div>'''Networked Expertise in the Era of Many-To-Many Communication: on Wikipedia and Invention''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Damien Smith Pfister]].<br />
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== Overview ==<br />
This essay extends the observations made in E. Johanna Hartelius’ The rhetoric of expertise about the nature of expertise in digital contexts. Author argue that digital media introduce a scale of communication—many-to-many—that reshapes how the invention of knowledge occurs. By examining how knowledge production on [[Wikipedia]] occurs, Author illustrate how many-to-many communication introduces a new model of “participatory expertise.” This model of participatory expertise challenges traditional information routines by elevating procedural expertise over subject matter expertise and opening up knowledge production to the many. Additionally, by hosting multiperspectival conversations on Wikipedia, the participatory model of expertise introduces epistemic turbulence into traditionally tranquil encyclopedia culture.</div>Ameliahttps://wikipediaquality.com/index.php?title=Wikipedia_as_Rational_Discourse:_an_Illustration_of_the_Emancipatory_Potential_of_Information_Systems&diff=22747Wikipedia as Rational Discourse: an Illustration of the Emancipatory Potential of Information Systems2019-12-09T06:03:22Z<p>Amelia: + wikilinks</p>
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<div>'''Wikipedia as Rational Discourse: an Illustration of the Emancipatory Potential of Information Systems''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Sean W. Hansen]], [[Nicholas Berente]] and [[Kalle Lyytinen]].<br />
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== Overview ==<br />
Critical social theorists often emphasize the control and surveillance aspects of information systems, building upon a characterization of information technology as a tool for increased rationalization. The emancipatory potential of information systems is often overlooked. In this paper, authors apply the Habermasian ideal of rational discourse to [[Wikipedia]] as an illustration of the emancipatory potential of information systems. Authors conclude that Wikipedia does embody an approximation of rational discourse, while several challenges remain</div>Ameliahttps://wikipediaquality.com/index.php?title=Preferential_Attachment_in_the_Growth_of_Social_Networks:_the_Internet_Encyclopedia_Wikipedia&diff=22746Preferential Attachment in the Growth of Social Networks: the Internet Encyclopedia Wikipedia2019-12-09T06:00:26Z<p>Amelia: + links</p>
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<div>'''Preferential Attachment in the Growth of Social Networks: the Internet Encyclopedia Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2006, written by [[Andrea Capocci]], [[Vito D. P. Servedio]], [[Francesca Colaiori]], [[Luciana S. Buriol]], [[Debora Donato]], [[Stefano Leonardi]] and [[Guido Caldarelli]].<br />
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== Overview ==<br />
Authors present an analysis of the statistical properties and growth of the free on-line encyclopedia [[Wikipedia]]. By describing topics by vertices and hyperlinks between them as edges, authors can represent this encyclopedia as a directed graph. The topological properties of this graph are in close analogy with those of the World Wide Web, despite the very different growth mechanism. In particular, authors measure a scale-invariant distribution of the in and out degree and authors are able to reproduce these [[features]] by means of a simple statistical model. As a major consequence, Wikipedia growth can be described by local rules such as the preferential attachment mechanism, though users, who are responsible of its evolution, can act globally on the network.</div>Ameliahttps://wikipediaquality.com/index.php?title=Expanding_Textual_Entailment_Corpora_Fromwikipedia_Using_Co-Training&diff=22745Expanding Textual Entailment Corpora Fromwikipedia Using Co-Training2019-12-09T05:58:42Z<p>Amelia: Category</p>
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<div>{{Infobox work<br />
| title = Expanding Textual Entailment Corpora Fromwikipedia Using Co-Training<br />
| date = 2010<br />
| authors = [[Fabio Massimo Zanzotto]]<br />[[Marco Pennacchiotti]]<br />
| link = http://art.uniroma2.it/zanzotto/publications/2010_wsCOLING_ZanzottoPennacchiotti.pdf<br />
}}<br />
'''Expanding Textual Entailment Corpora Fromwikipedia Using Co-Training''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Fabio Massimo Zanzotto]] and [[Marco Pennacchiotti]].<br />
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== Overview ==<br />
In this paper authors propose a novel method to automatically extract large textual entailment datasets homogeneous to existing ones. The key idea is the combination of two intuitions: (1) the use of [[Wikipedia]] to extract a large set of textual entailment pairs; (2) the application of semisupervised machine learning methods to make the extracted dataset homogeneous to the existing ones. Authors report empirical evidence that method successfully expands existing textual entailment corpora.<br />
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Zanzotto, Fabio Massimo; Pennacchiotti, Marco. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Expanding_Textual_Entailment_Corpora_Fromwikipedia_Using_Co-Training">Expanding Textual Entailment Corpora Fromwikipedia Using Co-Training</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Ameliahttps://wikipediaquality.com/index.php?title=Wikipedia_and_the_Emergence_of_Dialogic_Expertise&diff=22744Wikipedia and the Emergence of Dialogic Expertise2019-12-09T05:57:19Z<p>Amelia: wikilinks</p>
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<div>'''Wikipedia and the Emergence of Dialogic Expertise''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[E. Johanna Hartelius]].<br />
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== Overview ==<br />
Wikipedia's popularity as an online encyclopedia calls attention to fundamental assumptions about the management and dissemination of information. Drawing on a Bakhtinian framework, this article posits a model of dialogic expertise. Specifically, it argues that, by facilitating an ongoing chain of interdependent and multivocal “utterances,” [[Wikipedia]] challenges traditional “monologic” expertise. Nonetheless, the site's purportedly democratic defiance of knowledge elites (of encyclopedic publishing, academe, etc.) is compromised by the establishment of a “technocratic” hierarchy. Implications extend to the scholarly debate surrounding dialogue and rhetoric and to understanding of Wikipedia's success in the context of a cultural anxiety—Americans are at once dependent on an extensive system of experts and uneasy about the deferential distribution of power within that system.</div>Ameliahttps://wikipediaquality.com/index.php?title=Opengeist:_Insight_in_the_Stream_of_Page_Views_on_Wikipedia&diff=22743Opengeist: Insight in the Stream of Page Views on Wikipedia2019-12-09T05:54:56Z<p>Amelia: wikilinks</p>
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<div>'''Opengeist: Insight in the Stream of Page Views on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Maria-Hendrike Peetz]], [[Edgar Meij]] and [[M. de Rijke]].<br />
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== Overview ==<br />
Authors present a RESTful interface that captures insights into the zeitgeist of [[Wikipedia]] users. The system is an interface for clustering and comparing concepts based on the time series of the number of views of their Wikipedia page. The functionality is motivated by three use cases, ranging from technical novice to expert user and authors also provide two real-life example applications.</div>Ameliahttps://wikipediaquality.com/index.php?title=Wikipedia_and_Neurological_Disorders&diff=22742Wikipedia and Neurological Disorders2019-12-09T05:52:08Z<p>Amelia: Wikilinks</p>
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<div>'''Wikipedia and Neurological Disorders''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Francesco Brigo]], [[Stanley C. Igwe]], [[Raffaele Nardone]], [[Piergiorgio Lochner]], [[Frediano Tezzon]] and [[Willem M. Otte]].<br />
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== Overview ==<br />
Abstract Authors aim was to evaluate [[Wikipedia]] page visits in relation to the most common neurological disorders by determining which factors are related to peaks in Wikipedia searches for these conditions. Millions of people worldwide use the internet daily as a source of health information. Wikipedia is a popular free online encyclopedia used by patients and physicians to search for health-related information. The following Wikipedia articles were considered: Alzheimer’s disease; Amyotrophic lateral sclerosis; Dementia; Epilepsy; Epileptic seizure; Migraine; Multiple sclerosis; Parkinson’s disease; Stroke; Traumatic brain injury. Authors analyzed information regarding the total article views for 90 days and the rank of these articles among all those available in Wikipedia. Authors determined the highest search volume peaks to identify possible relation with online news headlines. No relation between incidence or prevalence of neurological disorders and the search volume for the related articles was found. Seven out of 10 neurological conditions showed relations in search volume peaks and news headlines. Six out of these seven peaks were related to news about famous people suffering from neurological disorders, especially those from showbusiness. Identification of discrepancies between disease burden and health seeking behavior on Wikipedia is useful in the planning of public health campaigns. Celebrities who publicly announce their neurological diagnosis might effectively promote awareness programs, increase public knowledge and reduce stigma related to diagnoses of neurological disorders.</div>Ameliahttps://wikipediaquality.com/index.php?title=A_Latent_Space_Analysis_of_Editor_Lifecycles_in_Wikipedia&diff=22741A Latent Space Analysis of Editor Lifecycles in Wikipedia2019-12-09T05:49:35Z<p>Amelia: Adding categories</p>
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<div>{{Infobox work<br />
| title = A Latent Space Analysis of Editor Lifecycles in Wikipedia<br />
| date = 2015<br />
| authors = [[Xiangju Qin]]<br />[[Derek Greene]]<br />[[Pádraig Cunningham]]<br />
| doi = 10.1007/978-3-319-29009-6_3<br />
| link = https://dl.acm.org/citation.cfm?id=2950241<br />
| plink = https://arxiv.org/abs/1407.7736<br />
}}<br />
'''A Latent Space Analysis of Editor Lifecycles in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Xiangju Qin]], [[Derek Greene]] and [[Pádraig Cunningham]].<br />
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== Overview ==<br />
Collaborations such as [[Wikipedia]] are a key part of the value of the modern Internet. At the same time there is concern that these collaborations are threatened by high levels of member withdrawal. In this paper authors borrow ideas from topic analysis to study editor activity on Wikipedia over time using latent space analysis, which offers an insight into the evolving patterns of editor behaviour. This latent space representation reveals a number of different [[categories]] of editor e.g. Technical Experts, Social Networkers and authors show that it does provide a signal that predicts an editor's departure from the community. Authors also show that long term editors generally have more diversified edit preference and experience relatively soft evolution in their editor profiles, while short term editors generally distribute their contribution at random among the namespaces and categories of articles and experience considerable fluctuation in the evolution of their editor profiles.<br />
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Qin, Xiangju; Greene, Derek; Cunningham, Pádraig. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/A_Latent_Space_Analysis_of_Editor_Lifecycles_in_Wikipedia">A Latent Space Analysis of Editor Lifecycles in Wikipedia</a>&amp;quot;. Springer, Cham. DOI: 10.1007/978-3-319-29009-6_3. <br />
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[[Category:Scientific works]]</div>Ameliahttps://wikipediaquality.com/index.php?title=Building_a_Standpoints_Web_to_Support_Decision-Making_in_Wikipedia&diff=22740Building a Standpoints Web to Support Decision-Making in Wikipedia2019-12-09T05:47:26Z<p>Amelia: cat.</p>
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<div>{{Infobox work<br />
| title = Building a Standpoints Web to Support Decision-Making in Wikipedia<br />
| date = 2012<br />
| authors = [[Jodi Schneider]]<br />
| doi = 10.1145/2141512.2141614<br />
| link = http://dl.acm.org/ft_gateway.cfm?id=2141614&amp;type=pdf<br />
}}<br />
'''Building a Standpoints Web to Support Decision-Making in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Jodi Schneider]].<br />
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== Overview ==<br />
Although the Web enables large-scale collaboration, its potential to support group decision-making has not been fully exploited. My research aims to analyze, extract, and represent disagreement in purposeful social web conversations. This supports decision-making in distributed groups by representing individuals' claims and their justifications in a "Standpoints Web", a hypertext web interlinking the claims and justifications made throughout the social web. The two main contributions of my dissertation are an architecture for the Standpoints Web and a case study implementing the Standpoints Web for [[Wikipedia]]'s deletion discussions.<br />
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Schneider, Jodi. (2012). "[[Building a Standpoints Web to Support Decision-Making in Wikipedia]]".DOI: 10.1145/2141512.2141614. <br />
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{{cite journal |last1=Schneider |first1=Jodi |title=Building a Standpoints Web to Support Decision-Making in Wikipedia |date=2012 |doi=10.1145/2141512.2141614 |url=https://wikipediaquality.com/wiki/Building_a_Standpoints_Web_to_Support_Decision-Making_in_Wikipedia}}<br />
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Schneider, Jodi. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Building_a_Standpoints_Web_to_Support_Decision-Making_in_Wikipedia">Building a Standpoints Web to Support Decision-Making in Wikipedia</a>&amp;quot;.DOI: 10.1145/2141512.2141614. <br />
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[[Category:Scientific works]]</div>Ameliahttps://wikipediaquality.com/index.php?title=Computing_Semantic_Relatedness_from_Human_Navigational_Paths:_a_Case_Study_on_Wikipedia&diff=22739Computing Semantic Relatedness from Human Navigational Paths: a Case Study on Wikipedia2019-12-09T05:44:37Z<p>Amelia: + categories</p>
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<div>{{Infobox work<br />
| title = Computing Semantic Relatedness from Human Navigational Paths: a Case Study on Wikipedia<br />
| date = 2013<br />
| authors = [[Philipp Singer]]<br />[[Thomas Niebler]]<br />[[Markus Strohmaier]]<br />[[Andreas Hotho]]<br />
| doi = 10.4018/ijswis.2013100103<br />
| link = https://www.igi-global.com/article/computing-semantic-relatedness-from-human-navigational-paths-a-case-study-on-wikipedia/102707<br />
}}<br />
'''Computing Semantic Relatedness from Human Navigational Paths: a Case Study on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Philipp Singer]], [[Thomas Niebler]], [[Markus Strohmaier]] and [[Andreas Hotho]].<br />
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== Overview ==<br />
In this article, the authors present a novel approach for computing semantic [[relatedness]] and conduct a large-scale study of it on [[Wikipedia]]. Unlike existing semantic analysis methods that utilize Wikipedia's content or link structure, the authors propose to use human navigational paths on Wikipedia for this task. The authors obtain 1.8 million human navigational paths from a semi-controlled navigation experiment-a Wikipedia-based navigation game, in which users are required to find short paths between two articles in a given Wikipedia article network. The authors' results are intriguing: They suggest that i semantic relatedness computed from human navigational paths may be more precise than semantic relatedness computed from Wikipedia's plain link structure alone and ii that not all navigational paths are equally useful. Intelligent selection based on path characteristics can improve accuracy. The authors' work makes an argument for expanding the existing arsenal of data sources for calculating semantic relatedness and to consider the utility of human navigational paths for this task.<br />
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Singer, Philipp; Niebler, Thomas; Strohmaier, Markus; Hotho, Andreas. (2013). "[[Computing Semantic Relatedness from Human Navigational Paths: a Case Study on Wikipedia]]". IGI Global. DOI: 10.4018/ijswis.2013100103. <br />
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{{cite journal |last1=Singer |first1=Philipp |last2=Niebler |first2=Thomas |last3=Strohmaier |first3=Markus |last4=Hotho |first4=Andreas |title=Computing Semantic Relatedness from Human Navigational Paths: a Case Study on Wikipedia |date=2013 |doi=10.4018/ijswis.2013100103 |url=https://wikipediaquality.com/wiki/Computing_Semantic_Relatedness_from_Human_Navigational_Paths:_a_Case_Study_on_Wikipedia |journal=IGI Global}}<br />
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Singer, Philipp; Niebler, Thomas; Strohmaier, Markus; Hotho, Andreas. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Computing_Semantic_Relatedness_from_Human_Navigational_Paths:_a_Case_Study_on_Wikipedia">Computing Semantic Relatedness from Human Navigational Paths: a Case Study on Wikipedia</a>&amp;quot;. IGI Global. DOI: 10.4018/ijswis.2013100103. <br />
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[[Category:Scientific works]]</div>Ameliahttps://wikipediaquality.com/index.php?title=Semantic_Wikipedia&diff=22738Semantic Wikipedia2019-12-09T05:42:43Z<p>Amelia: + Embed</p>
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<div>{{Infobox work<br />
| title = Semantic Wikipedia<br />
| date = 2006<br />
| authors = [[Max Völkel]]<br />[[Markus Krötzsch]]<br />[[Denny Vrandecic]]<br />[[Heiko Haller]]<br />[[Rudi Studer]]<br />
| doi = 10.1145/1135777.1135863<br />
| link = https://dl.acm.org/citation.cfm?id=1135863<br />
}}<br />
'''Semantic Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2006, written by [[Max Völkel]], [[Markus Krötzsch]], [[Denny Vrandecic]], [[Heiko Haller]] and [[Rudi Studer]].<br />
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== Overview ==<br />
Wikipedia is the world's largest collaboratively edited source of encyclopaedic knowledge. But in spite of its utility, its contents are barely machine-interpretable. Structural knowledge, e.,g. about how concepts are interrelated, can neither be formally stated nor automatically processed. Also the wealth of numerical data is only available as plain text and thus can not be processed by its actual meaning.Authors provide an extension to be integrated in [[Wikipedia]], that allows the typing of links between articles and the specification of typed data inside the articles in an easy-to-use manner.Enabling even casual users to participate in the creation of an open [[semantic knowledge]] base, Wikipedia has the chance to become a resource of semantic statements, hitherto unknown regarding size, scope, openness, and internationalisation. These semantic enhancements bring to Wikipedia benefits of today's semantic technologies: more specific ways of searching and browsing. Also, the RDF export, that gives direct access to the formalised knowledge, opens Wikipedia up to a wide range of external applications, that will be able to use it as a background knowledge base.In this paper, authors present the design, implementation, and possible uses of this extension.<br />
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Völkel, Max; Krötzsch, Markus; Vrandecic, Denny; Haller, Heiko; Studer, Rudi. (2006). "[[Semantic Wikipedia]]".DOI: 10.1145/1135777.1135863. <br />
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{{cite journal |last1=Völkel |first1=Max |last2=Krötzsch |first2=Markus |last3=Vrandecic |first3=Denny |last4=Haller |first4=Heiko |last5=Studer |first5=Rudi |title=Semantic Wikipedia |date=2006 |doi=10.1145/1135777.1135863 |url=https://wikipediaquality.com/wiki/Semantic_Wikipedia}}<br />
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Völkel, Max; Krötzsch, Markus; Vrandecic, Denny; Haller, Heiko; Studer, Rudi. (2006). &amp;quot;<a href="https://wikipediaquality.com/wiki/Semantic_Wikipedia">Semantic Wikipedia</a>&amp;quot;.DOI: 10.1145/1135777.1135863. <br />
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</code></div>Ameliahttps://wikipediaquality.com/index.php?title=Integration_of_Knowledge_on_Wikipedia_and_Other_Web_Resources&diff=22737Integration of Knowledge on Wikipedia and Other Web Resources2019-12-09T05:39:55Z<p>Amelia: Infobox work</p>
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<div>{{Infobox work<br />
| title = Integration of Knowledge on Wikipedia and Other Web Resources<br />
| date = 2011<br />
| authors = [[Eklou Damien]]<br />[[Yasuhito Asano]]<br />[[Masatoshi Yoshikawa]]<br />
| link = http://db-event.jpn.org/deim2011/proceedings/pdf/f3-3.pdf<br />
}}<br />
'''Integration of Knowledge on Wikipedia and Other Web Resources''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Eklou Damien]], [[Yasuhito Asano]] and [[Masatoshi Yoshikawa]].<br />
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== Overview ==<br />
Looking for desired information on the web can be a time consuming task. In this process [[Wikipedia]] constitutes a very helpful tool as it is the largest and most popular general reference site on the internet. Most search engines actually rank Wikipedia pages among the top listed results. However due to the nature of Wikipedia which is manually updated by users, it is virtually impossible to have all the valuable information related to a subject covered in a single article. In order to support the user search experience, authors propose a method for finding valuable information not included in Wikipedia from other web resources.</div>Ameliahttps://wikipediaquality.com/index.php?title=Libguides._What_About_Wikipedia%3F._What_is_Wikipedia%3F&diff=22736Libguides. What About Wikipedia?. What is Wikipedia?2019-12-09T05:37:05Z<p>Amelia: + embed code</p>
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<div>{{Infobox work<br />
| title = Libguides. What About Wikipedia?. What is Wikipedia?<br />
| date = 2011<br />
| authors = [[Maria de Jesus Ayala-Schueneman]]<br />
| link = http://libguides.tamuk.edu/content.php?pid=164882&amp;sid=1575375<br />
}}<br />
'''Libguides. What About Wikipedia?. What is Wikipedia?''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Maria de Jesus Ayala-Schueneman]].<br />
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== Overview ==<br />
What is [[Wikipedia]] and why can't Author cite it? This guide discusses the strengths and weaknesses of Wikipedia articles and how Academia can improve the accuracy of this source.<br />
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Ayala-Schueneman, Maria de Jesus. (2011). "[[Libguides. What About Wikipedia?. What is Wikipedia?]]". Texas A&M University-Kingsville Library. <br />
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{{cite journal |last1=Ayala-Schueneman |first1=Maria de Jesus |title=Libguides. What About Wikipedia?. What is Wikipedia? |date=2011 |url=https://wikipediaquality.com/wiki/Libguides._What_About_Wikipedia?._What_is_Wikipedia? |journal=Texas A&M University-Kingsville Library}}<br />
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Ayala-Schueneman, Maria de Jesus. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Libguides._What_About_Wikipedia?._What_is_Wikipedia?">Libguides. What About Wikipedia?. What is Wikipedia?</a>&amp;quot;. Texas A&M University-Kingsville Library. <br />
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</code></div>Ameliahttps://wikipediaquality.com/index.php?title=Assigning_Students_to_Edit_Wikipedia:_Four_Case_Studies&diff=22735Assigning Students to Edit Wikipedia: Four Case Studies2019-12-09T05:34:49Z<p>Amelia: + infobox</p>
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<div>{{Infobox work<br />
| title = Assigning Students to Edit Wikipedia: Four Case Studies<br />
| date = 2012<br />
| authors = [[Brian W. Carver]]<br />[[Rochelle Davis]]<br />[[Robin T. Kelley]]<br />[[Jonathan A. Obar]]<br />[[Lianna L. Davis]]<br />
| doi = 10.2304/elea.2012.9.3.273<br />
| link = http://journals.sagepub.com/doi/10.2304/elea.2012.9.3.273<br />
}}<br />
'''Assigning Students to Edit Wikipedia: Four Case Studies''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Brian W. Carver]], [[Rochelle Davis]], [[Robin T. Kelley]], [[Jonathan A. Obar]] and [[Lianna L. Davis]].<br />
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== Overview ==<br />
During the 2010–11 academic year, the [[Wikimedia Foundation]], the nonprofit organization that supports [[Wikipedia]], worked with professors at universities across the United States who were interested in using Wikipedia as a teaching tool in their classrooms through a pilot version of the Wikipedia Education Program. This article presents a case study of four professors' experiences: two professors at Georgetown University, one professor at Michigan State University, and one professor at the University of California at Berkeley. Each describe the ways they incorporated Wikipedia into their classroom and the learning points that emerged from the experiences. Together, they offer suggestions for other professors who are interested in participating in the Wikipedia Education Program.</div>Ameliahttps://wikipediaquality.com/index.php?title=An_Aesthetic_for_Deliberating_Online:_Thinking_Through_%E2%80%9CUniversal_Pragmatics%E2%80%9D_and_%E2%80%9CDialogism%E2%80%9D_with_Reference_to_Wikipedia&diff=22734An Aesthetic for Deliberating Online: Thinking Through “Universal Pragmatics” and “Dialogism” with Reference to Wikipedia2019-12-09T05:32:28Z<p>Amelia: + links</p>
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<div>'''An Aesthetic for Deliberating Online: Thinking Through “Universal Pragmatics” and “Dialogism” with Reference to Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Nicholas Cimini]] and [[Jennifer Burr]].<br />
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== Overview ==<br />
In this article authors examine contributions to [[Wikipedia]] through the prism of two divergent critical theorists: Jurgen Habermas and Mikhail Bakhtin. Authors show that, in slightly dissimilar ways, these theorists came to consider an “aesthetic for democracy” Hirschkop 1999 or template for deliberative relationships that privileges relatively free and unconstrained dialogue to which every speaker has equal access and without authoritative closure. Authors employ Habermas's theory of “universal pragmatics” and Bakhtin's “dialogism” for analyses of contributions on Wikipedia for its entry on stem cells and transhumanism and show that the decision to embrace either unified or pluralistic forms of deliberation is an empirical matter to be judged in sociohistorical context, as opposed to what normative theories insist on. Authors conclude by stressing the need to be attuned to the complexity and ambiguity of deliberative relations online.</div>Ameliahttps://wikipediaquality.com/index.php?title=Approaches_for_Automatically_Enriching_Wikipedia&diff=22733Approaches for Automatically Enriching Wikipedia2019-12-09T05:30:19Z<p>Amelia: Infobox</p>
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<div>{{Infobox work<br />
| title = Approaches for Automatically Enriching Wikipedia<br />
| date = 2010<br />
| authors = [[Zareen Syed]]<br />[[Tim Finin]]<br />
| link = https://dl.acm.org/citation.cfm?id=2908532<br />
}}<br />
'''Approaches for Automatically Enriching Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Zareen Syed]] and [[Tim Finin]].<br />
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== Overview ==<br />
Authors have been exploring the use of Web-derived knowledge bases through the development of Wikitology - a hybrid knowledge base of structured and un[[structured information]] extracted from [[Wikipedia]] augmented by RDF data from [[DBpedia]] and other Linked Open Data resources. In this paper, authors describe approaches that aid in enriching Wikipedia and thus the resources that derive from Wikipedia such as the Wikitology knowledge base, DBpedia, Freebase and Powerset.</div>Ameliahttps://wikipediaquality.com/index.php?title=Pushing_Your_Point_of_View:_Behavioral_Measures_of_Manipulation_in_Wikipedia&diff=22732Pushing Your Point of View: Behavioral Measures of Manipulation in Wikipedia2019-12-09T05:28:33Z<p>Amelia: Adding categories</p>
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<div>{{Infobox work<br />
| title = Pushing Your Point of View: Behavioral Measures of Manipulation in Wikipedia<br />
| date = 2011<br />
| authors = [[Sanmay Das]]<br />[[Allen Lavoie]]<br />[[Malik Magdon-Ismail]]<br />
| link = http://journals.sagepub.com/doi/full/10.1177/1536504214522017<br />
| plink = https://arxiv.org/pdf/1111.2092<br />
}}<br />
'''Pushing Your Point of View: Behavioral Measures of Manipulation in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Sanmay Das]], [[Allen Lavoie]] and [[Malik Magdon-Ismail]].<br />
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== Overview ==<br />
As a major source for information on virtually any topic, [[Wikipedia]] serves an important role in public dissemination and consumption of knowledge. As a result, it presents tremendous potential for people to promulgate their own points of view; such efforts may be more subtle than typical vandalism. In this paper, authors introduce new behavioral metrics to quantify the level of controversy associated with a particular user: a Controversy Score (C-Score) based on the amount of attention the user focuses on controversial pages, and a Clustered Controversy Score (CC-Score) that also takes into account topical clustering. Authors show that both these [[measures]] are useful for identifying people who try to “push” their points of view, by showing that they are good predictors of which editors get blocked. The metrics can be used to triage potential POV pushers. Authors apply this idea to a dataset of users who requested promotion to administrator status and easily identify some editors who significantly changed their behavior upon becoming administrators. At the same time, such behavior is not rampant. Those who are promoted to administrator status tend to have more stable behavior than comparable groups of prolific editors. This suggests that the Adminship process works well, and that the [[Wikipedia community]] is not overwhelmed by users who become administrators to promote their own points of view.<br />
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{{cite journal |last1=Das |first1=Sanmay |last2=Lavoie |first2=Allen |last3=Magdon-Ismail |first3=Malik |title=Pushing Your Point of View: Behavioral Measures of Manipulation in Wikipedia |date=2011 |url=https://wikipediaquality.com/wiki/Pushing_Your_Point_of_View:_Behavioral_Measures_of_Manipulation_in_Wikipedia}}<br />
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Das, Sanmay; Lavoie, Allen; Magdon-Ismail, Malik. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Pushing_Your_Point_of_View:_Behavioral_Measures_of_Manipulation_in_Wikipedia">Pushing Your Point of View: Behavioral Measures of Manipulation in Wikipedia</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Ameliahttps://wikipediaquality.com/index.php?title=On_the_Analysis_of_Wikipedia_Activity_Through_Time&diff=22731On the Analysis of Wikipedia Activity Through Time2019-12-09T05:27:28Z<p>Amelia: + category</p>
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<div>{{Infobox work<br />
| title = On the Analysis of Wikipedia Activity Through Time<br />
| date = 2014<br />
| authors = [[Nuno Silva]]<br />[[Daniel Gonçalves]]<br />
| doi = 10.1109/IV.2014.61<br />
| link = http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6902937<br />
}}<br />
'''On the Analysis of Wikipedia Activity Through Time''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Nuno Silva]] and [[Daniel Gonçalves]].<br />
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== Overview ==<br />
Wikipedia articles see bursts of update activity whenever a topic is of more interest to the community or has somehow become controversial. Analyzing when and what changes are made can, thus, give us an idea of how the community feels about particular subjects. In this paper authors present Pop Culture, a system that provides a visualization of [[Wikipedia]]'s edits that allows us to reflect on how different subjects are perceived by people over time and, by comparing articles from [[different language]] wikipedias, find regional and cultural differences of interest and perception. A set of user studies shows that, indeed, users are able to use Pop Culture effectively and efficiently to find such trends and differences.<br />
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Silva, Nuno; Gonçalves, Daniel. (2014). "[[On the Analysis of Wikipedia Activity Through Time]]".DOI: 10.1109/IV.2014.61. <br />
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{{cite journal |last1=Silva |first1=Nuno |last2=Gonçalves |first2=Daniel |title=On the Analysis of Wikipedia Activity Through Time |date=2014 |doi=10.1109/IV.2014.61 |url=https://wikipediaquality.com/wiki/On_the_Analysis_of_Wikipedia_Activity_Through_Time}}<br />
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Silva, Nuno; Gonçalves, Daniel. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/On_the_Analysis_of_Wikipedia_Activity_Through_Time">On the Analysis of Wikipedia Activity Through Time</a>&amp;quot;.DOI: 10.1109/IV.2014.61. <br />
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[[Category:Scientific works]]</div>Ameliahttps://wikipediaquality.com/index.php?title=Who_Likes_Me_More%3F:_Analysing_Entity-Centric_Language-Specific_Bias_in_Multilingual_Wikipedia&diff=22730Who Likes Me More?: Analysing Entity-Centric Language-Specific Bias in Multilingual Wikipedia2019-12-09T05:24:39Z<p>Amelia: infobox</p>
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<div>{{Infobox work<br />
| title = Who Likes Me More?: Analysing Entity-Centric Language-Specific Bias in Multilingual Wikipedia<br />
| date = 2016<br />
| authors = [[Yiwei Zhou]]<br />[[Elena Demidova]]<br />[[Alexandra I. Cristea]]<br />
| doi = 10.1145/2851613.2851858<br />
| link = https://dl.acm.org/citation.cfm?doid=2851613.2851858<br />
}}<br />
'''Who Likes Me More?: Analysing Entity-Centric Language-Specific Bias in Multilingual Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Yiwei Zhou]], [[Elena Demidova]] and [[Alexandra I. Cristea]].<br />
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== Overview ==<br />
In this paper authors take an important step towards better understanding the existence and extent of entity-centric language-specific bias in [[multilingual]] [[Wikipedia]] , and any deviation from its targeted [[neutral point of view]]. Authors propose a methodology using sentiment analysis techniques to systematically extract the variations in sentiments associated with real-world entities in [[different language]] editions of Wikipedia, illustrated with a case study of five Wikipedia language editions and a set of target entities from four [[categories]].</div>Amelia