https://wikipediaquality.com/api.php?action=feedcontributions&user=Beata&feedformat=atomWikipedia Quality - User contributions [en]2024-03-29T05:53:29ZUser contributionsMediaWiki 1.30.0https://wikipediaquality.com/index.php?title=Wikiseealso:_Suggesting_Tangentially_Related_Concepts_(_See_Also_Links_)_for_Wikipedia_Articles&diff=27932Wikiseealso: Suggesting Tangentially Related Concepts ( See Also Links ) for Wikipedia Articles2021-02-26T06:11:36Z<p>Beata: + categories</p>
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<div>{{Infobox work<br />
| title = Wikiseealso: Suggesting Tangentially Related Concepts ( See Also Links ) for Wikipedia Articles<br />
| date = 2017<br />
| authors = [[Sahiti Labhishetty]]<br />[[Ayesha Siddiqa]]<br />[[Rajivteja Nagipogu]]<br />[[Sutanu Chakraborti]]<br />
| doi = 10.1007/978-3-319-71928-3_27<br />
| link = https://link.springer.com/chapter/10.1007/978-3-319-71928-3_27<br />
}}<br />
'''Wikiseealso: Suggesting Tangentially Related Concepts ( See Also Links ) for Wikipedia Articles''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Sahiti Labhishetty]], [[Ayesha Siddiqa]], [[Rajivteja Nagipogu]] and [[Sutanu Chakraborti]].<br />
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== Overview ==<br />
Wikipedia is the pervasive knowledge source for widely utilized applications like [[Google]]’s Knowledge Graph, IBM’s Watson and Apple’s Siri system. [[Wikipedia]] articles contain internal links and See also section links. According to Wikipedia, one of the purposes of See also links is to enable readers to explore tangentially related topics. Currently, Wikipedia relies on human judgments for adding See also links. Authors attempt to automate the process of See also recommendation by utilizing the aspects of Wikipedia articles like category knowledge, Backlink and the ESA concept vector similarity and external knowledge retrieved by web search engine. Authors proposed ensemble based approach combines similarities obtained from these aspects to give a final prediction score. Authors evaluate approach on datasets of Wikipedia articles and present empirical comparison and case studies results with the state-of-the art approaches. Authors envisage that this work will aid [[Wikipedia editors]] and readers to facilitate information search.<br />
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Labhishetty, Sahiti; Siddiqa, Ayesha; Nagipogu, Rajivteja; Chakraborti, Sutanu. (2017). "[[Wikiseealso: Suggesting Tangentially Related Concepts ( See Also Links ) for Wikipedia Articles]]". Springer, Cham. DOI: 10.1007/978-3-319-71928-3_27. <br />
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{{cite journal |last1=Labhishetty |first1=Sahiti |last2=Siddiqa |first2=Ayesha |last3=Nagipogu |first3=Rajivteja |last4=Chakraborti |first4=Sutanu |title=Wikiseealso: Suggesting Tangentially Related Concepts ( See Also Links ) for Wikipedia Articles |date=2017 |doi=10.1007/978-3-319-71928-3_27 |url=https://wikipediaquality.com/wiki/Wikiseealso:_Suggesting_Tangentially_Related_Concepts_(_See_Also_Links_)_for_Wikipedia_Articles |journal=Springer, Cham}}<br />
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Labhishetty, Sahiti; Siddiqa, Ayesha; Nagipogu, Rajivteja; Chakraborti, Sutanu. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikiseealso:_Suggesting_Tangentially_Related_Concepts_(_See_Also_Links_)_for_Wikipedia_Articles">Wikiseealso: Suggesting Tangentially Related Concepts ( See Also Links ) for Wikipedia Articles</a>&amp;quot;. Springer, Cham. DOI: 10.1007/978-3-319-71928-3_27. <br />
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[[Category:Scientific works]]</div>Beatahttps://wikipediaquality.com/index.php?title=An_Empirical_Research:_%22Wikipedia_Vandalism_Detection_Using_Vandalsense_2.0%22_-_Notebook_for_Pan_at_Clef_2011&diff=27931An Empirical Research: "Wikipedia Vandalism Detection Using Vandalsense 2.0" - Notebook for Pan at Clef 20112021-02-26T06:09:53Z<p>Beata: Embed</p>
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<div>{{Infobox work<br />
| title = An Empirical Research: "Wikipedia Vandalism Detection Using Vandalsense 2.0" - Notebook for Pan at Clef 2011<br />
| date = 2011<br />
| authors = [[F. Gediz Aksit]]<br />
| link = http://ceur-ws.org/Vol-1177/CLEF2011wn-PAN-Aksit2011.pdf<br />
}}<br />
'''An Empirical Research: "Wikipedia Vandalism Detection Using Vandalsense 2.0" - Notebook for Pan at Clef 2011''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[F. Gediz Aksit]].<br />
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== Overview ==<br />
Wikipedia despite having a very small budget has been among the top ten most visited websites for over half a decade. Being this visible also generated the problem of ill intended people modifying [[Wikipedia]] in a destructive manner. VandalSense is an experimental tool programmed by F. Gediz Aksit to automatically identify vandalism on Wikipedia through the use of machine learning and text mining as well as the use of years of personal experience. VandalSense is not intended to replace traditional recent changes patrolling and instead it is intended to be a tool to compliment it.<br />
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Aksit, F. Gediz. (2011). "[[An Empirical Research: "Wikipedia Vandalism Detection Using Vandalsense 2.0" - Notebook for Pan at Clef 2011]]".<br />
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{{cite journal |last1=Aksit |first1=F. Gediz |title=An Empirical Research: "Wikipedia Vandalism Detection Using Vandalsense 2.0" - Notebook for Pan at Clef 2011 |date=2011 |url=https://wikipediaquality.com/wiki/An_Empirical_Research:_"Wikipedia_Vandalism_Detection_Using_Vandalsense_2.0"_-_Notebook_for_Pan_at_Clef_2011}}<br />
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Aksit, F. Gediz. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/An_Empirical_Research:_"Wikipedia_Vandalism_Detection_Using_Vandalsense_2.0"_-_Notebook_for_Pan_at_Clef_2011">An Empirical Research: &amp;quot;Wikipedia Vandalism Detection Using Vandalsense 2.0&amp;quot; - Notebook for Pan at Clef 2011</a>&amp;quot;.<br />
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</code></div>Beatahttps://wikipediaquality.com/index.php?title=Using_Wikipedia_to_Boost_Collaborative_Filtering_Techniques&diff=27930Using Wikipedia to Boost Collaborative Filtering Techniques2021-02-26T06:07:41Z<p>Beata: Infobox</p>
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<div>{{Infobox work<br />
| title = Using Wikipedia to Boost Collaborative Filtering Techniques<br />
| date = 2011<br />
| authors = [[Gilad Katz]]<br />[[Bracha Shapira]]<br />[[Lior Rokach]]<br />[[Guy Shani]]<br />
| doi = 10.1145/2043932.2043984<br />
| link = http://dl.acm.org/citation.cfm?id=2043984<br />
}}<br />
'''Using Wikipedia to Boost Collaborative Filtering Techniques''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Gilad Katz]], [[Bracha Shapira]], [[Lior Rokach]] and [[Guy Shani]].<br />
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== Overview ==<br />
One important challenge in the field of recommender systems is the sparsity of available data. This problem limits the ability of recommender systems to provide accurate predictions of user ratings. Authors overcome this problem by using the publicly available user generated information contained in [[Wikipedia]]. Authors identify similarities between items by mapping them to Wikipedia pages and finding similarities in the text and commonalities in the links and [[categories]] of each page. These similarities can be used in the recommendation process and improve ranking predictions. Authors find that this method is most effective in cases where ratings are extremely sparse or nonexistent. Preliminary experimental results on the MovieLens dataset are encouraging.</div>Beatahttps://wikipediaquality.com/index.php?title=Toward_Data-Driven_Idea_Generation:_Application_of_Wikipedia_to_Morphological_Analysis&diff=27929Toward Data-Driven Idea Generation: Application of Wikipedia to Morphological Analysis2021-02-26T06:04:58Z<p>Beata: Embed for English Wikipedia, HTML</p>
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<div>{{Infobox work<br />
| title = Toward Data-Driven Idea Generation: Application of Wikipedia to Morphological Analysis<br />
| date = 2018<br />
| authors = [[Heeyeul Kwon]]<br />[[Yongtae Park]]<br />[[Youngjung Geum]]<br />
| doi = 10.1016/j.techfore.2018.01.009<br />
| link = https://www.sciencedirect.com/science/article/pii/S0040162517300859<br />
}}<br />
'''Toward Data-Driven Idea Generation: Application of Wikipedia to Morphological Analysis''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Heeyeul Kwon]], [[Yongtae Park]] and [[Youngjung Geum]].<br />
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== Overview ==<br />
Abstract The generation of new and creative ideas is vital to stimulating innovation. Morphological analysis is one appropriate method given its objective, impersonal, and systematic nature. However, how to build a morphological matrix is a critical problem, especially in the big data era. This research focuses on [[Wikipedia]]'s case-specific characteristics and well-coordinated knowledge structure and attempts to integrate the platform with morphological analysis. In details, several methodological options are explored to implement Wikipedia data into morphological analysis. Authors then propose a Wikipedia-based approach to the development of morphological matrix, which incorporates the data on table of contents , hyperlinks , and [[categories]] . Its feasibility was demonstrated through a case study of drone technology, and its validity and effectiveness was shown based on a comparative analysis with a conventional discussion-based approach. The methodology is expected to be served as an essential supporting tool for generating creative ideas that could spark innovation.<br />
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Kwon, Heeyeul; Park, Yongtae; Geum, Youngjung. (2018). "[[Toward Data-Driven Idea Generation: Application of Wikipedia to Morphological Analysis]]". North-Holland. DOI: 10.1016/j.techfore.2018.01.009. <br />
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{{cite journal |last1=Kwon |first1=Heeyeul |last2=Park |first2=Yongtae |last3=Geum |first3=Youngjung |title=Toward Data-Driven Idea Generation: Application of Wikipedia to Morphological Analysis |date=2018 |doi=10.1016/j.techfore.2018.01.009 |url=https://wikipediaquality.com/wiki/Toward_Data-Driven_Idea_Generation:_Application_of_Wikipedia_to_Morphological_Analysis |journal=North-Holland}}<br />
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Kwon, Heeyeul; Park, Yongtae; Geum, Youngjung. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/Toward_Data-Driven_Idea_Generation:_Application_of_Wikipedia_to_Morphological_Analysis">Toward Data-Driven Idea Generation: Application of Wikipedia to Morphological Analysis</a>&amp;quot;. North-Holland. DOI: 10.1016/j.techfore.2018.01.009. <br />
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</code></div>Beatahttps://wikipediaquality.com/index.php?title=Collaborative_Thesaurus_Tagging_the_Wikipedia_Way&diff=27928Collaborative Thesaurus Tagging the Wikipedia Way2021-02-26T06:02:52Z<p>Beata: Adding embed</p>
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<div>{{Infobox work<br />
| title = Collaborative Thesaurus Tagging the Wikipedia Way<br />
| date = 2006<br />
| authors = [[Jakob Voss]]<br />
| link = http://www.redalyc.org/pdf/550/Resumenes/Resumen_55023345002_1.pdf<br />
| plink = https://arxiv.org/abs/cs/0604036<br />
}}<br />
'''Collaborative Thesaurus Tagging the Wikipedia Way''' - scientific work related to [[Wikipedia quality]] published in 2006, written by [[Jakob Voss]].<br />
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== Overview ==<br />
This paper explores the system of [[categories]] that is used to classify articles in [[Wikipedia]]. It is compared to collaborative tagging systems like del.icio.us and to hierarchical classification like the Dewey Decimal Classification (DDC). Specifics and commonalitiess of these systems of subject indexing are exposed. Analysis of structural and statistical properties (descriptors per record, records per descriptor, descriptor levels) shows that the category system of [[Wikimedia]] is a thesaurus that combines collaborative tagging and hierarchical subject indexing in a special way.<br />
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Voss, Jakob. (2006). "[[Collaborative Thesaurus Tagging the Wikipedia Way]]".<br />
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{{cite journal |last1=Voss |first1=Jakob |title=Collaborative Thesaurus Tagging the Wikipedia Way |date=2006 |url=https://wikipediaquality.com/wiki/Collaborative_Thesaurus_Tagging_the_Wikipedia_Way}}<br />
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Voss, Jakob. (2006). &amp;quot;<a href="https://wikipediaquality.com/wiki/Collaborative_Thesaurus_Tagging_the_Wikipedia_Way">Collaborative Thesaurus Tagging the Wikipedia Way</a>&amp;quot;.<br />
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</code></div>Beatahttps://wikipediaquality.com/index.php?title=Semantic_Metadata_Generation:A_Method_based_on_Wikipedia&diff=27927Semantic Metadata Generation:A Method based on Wikipedia2021-02-26T06:00:04Z<p>Beata: Categories</p>
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<div>{{Infobox work<br />
| title = Semantic Metadata Generation:A Method based on Wikipedia<br />
| date = 2009<br />
| authors = [[Zhao Jun]]<br />
| link = http://www.nlpr.ia.ac.cn/2009papers/gnkw/nk18.pdf<br />
}}<br />
'''Semantic Metadata Generation:A Method based on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Zhao Jun]].<br />
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== Overview ==<br />
Semantic metadata,which provides [[semantic information]] about data,plays an important role in document management,fusion and information search.The automatic metadata generation technique,which subsumes the acquisition of target semantic metadata and the collection of training corpus as two fundamental problems,becomes more demanding in the data explosion time.The first problem involves expert knowledge and the second problem needs lots of manual work,and accordingly,they are critical to a successful system.In this paper,we resolve the two problems based on [[Wikipedia]]: extracting the target metadata by analyzing the table-of-contents of Wikipedia's entries and building the training corpus by analyzing the Wikipedia entry's structure and assigning its true semantic metadata.The experiment results demonstrate that this approach can resolve the two issues in automatic metadata generation effectively.<br />
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{{cite journal |last1=Jun |first1=Zhao |title=Semantic Metadata Generation:A Method based on Wikipedia |date=2009 |url=https://wikipediaquality.com/wiki/Semantic_Metadata_Generation:A_Method_based_on_Wikipedia}}<br />
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Jun, Zhao. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Semantic_Metadata_Generation:A_Method_based_on_Wikipedia">Semantic Metadata Generation:A Method based on Wikipedia</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Beatahttps://wikipediaquality.com/index.php?title=Making_Wikipedia_Editing_Easier_for_the_Blind&diff=27926Making Wikipedia Editing Easier for the Blind2021-02-26T05:57:59Z<p>Beata: Category</p>
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<div>{{Infobox work<br />
| title = Making Wikipedia Editing Easier for the Blind<br />
| date = 2008<br />
| authors = [[M. Claudia Buzzi]]<br />[[Marina Buzzi]]<br />[[Barbara Leporini]]<br />[[Caterina Senette]]<br />
| doi = 10.1145/1463160.1463210<br />
| link = http://dl.acm.org/citation.cfm?id=1463210<br />
}}<br />
'''Making Wikipedia Editing Easier for the Blind''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[M. Claudia Buzzi]], [[Marina Buzzi]], [[Barbara Leporini]] and [[Caterina Senette]].<br />
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== Overview ==<br />
A key feature of Web 2.0 is the possibility of sharing, creating and editing on-line content. This approach is increasingly used in learning environments to favor interaction and cooperation among students. These functions should be accessible as well as easy to use for all participants. Unfortunately accessibility and usability issues still exist for Web 2.0-based applications. For instance, [[Wikipedia]] presents many difficulties for the blind. In this paper authors discuss a possible solution for simplifying the Wikipedia editing page when interacting via screen reader. Building an editing interface that conforms to W3C ARIA (Accessible Rich Internet Applications) recommendations would overcome accessibility and usability problems that prevent blind users from actively contributing to Wikipedia.<br />
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Buzzi, M. Claudia; Buzzi, Marina; Leporini, Barbara; Senette, Caterina. (2008). "[[Making Wikipedia Editing Easier for the Blind]]".DOI: 10.1145/1463160.1463210. <br />
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{{cite journal |last1=Buzzi |first1=M. Claudia |last2=Buzzi |first2=Marina |last3=Leporini |first3=Barbara |last4=Senette |first4=Caterina |title=Making Wikipedia Editing Easier for the Blind |date=2008 |doi=10.1145/1463160.1463210 |url=https://wikipediaquality.com/wiki/Making_Wikipedia_Editing_Easier_for_the_Blind}}<br />
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Buzzi, M. Claudia; Buzzi, Marina; Leporini, Barbara; Senette, Caterina. (2008). &amp;quot;<a href="https://wikipediaquality.com/wiki/Making_Wikipedia_Editing_Easier_for_the_Blind">Making Wikipedia Editing Easier for the Blind</a>&amp;quot;.DOI: 10.1145/1463160.1463210. <br />
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[[Category:Scientific works]]</div>Beatahttps://wikipediaquality.com/index.php?title=Multiwiki:_Interlingual_Text_Passage_Alignment_in_Wikipedia&diff=27925Multiwiki: Interlingual Text Passage Alignment in Wikipedia2021-02-26T05:56:53Z<p>Beata: + cat.</p>
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<div>{{Infobox work<br />
| title = Multiwiki: Interlingual Text Passage Alignment in Wikipedia<br />
| date = 2017<br />
| authors = [[Simon Gottschalk]]<br />[[Elena Demidova]]<br />
| doi = 10.1145/3004296<br />
| link = https://dl.acm.org/citation.cfm?doid=3062397.3004296<br />
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'''Multiwiki: Interlingual Text Passage Alignment in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Simon Gottschalk]] and [[Elena Demidova]].<br />
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== Overview ==<br />
In this article, authors address the problem of text passage alignment across interlingual article pairs in [[Wikipedia]]. Authors develop methods that enable the identification and interlinking of text passages written in [[different language]]s and containing overlapping information. Interlingual text passage alignment can enable [[Wikipedia editors]] and readers to better understand language-specific context of entities, provide valuable insights in cultural differences, and build a basis for qualitative analysis of the articles. An important challenge in this context is the tradeoff between the granularity of the extracted text passages and the precision of the alignment. Whereas short text passages can result in more precise alignment, longer text passages can facilitate a better overview of the differences in an article pair. To better understand these aspects from the user perspective, authors conduct a user study at the example of the German, Russian, and [[English Wikipedia]] and collect a user-annotated benchmark. Then authors propose MultiWiki, a method that adopts an integrated approach to the text passage alignment using [[semantic similarity]] [[measures]] and greedy algorithms and achieves precise results with respect to the user-defined alignment. The MultiWiki demonstration is publicly available and currently supports four language pairs.<br />
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Gottschalk, Simon; Demidova, Elena. (2017). "[[Multiwiki: Interlingual Text Passage Alignment in Wikipedia]]".DOI: 10.1145/3004296. <br />
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{{cite journal |last1=Gottschalk |first1=Simon |last2=Demidova |first2=Elena |title=Multiwiki: Interlingual Text Passage Alignment in Wikipedia |date=2017 |doi=10.1145/3004296 |url=https://wikipediaquality.com/wiki/Multiwiki:_Interlingual_Text_Passage_Alignment_in_Wikipedia}}<br />
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Gottschalk, Simon; Demidova, Elena. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Multiwiki:_Interlingual_Text_Passage_Alignment_in_Wikipedia">Multiwiki: Interlingual Text Passage Alignment in Wikipedia</a>&amp;quot;.DOI: 10.1145/3004296. <br />
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[[Category:Scientific works]]<br />
[[Category:English Wikipedia]]<br />
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[[Category:Interlingua Wikipedia]]</div>Beatahttps://wikipediaquality.com/index.php?title=Understanding_the_Wikipedia_Phenomenon:_a_Case_for_Agent_based_Modeling&diff=27924Understanding the Wikipedia Phenomenon: a Case for Agent based Modeling2021-02-26T05:54:03Z<p>Beata: + cat.</p>
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<div>{{Infobox work<br />
| title = Understanding the Wikipedia Phenomenon: a Case for Agent based Modeling<br />
| date = 2008<br />
| authors = [[Myshkin Ingawale]]<br />
| doi = 10.1145/1458550.1458565<br />
| link = http://dl.acm.org/citation.cfm?id=1458565<br />
}}<br />
'''Understanding the Wikipedia Phenomenon: a Case for Agent based Modeling''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Myshkin Ingawale]].<br />
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== Overview ==<br />
Wikipedia, the user led and monitored "open" encyclopedia has been an undoubted popular success. Of particular interest are the diffusion process of the innovation throughout the "contributor" community, and the question as to why unpaid, often well qualified, volunteers contribute content and time. Explanations for 'altruistic' contributor behavior based on the positivistic paradigm, and with roots in organizational psychology, while heavily researched and documented, have not been readily transferable to quantitative models of sufficient predictive value, in relation to [[Wikipedia]]'s metrics. For despite the wide range of types, ages, locations and motivations of its contributors and seekers, investigators on Wikipedia have identified certain definite and often surprisingly universal trends ('laws') in its overall growth curve, organization structure, community and article formation. Models based on aggregated top-level relationships between entities on and around wikipedia suffer from assuming relationships between these entities as inputs to the wikipedia process, rather than emergent phenomena that evolve and change with the output. Authors argue for an Agent Based Model of Wikipedia, with the end objective of work being a tool with diagnostic and/or prescriptive value for decision makers in organizations using or planning to use Knowledge Management Systems.<br />
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Ingawale, Myshkin. (2008). "[[Understanding the Wikipedia Phenomenon: a Case for Agent based Modeling]]".DOI: 10.1145/1458550.1458565. <br />
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{{cite journal |last1=Ingawale |first1=Myshkin |title=Understanding the Wikipedia Phenomenon: a Case for Agent based Modeling |date=2008 |doi=10.1145/1458550.1458565 |url=https://wikipediaquality.com/wiki/Understanding_the_Wikipedia_Phenomenon:_a_Case_for_Agent_based_Modeling}}<br />
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Ingawale, Myshkin. (2008). &amp;quot;<a href="https://wikipediaquality.com/wiki/Understanding_the_Wikipedia_Phenomenon:_a_Case_for_Agent_based_Modeling">Understanding the Wikipedia Phenomenon: a Case for Agent based Modeling</a>&amp;quot;.DOI: 10.1145/1458550.1458565. <br />
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[[Category:Scientific works]]</div>Beatahttps://wikipediaquality.com/index.php?title=Cprel:_Semantic_Relatedness_Computation_Using_Wikipedia_based_Context_Profiles&diff=27923Cprel: Semantic Relatedness Computation Using Wikipedia based Context Profiles2021-02-26T05:52:37Z<p>Beata: Category</p>
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<div>{{Infobox work<br />
| title = Cprel: Semantic Relatedness Computation Using Wikipedia based Context Profiles<br />
| date = 2013<br />
| authors = [[Shahida Jabeen]]<br />[[Xiaoying Gao]]<br />[[Peter Andreae]]<br />
| link = http://www.rcs.cic.ipn.mx/rcs/2013_70/CPRel_%20Semantic%20Relatedness%20Computation%20Using%20Wikipedia%20based%20Context%20Profiles.pdf<br />
}}<br />
'''Cprel: Semantic Relatedness Computation Using Wikipedia based Context Profiles''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Shahida Jabeen]], [[Xiaoying Gao]] and [[Peter Andreae]].<br />
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== Overview ==<br />
Semantic [[relatedness]] is a well known problem with its sig- nicance ranging from computational linguistics to Natural language Processing applications. Relatedness computation is restricted by the amount of common sense and background knowledge required to relate any two terms. This paper proposes a novel model of relatedness using context prole built on [[features]] extracted from encyclopedic knowledge. Proposed research makes use of [[Wikipedia]] to represent the context of a word in the high dimensional space of Wikipedia labels. Semantic relat- edness of a word pair is then assessed by comparing their corresponding context proles based on three dierent weighting schemes using tradi- tional Cosine similarity metrics. To evaluate proposed relatedness ap- proach, three well known benchmark datasets are used and it is shown that Wikipedia article contents can be used eectively to compute term relatedness. The experiments demonstrate that the proposed approach is computationally cheap as well as eective when correlated with human judgments.<br />
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Jabeen, Shahida; Gao, Xiaoying; Andreae, Peter. (2013). "[[Cprel: Semantic Relatedness Computation Using Wikipedia based Context Profiles]]".<br />
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{{cite journal |last1=Jabeen |first1=Shahida |last2=Gao |first2=Xiaoying |last3=Andreae |first3=Peter |title=Cprel: Semantic Relatedness Computation Using Wikipedia based Context Profiles |date=2013 |url=https://wikipediaquality.com/wiki/Cprel:_Semantic_Relatedness_Computation_Using_Wikipedia_based_Context_Profiles}}<br />
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Jabeen, Shahida; Gao, Xiaoying; Andreae, Peter. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Cprel:_Semantic_Relatedness_Computation_Using_Wikipedia_based_Context_Profiles">Cprel: Semantic Relatedness Computation Using Wikipedia based Context Profiles</a>&amp;quot;.<br />
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[[Category:Scientific works]]</div>Beatahttps://wikipediaquality.com/index.php?title=Dawt:_Densely_Annotated_Wikipedia_Texts_Across_Multiple_Languages&diff=27922Dawt: Densely Annotated Wikipedia Texts Across Multiple Languages2021-02-26T05:46:36Z<p>Beata: Information about: Dawt: Densely Annotated Wikipedia Texts Across Multiple Languages</p>
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<div>'''Dawt: Densely Annotated Wikipedia Texts Across Multiple Languages''' - scientific work related to Wikipedia quality published in 2017, written by Nemanja Spasojevic, Preeti Bhargava and Guoning Hu.<br />
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== Overview ==<br />
In this work, authors open up the DAWT dataset - Densely Annotated Wikipedia Texts across multiple languages. The annotations include labeled text mentions mapping to entities (represented by their Freebase machine ids) as well as the type of the entity. The data set contains total of 13.6M articles, 5.0B tokens, 13.8M mention entity co-occurrences. DAWT contains 4.8 times more anchor text to entity links than originally present in the Wikipedia markup. Moreover, it spans several languages including English, Spanish, Italian, German, French and Arabic. Authors also present the methodology used to generate the dataset which enriches Wikipedia markup in order to increase number of links. In addition to the main dataset, authors open up several derived datasets including mention entity co-occurrence counts and entity embeddings, as well as mappings between Freebase ids and Wikidata item ids. Authors also discuss two applications of these datasets and hope that opening them up would prove useful for the Natural Language Processing and Information Retrieval communities, as well as facilitate multi-lingual research.</div>Beatahttps://wikipediaquality.com/index.php?title=Enhancing_Wikipedia_Management_by_Evaluation_Agent_System&diff=27921Enhancing Wikipedia Management by Evaluation Agent System2021-02-26T05:40:50Z<p>Beata: Categories</p>
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<div>{{Infobox work<br />
| title = Enhancing Wikipedia Management by Evaluation Agent System<br />
| date = 2012<br />
| authors = [[Yue Qi]]<br />
| doi = 10.4018/japuc.2012070104<br />
| link = https://dl.acm.org/citation.cfm?id=2434154<br />
}}<br />
'''Enhancing Wikipedia Management by Evaluation Agent System''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Yue Qi]].<br />
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== Overview ==<br />
Wikipedia has recently become a popular platform for knowledge sharing and creation. However, the enormously increasing amount of editing has caused management problems with efficiency, accuracy, and convenience for [[Wikipedia]] administrators. Therefore, this study aimed to develop an intelligent agent system based on Web 3.0, the evaluation agent system (EAS), to solve these problems. The EAS is characterized by hybrid Web techniques, [[artificial intelligence]], integration of management guidelines, retrieval of real-time information, and the transfer of cross-platform data and includes the following three systems: the testing agent, the wiki agent, and the rule-based expert system (RBES) agent. Because the RBES was central to the EAS, 29 university students were included in the study to examine the effectiveness of the RBES compared to the conventional approach to administration. The findings revealed that the RBES was better than the conventional approach in accuracy, efficiency, operation convenience, and fatigue strength.<br />
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Qi, Yue. (2012). "[[Enhancing Wikipedia Management by Evaluation Agent System]]". IGI Global. DOI: 10.4018/japuc.2012070104. <br />
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{{cite journal |last1=Qi |first1=Yue |title=Enhancing Wikipedia Management by Evaluation Agent System |date=2012 |doi=10.4018/japuc.2012070104 |url=https://wikipediaquality.com/wiki/Enhancing_Wikipedia_Management_by_Evaluation_Agent_System |journal=IGI Global}}<br />
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Qi, Yue. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Enhancing_Wikipedia_Management_by_Evaluation_Agent_System">Enhancing Wikipedia Management by Evaluation Agent System</a>&amp;quot;. IGI Global. DOI: 10.4018/japuc.2012070104. <br />
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[[Category:Scientific works]]</div>Beatahttps://wikipediaquality.com/index.php?title=Exploiting_the_Category_Structure_of_Wikipedia_for_Entity_Ranking&diff=27920Exploiting the Category Structure of Wikipedia for Entity Ranking2021-02-26T05:35:30Z<p>Beata: + category</p>
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<div>{{Infobox work<br />
| title = Exploiting the Category Structure of Wikipedia for Entity Ranking<br />
| date = 2013<br />
| authors = [[Rianne Kaptein]]<br />[[Jaap Kamps]]<br />
| doi = 10.1016/j.artint.2012.06.003<br />
| link = http://dl.acm.org/citation.cfm?id=2405838.2405908<br />
}}<br />
'''Exploiting the Category Structure of Wikipedia for Entity Ranking''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Rianne Kaptein]] and [[Jaap Kamps]].<br />
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== Overview ==<br />
The Web has not only grown in size, but also changed its character, due to collaborative content creation and an increasing amount of structure. Current Search Engines find Web pages rather than information or knowledge, and leave it to the searchers to locate the sought information within the Web page. A considerable fraction of Web searches contains [[named entities]]. Authors focus on how the [[Wikipedia]] structure can help rank relevant entities directly in response to a search request, rather than retrieve an unorganized list of Web pages with relevant but also potentially redundant information about these entities. Authors results demonstrate the benefits of using topical and link structure over the use of shallow statistics. Authors main findings are the following. First, authors examine whether Wikipedia category and link structure can be used to retrieve entities inside Wikipedia as is the goal of the INEX (Initiative for the Evaluation of XML retrieval) Entity Ranking task. Category information proves to be a highly effective source of information, leading to large and significant improvements in retrieval performance on all data sets. Secondly, authors study how authors can use category information to retrieve documents for ad hoc retrieval topics in Wikipedia. Authors study the differences between entity ranking and ad hoc retrieval in Wikipedia by analyzing the relevance assessments. Considering retrieval performance, also on ad hoc retrieval topics authors achieve significantly better results by exploiting the category information. Finally, authors examine whether authors can automatically assign target [[categories]] to ad hoc and entity ranking queries. Guessed categories lead to performance improvements that are not as large as when the categories are assigned manually, but they are still significant. Authors conclude that the category information in Wikipedia is a useful source of information that can be used for entity ranking as well as other retrieval tasks.<br />
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Kaptein, Rianne; Kamps, Jaap. (2013). "[[Exploiting the Category Structure of Wikipedia for Entity Ranking]]". Elsevier Science Publishers Ltd.. DOI: 10.1016/j.artint.2012.06.003. <br />
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{{cite journal |last1=Kaptein |first1=Rianne |last2=Kamps |first2=Jaap |title=Exploiting the Category Structure of Wikipedia for Entity Ranking |date=2013 |doi=10.1016/j.artint.2012.06.003 |url=https://wikipediaquality.com/wiki/Exploiting_the_Category_Structure_of_Wikipedia_for_Entity_Ranking |journal=Elsevier Science Publishers Ltd.}}<br />
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Kaptein, Rianne; Kamps, Jaap. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Exploiting_the_Category_Structure_of_Wikipedia_for_Entity_Ranking">Exploiting the Category Structure of Wikipedia for Entity Ranking</a>&amp;quot;. Elsevier Science Publishers Ltd.. DOI: 10.1016/j.artint.2012.06.003. <br />
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[[Category:Scientific works]]</div>Beatahttps://wikipediaquality.com/index.php?title=Using_Wikipedia_Knowledge_and_Query_Types_in_a_New_Indexing_Approach_for_Web_Search_Engines&diff=27919Using Wikipedia Knowledge and Query Types in a New Indexing Approach for Web Search Engines2021-02-26T05:29:05Z<p>Beata: + categories</p>
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<div>{{Infobox work<br />
| title = Using Wikipedia Knowledge and Query Types in a New Indexing Approach for Web Search Engines<br />
| date = 2014<br />
| authors = [[Falah Hassan]]<br />[[Ali Al-Akashi]]<br />
| doi = 10.20381/ruor-6304<br />
| link = http://ruor.uottawa.ca/bitstream/10393/31773/3/Al-Akashi_Falah_Hassan_Ali_2014_thesis.pdf<br />
}}<br />
'''Using Wikipedia Knowledge and Query Types in a New Indexing Approach for Web Search Engines''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Falah Hassan]] and [[Ali Al-Akashi]].<br />
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== Overview ==<br />
_____________________________________________________________________________ The Web is comprised of a vast quantity of text. Modern search engines struggle to index it independent of the structure of queries and type of Web data, and commonly use indexing based on Web‘s graph structure to identify high-quality relevant pages. However, despite the apparent widespread use of these algorithms, Web indexing based on human feedback and document content is controversial. There are many fundamental questions that need to be addressed, including: How many types of domains/websites are there in the Web? What type of data is in each type of domain? For each type, which segments/HTML fields in the documents are most useful? What are the relationships between the segments? How can web content be indexed efficiently in all forms of document configurations? Authors investigation of these questions has led to a novel way to use [[Wikipedia]] to find the relationships between the query structures and document configurations throughout the document indexing process and to use them to build an efficient index that allows fast indexing and searching, and optimizes the retrieval of highly relevant results. Authors consider the top page on the ranked list to be highly important in determining the types of queries. Authors aim is to design a powerful search engine with a strong focus on how to make the first page highly relevant to the user, and on how to retrieve other pages based on that first page. Through processing the user query using the Wikipedia index and determining the type of the query, approach could trace the path of a query in index, and retrieve specific results for each type. Authors use two kinds of data to increase the relevancy and efficiency of the ranked results: offline and real-time. Traditional search engines find it difficult to use these two kinds of data together, because building a real-time index from social data and integrating it with the index for the offline data is difficult in a traditional distributed index. As a source of offline data, authors use data from the Text Retrieval Conference (TREC) evaluation campaign. The web track at TREC offers researchers chance to investigate different retrieval approaches for web indexing and searching. The crawled offline dataset makes it possible to design powerful search engines that extends current methods and to evaluate and compare them. Authors propose a new indexing method, based on the structures of the queries and the content of documents. Authors search engine uses a core index for offline data and a hash index for real-time<br />
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Hassan, Falah; Al-Akashi, Ali. (2014). "[[Using Wikipedia Knowledge and Query Types in a New Indexing Approach for Web Search Engines]]". Université d'Ottawa / University of Ottawa. DOI: 10.20381/ruor-6304. <br />
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{{cite journal |last1=Hassan |first1=Falah |last2=Al-Akashi |first2=Ali |title=Using Wikipedia Knowledge and Query Types in a New Indexing Approach for Web Search Engines |date=2014 |doi=10.20381/ruor-6304 |url=https://wikipediaquality.com/wiki/Using_Wikipedia_Knowledge_and_Query_Types_in_a_New_Indexing_Approach_for_Web_Search_Engines |journal=Université d'Ottawa / University of Ottawa}}<br />
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Hassan, Falah; Al-Akashi, Ali. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Using_Wikipedia_Knowledge_and_Query_Types_in_a_New_Indexing_Approach_for_Web_Search_Engines">Using Wikipedia Knowledge and Query Types in a New Indexing Approach for Web Search Engines</a>&amp;quot;. Université d'Ottawa / University of Ottawa. DOI: 10.20381/ruor-6304. <br />
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[[Category:Scientific works]]</div>Beatahttps://wikipediaquality.com/index.php?title=Accuracy_and_Quality_in_Historical_Representation:_Wikipedia,_Textbooks_and_the_Investiture_Controversy&diff=27918Accuracy and Quality in Historical Representation: Wikipedia, Textbooks and the Investiture Controversy2021-02-26T05:21:36Z<p>Beata: cat.</p>
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<div>{{Infobox work<br />
| title = Accuracy and Quality in Historical Representation: Wikipedia, Textbooks and the Investiture Controversy<br />
| date = 2013<br />
| authors = [[David G. Halsted]]<br />
| doi = 10.16995/dm.50<br />
| link = https://journal.digitalmedievalist.org/articles/10.16995/dm.50/<br />
}}<br />
'''Accuracy and Quality in Historical Representation: Wikipedia, Textbooks and the Investiture Controversy''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[David G. Halsted]].<br />
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== Overview ==<br />
Wikipedia’s popularity is unquestioned, but a perceived lack of accuracy and [[reliability]] in articles on historical topics prevents historians from embracing it more fully. This article argues that accuracy may be only one component of overall quality. While [[Wikipedia]] may have demonstrable shortcomings, it also has strengths in areas such as [[completeness]] and accessibility. These strengths appear when historical narratives in Wikipedia are compared to other sources of historical information readily available to American undergraduates. The article compares Wikipedia’s entry on the Investiture Controversy to current scholarship and textbook treatments of the theme. On a broader view of quality, Wikipedia appears in a more favorable light than it does when authors employ a narrow focus on accuracy about specific dates and events.<br />
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Halsted, David G.. (2013). "[[Accuracy and Quality in Historical Representation: Wikipedia, Textbooks and the Investiture Controversy]]". Open Library of Humanities. DOI: 10.16995/dm.50. <br />
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{{cite journal |last1=Halsted |first1=David G. |title=Accuracy and Quality in Historical Representation: Wikipedia, Textbooks and the Investiture Controversy |date=2013 |doi=10.16995/dm.50 |url=https://wikipediaquality.com/wiki/Accuracy_and_Quality_in_Historical_Representation:_Wikipedia,_Textbooks_and_the_Investiture_Controversy |journal=Open Library of Humanities}}<br />
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Halsted, David G.. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Accuracy_and_Quality_in_Historical_Representation:_Wikipedia,_Textbooks_and_the_Investiture_Controversy">Accuracy and Quality in Historical Representation: Wikipedia, Textbooks and the Investiture Controversy</a>&amp;quot;. Open Library of Humanities. DOI: 10.16995/dm.50. <br />
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[[Category:Scientific works]]</div>Beatahttps://wikipediaquality.com/index.php?title=Representing_African_Cities_in_Wikipedia:_the_Case_of_Lagos_and_Kinshasa&diff=27917Representing African Cities in Wikipedia: the Case of Lagos and Kinshasa2021-02-26T05:12:07Z<p>Beata: Adding categories</p>
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<div>{{Infobox work<br />
| title = Representing African Cities in Wikipedia: the Case of Lagos and Kinshasa<br />
| date = 2018<br />
| authors = [[Brendan Luyt]]<br />
| doi = 10.1177/0266666918756171<br />
| link = http://journals.sagepub.com/doi/full/10.1177/0266666918756171<br />
}}<br />
'''Representing African Cities in Wikipedia: the Case of Lagos and Kinshasa''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Brendan Luyt]].<br />
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== Overview ==<br />
The role played by representations in the lives of cities endows the study of their production and distribution in various media with importance. Today, the Internet, that amorphous network linking much of the world, is a powerful new media for the imagination of city spaces and hence in need of investigation. In this article, Author focus on the online encyclopaedia [[Wikipedia]], one of the most popular websites on the Internet. My aim is to explore the representations of two of the largest sub-Saharan African cities, Lagos and Kinshasa, in their respective Wikipedia articles. Wikipedia has been described as the encyclopaedia anyone can edit, suggesting that it is open to multiple perspectives on any particular topic. Given the history of how Africa in general has been either marginalized or conjured as an exotic or miserable “other” by much media work this potential for wider range of representations should not be overlooked. Does Wikipedia live up to its [[reputation]] in the case of Kinshasa and Lagos?<br />
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Luyt, Brendan. (2018). "[[Representing African Cities in Wikipedia: the Case of Lagos and Kinshasa]]". SAGE PublicationsSage UK: London, England. DOI: 10.1177/0266666918756171. <br />
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{{cite journal |last1=Luyt |first1=Brendan |title=Representing African Cities in Wikipedia: the Case of Lagos and Kinshasa |date=2018 |doi=10.1177/0266666918756171 |url=https://wikipediaquality.com/wiki/Representing_African_Cities_in_Wikipedia:_the_Case_of_Lagos_and_Kinshasa |journal=SAGE PublicationsSage UK: London, England}}<br />
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Luyt, Brendan. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/Representing_African_Cities_in_Wikipedia:_the_Case_of_Lagos_and_Kinshasa">Representing African Cities in Wikipedia: the Case of Lagos and Kinshasa</a>&amp;quot;. SAGE PublicationsSage UK: London, England. DOI: 10.1177/0266666918756171. <br />
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[[Category:Scientific works]]</div>Beatahttps://wikipediaquality.com/index.php?title=Manipulation_Among_the_Arbiters_of_Collective_Intelligence:_How_Wikipedia_Administrators_Mold_Public_Opinion&diff=27916Manipulation Among the Arbiters of Collective Intelligence: How Wikipedia Administrators Mold Public Opinion2021-02-26T05:01:35Z<p>Beata: + embed code</p>
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<div>{{Infobox work<br />
| title = Manipulation Among the Arbiters of Collective Intelligence: How Wikipedia Administrators Mold Public Opinion<br />
| date = 2013<br />
| authors = [[Sanmay Das]]<br />[[Allen Lavoie]]<br />[[Malik Magdon-Ismail]]<br />
| doi = 10.1145/2505515.2505566<br />
| link = http://dl.acm.org/citation.cfm?doid=2505515.2505566<br />
}}<br />
'''Manipulation Among the Arbiters of Collective Intelligence: How Wikipedia Administrators Mold Public Opinion''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Sanmay Das]], [[Allen Lavoie]] and [[Malik Magdon-Ismail]].<br />
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== Overview ==<br />
Authors reliance on networked, collectively built information is a vulnerability when the quality or [[reliability]] of this information is poor. [[Wikipedia]], one such collectively built information source, is often first stop for information on all kinds of topics; its quality has stood up to many tests, and it prides itself on having a "Neutral Point of View". Enforcement of neutrality is in the hands of comparatively few, powerful administrators. Authors find a surprisingly large number of editors who change their behavior and begin focusing more on a particular controversial topic once they are promoted to administrator status. The conscious and unconscious biases of these few, but powerful, administrators may be shaping the information on many of the most sensitive topics on Wikipedia; some may even be explicitly infiltrating the ranks of administrators in order to promote their own points of view. Neither prior history nor vote counts during an administrator's election can identify those editors most likely to change their behavior in this suspicious manner. Authors find that an alternative measure, which gives more weight to influential voters, can successfully reject these suspicious candidates. This has important implications for how authors harness collective intelligence: even if wisdom exists in a collective opinion (like a vote), that signal can be lost unless authors carefully distinguish the true expert voter from the noisy or manipulative voter.<br />
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Das, Sanmay; Lavoie, Allen; Magdon-Ismail, Malik. (2013). "[[Manipulation Among the Arbiters of Collective Intelligence: How Wikipedia Administrators Mold Public Opinion]]".DOI: 10.1145/2505515.2505566. <br />
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{{cite journal |last1=Das |first1=Sanmay |last2=Lavoie |first2=Allen |last3=Magdon-Ismail |first3=Malik |title=Manipulation Among the Arbiters of Collective Intelligence: How Wikipedia Administrators Mold Public Opinion |date=2013 |doi=10.1145/2505515.2505566 |url=https://wikipediaquality.com/wiki/Manipulation_Among_the_Arbiters_of_Collective_Intelligence:_How_Wikipedia_Administrators_Mold_Public_Opinion}}<br />
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Das, Sanmay; Lavoie, Allen; Magdon-Ismail, Malik. (2013). &amp;quot;<a href="https://wikipediaquality.com/wiki/Manipulation_Among_the_Arbiters_of_Collective_Intelligence:_How_Wikipedia_Administrators_Mold_Public_Opinion">Manipulation Among the Arbiters of Collective Intelligence: How Wikipedia Administrators Mold Public Opinion</a>&amp;quot;.DOI: 10.1145/2505515.2505566. <br />
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