https://wikipediaquality.com/api.php?action=feedcontributions&user=Naomi&feedformat=atomWikipedia Quality - User contributions [en]2024-03-29T07:24:43ZUser contributionsMediaWiki 1.30.0https://wikipediaquality.com/index.php?title=Relation_Extraction_from_Wikipedia_Articles_by_Entities_Clustering&diff=25221Relation Extraction from Wikipedia Articles by Entities Clustering2020-08-18T07:19:33Z<p>Naomi: + categories</p>
<hr />
<div>{{Infobox work<br />
| title = Relation Extraction from Wikipedia Articles by Entities Clustering<br />
| date = 2012<br />
| authors = [[Song Liu]]<br />[[Fuji Ren]]<br />
| doi = 10.1109/CCIS.2012.6664633<br />
| link = <br />
}}<br />
'''Relation Extraction from Wikipedia Articles by Entities Clustering''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Song Liu]] and [[Fuji Ren]].<br />
<br />
== Overview ==<br />
Wikipedia is an encyclopedia based on wiki technology. It is [[multilingual]] high quality knowledge base. In this work a episode based extraction method are proposed to extract relations from [[Wikipedia]] articles. The entities are clustered and labeled. The relation extraction is benefited by the information redundancy provided by the clusters. A strict Wikipedia entities clustering algorithm based on the category system and first sentence of the article is approached. This work required less manual assist. And the relations are abundant. The results are comparable with other works [1, 2].<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Liu, Song; Ren, Fuji. (2012). "[[Relation Extraction from Wikipedia Articles by Entities Clustering]]".DOI: 10.1109/CCIS.2012.6664633. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Liu |first1=Song |last2=Ren |first2=Fuji |title=Relation Extraction from Wikipedia Articles by Entities Clustering |date=2012 |doi=10.1109/CCIS.2012.6664633 |url=https://wikipediaquality.com/wiki/Relation_Extraction_from_Wikipedia_Articles_by_Entities_Clustering}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Liu, Song; Ren, Fuji. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Relation_Extraction_from_Wikipedia_Articles_by_Entities_Clustering">Relation Extraction from Wikipedia Articles by Entities Clustering</a>&amp;quot;.DOI: 10.1109/CCIS.2012.6664633. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Naomihttps://wikipediaquality.com/index.php?title=Smart_Cities_Depictions_in_Wikipedia_Articles:_Reflections_from_a_Text_Analysis_Approach&diff=25220Smart Cities Depictions in Wikipedia Articles: Reflections from a Text Analysis Approach2020-08-18T07:17:04Z<p>Naomi: + Embed</p>
<hr />
<div>{{Infobox work<br />
| title = Smart Cities Depictions in Wikipedia Articles: Reflections from a Text Analysis Approach<br />
| date = 2018<br />
| authors = [[Felippe Cronemberger]]<br />[[J. Ramon Gil-Garcia]]<br />[[Felipe Xavier Costa]]<br />[[Theresa A. Pardo]]<br />
| doi = 10.1145/3209415.3209508<br />
| link = <br />
}}<br />
'''Smart Cities Depictions in Wikipedia Articles: Reflections from a Text Analysis Approach''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Felippe Cronemberger]], [[J. Ramon Gil-Garcia]], [[Felipe Xavier Costa]] and [[Theresa A. Pardo]].<br />
<br />
== Overview ==<br />
Discussions about smart cities continue to attract much attention from researchers, practitioners and citizens at large. As the literature continues to grow the number of [[indicators]] and frameworks for "smartness" are becoming more robust and perspectives on what makes a city smart continue to evolve. This exploratory research analyzes text from 51 [[Wikipedia]] articles that describe cities considered "smart" according to three globally recognized and independent rankings of cities. By comparing findings on word frequencies and word associations found in Wikipedia articles with terminology found in academic literature on smart cities, this study intends to determine to what extent data about smart cities produced through Wikipedia's crowdsourcing approach relates to theoretical developments in the field. This inductive approach may open avenues to the application of automated text analysis methods in theorizing and empirical efforts with information produced about smart cities. This exploratory work may facilitate conceptual understanding of the properties and [[features]] of smart cities and may also open avenues to future applications of alternative conceptualization methods.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Cronemberger, Felippe; Gil-Garcia, J. Ramon; Costa, Felipe Xavier; Pardo, Theresa A.. (2018). "[[Smart Cities Depictions in Wikipedia Articles: Reflections from a Text Analysis Approach]]". ACM Press. DOI: 10.1145/3209415.3209508. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Cronemberger |first1=Felippe |last2=Gil-Garcia |first2=J. Ramon |last3=Costa |first3=Felipe Xavier |last4=Pardo |first4=Theresa A. |title=Smart Cities Depictions in Wikipedia Articles: Reflections from a Text Analysis Approach |date=2018 |doi=10.1145/3209415.3209508 |url=https://wikipediaquality.com/wiki/Smart_Cities_Depictions_in_Wikipedia_Articles:_Reflections_from_a_Text_Analysis_Approach |journal=ACM Press}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Cronemberger, Felippe; Gil-Garcia, J. Ramon; Costa, Felipe Xavier; Pardo, Theresa A.. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/Smart_Cities_Depictions_in_Wikipedia_Articles:_Reflections_from_a_Text_Analysis_Approach">Smart Cities Depictions in Wikipedia Articles: Reflections from a Text Analysis Approach</a>&amp;quot;. ACM Press. DOI: 10.1145/3209415.3209508. <br />
</nowiki><br />
</code></div>Naomihttps://wikipediaquality.com/index.php?title=Empirical_Analysis_of_User_Participation_in_Online_Communities:_the_Case_of_Wikipedia&diff=25219Empirical Analysis of User Participation in Online Communities: the Case of Wikipedia2020-08-18T07:14:46Z<p>Naomi: + category</p>
<hr />
<div>{{Infobox work<br />
| title = Empirical Analysis of User Participation in Online Communities: the Case of Wikipedia<br />
| date = 2010<br />
| authors = [[Giovanni Luca Ciampaglia]]<br />
| link = <br />
}}<br />
'''Empirical Analysis of User Participation in Online Communities: the Case of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Giovanni Luca Ciampaglia]].<br />
<br />
== Overview ==<br />
Authors study the distribution of the activity period of users in five of the largest localized versions of the free, on- line encyclopedia [[Wikipedia]]. Authors find it to be consis- tent with a mixture of two truncated log-normal distri- butions. Using this model, the temporal evolution of these systems can be analyzed, showing that the statis- tical description is consistent over time. contributions: 1. Authors find all datasets to be consistent with the hypothesis that the lifetime of an user account is described by the su- perposition of two truncated log-normal distributions. An interpretation for this phenomenon is that two different regimes govern the participation of individuals to these versions of the Wikipedia project: the occasional users, who fail to find interest in the project after the first few at- tempts to contribute, and the long-term users, whose with- drawal is probably more related to external factors like the loss of personal incentives in contributing and similar causes. 2. Using model, authors characterize how the participation of users over time evolves, as the system ages. Authors find that the statistical description of the one-timers is stable over time, while the properties of the group of long-term users change as a consequence of the aging of the system. 3. Authors find evidence that the inter-edit time distribution de- cays with an heavy tail. In view of this finding, authors check that analysis is not affected by the choice of the pa- rameter used for determining when an user is to be con- sidered "inactive"; authors find that for the one-timers it has no quantitative effect. For the statistics of the long-lived users authors find instead a very good qualitative agreement.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Ciampaglia, Giovanni Luca. (2010). "[[Empirical Analysis of User Participation in Online Communities: the Case of Wikipedia]]".<br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Ciampaglia |first1=Giovanni Luca |title=Empirical Analysis of User Participation in Online Communities: the Case of Wikipedia |date=2010 |url=https://wikipediaquality.com/wiki/Empirical_Analysis_of_User_Participation_in_Online_Communities:_the_Case_of_Wikipedia}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Ciampaglia, Giovanni Luca. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Empirical_Analysis_of_User_Participation_in_Online_Communities:_the_Case_of_Wikipedia">Empirical Analysis of User Participation in Online Communities: the Case of Wikipedia</a>&amp;quot;.<br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Naomihttps://wikipediaquality.com/index.php?title=Constructing_a_Global_Ontology_by_Concept_Mapping_Using_Wikipedia_Thesaurus&diff=25218Constructing a Global Ontology by Concept Mapping Using Wikipedia Thesaurus2020-08-18T07:12:07Z<p>Naomi: + links</p>
<hr />
<div>'''Constructing a Global Ontology by Concept Mapping Using Wikipedia Thesaurus''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Minghua Pei]], [[Kotaro Nakayama]], [[Takahiro Hara]] and [[Shojiro Nishio]].<br />
<br />
== Overview ==<br />
Recently, the importance of semantics on the WWW is widely recognized and a lot of [[semantic information]] (RDF OWL etc.) is being built/published on the WWW. However, the lack of [[ontology]] mappings becomes a serious problem for the semantic Web since it needs well defined relations to retrieve information correctly by inferring the meaning of information. One to one mapping is not an efficient method due to the nature of distributed environment. Therefore, it would be a considerable method to map the concepts by using a large-scale intermediate ontology. On the other hand, [[Wikipedia]] is a large-scale of concept network covering almost all concepts in the real world. In this paper, authors propose an intermediate ontology construction method using Wikipedia Thesaurus, an association thesaurus extracted from Wikipedia. Since Wikipedia Thesaurus provides associated concepts without explicit relation type, authors propose an approach of concept mapping using two sub methods; "name mapping" and "logic-based mapping".</div>Naomihttps://wikipediaquality.com/index.php?title=Authoring_the_Neighbourhood_in_Wikipedia&diff=25217Authoring the Neighbourhood in Wikipedia2020-08-18T07:10:47Z<p>Naomi: infobox</p>
<hr />
<div>{{Infobox work<br />
| title = Authoring the Neighbourhood in Wikipedia<br />
| date = 2016<br />
| authors = [[Rebecca Ross]]<br />[[Chi Nguyen]]<br />
| link = http://ualresearchonline.arts.ac.uk/11953/<br />
}}<br />
'''Authoring the Neighbourhood in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Rebecca Ross]] and [[Chi Nguyen]].<br />
<br />
== Overview ==<br />
Engaged Urbanism showcases the exciting ways in which urbanists are responding to this question and working towards fairer cities. Its authors offer succinct, candid and carefully illustrated commentaries on the trials and successes of risk-taking research, revealing how they collaborate across fields of expertise, inventing or adapting methods to suit bespoke situations. Featuring novel uses and combinations of practice-from activism, architectural design and undercover journalism, to film, sculpture, performance and photography- in a diversity of cities such as Beirut, Johannesburg, Kisumu, London and Rio de Janeiro, Engaged Urbanism demonstrates how some of the greatest challenges for present and future populations are being rigorously and creatively addressed.</div>Naomihttps://wikipediaquality.com/index.php?title=Ways_of_Worldmaking_in_Wikipedia:_Reality,_Legitimacy_and_Collaborative_Knowledge_Making&diff=25216Ways of Worldmaking in Wikipedia: Reality, Legitimacy and Collaborative Knowledge Making2020-08-18T07:08:59Z<p>Naomi: Categories</p>
<hr />
<div>{{Infobox work<br />
| title = Ways of Worldmaking in Wikipedia: Reality, Legitimacy and Collaborative Knowledge Making<br />
| date = 2014<br />
| authors = [[Lindsay Fullerton]]<br />[[James S. Ettema]]<br />
| doi = 10.1177/0163443713515739<br />
| link = https://www.scholars.northwestern.edu/en/publications/ways-of-worldmaking-in-wikipedia-reality-legitimacy-and-collabora<br />
}}<br />
'''Ways of Worldmaking in Wikipedia: Reality, Legitimacy and Collaborative Knowledge Making''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Lindsay Fullerton]] and [[James S. Ettema]].<br />
<br />
== Overview ==<br />
The on-going social construction of reality, according to Berger and Luckmann’s classic treatise, entails both an explanation of the social order which ascribes “cognitive validity to its objectivated meanings” and a justification of that order which provides “a normative dignity to its practical imperatives.” The implication is that knowledge of social reality integrates cognitive facts and normative values to continuously legitimize that reality. Authors explore this integration of fact and value in an unexpected setting: the “[[talk pages]]” of the online encyclopedia [[Wikipedia]] in which discussions of article creation are recorded. Authors analysis of these discussions draws on Nelson Goodman’s Ways of Worldmaking, another classic on the social construction of reality, which catalogues strategies for producing a worldview. Authors utilize Goodman’s theories in four cases of Wikipedia article creation – two histories, “Iraq War” and “Afghanistan War,” and two biographies, “George W. Bush” and “Barack Obama” – all of ...<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Fullerton, Lindsay; Ettema, James S.. (2014). "[[Ways of Worldmaking in Wikipedia: Reality, Legitimacy and Collaborative Knowledge Making]]". SAGE Publications. DOI: 10.1177/0163443713515739. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Fullerton |first1=Lindsay |last2=Ettema |first2=James S. |title=Ways of Worldmaking in Wikipedia: Reality, Legitimacy and Collaborative Knowledge Making |date=2014 |doi=10.1177/0163443713515739 |url=https://wikipediaquality.com/wiki/Ways_of_Worldmaking_in_Wikipedia:_Reality,_Legitimacy_and_Collaborative_Knowledge_Making |journal=SAGE Publications}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Fullerton, Lindsay; Ettema, James S.. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Ways_of_Worldmaking_in_Wikipedia:_Reality,_Legitimacy_and_Collaborative_Knowledge_Making">Ways of Worldmaking in Wikipedia: Reality, Legitimacy and Collaborative Knowledge Making</a>&amp;quot;. SAGE Publications. DOI: 10.1177/0163443713515739. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Naomihttps://wikipediaquality.com/index.php?title=Creating_Categories_for_Wikipedia_Articles_Using_Self-Organizing_Maps&diff=25215Creating Categories for Wikipedia Articles Using Self-Organizing Maps2020-08-18T07:06:26Z<p>Naomi: Category</p>
<hr />
<div>{{Infobox work<br />
| title = Creating Categories for Wikipedia Articles Using Self-Organizing Maps<br />
| date = 2011<br />
| authors = [[Julian Szymański]]<br />
| doi = 10.1109/CCCA.2011.6031483<br />
| link = http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&amp;arnumber=6031483<br />
}}<br />
'''Creating Categories for Wikipedia Articles Using Self-Organizing Maps''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Julian Szymański]].<br />
<br />
== Overview ==<br />
The article presents the results of the experiments performed on selected sub-set of [[Wikipedia]] which authors categorized automaticly. Authors analyze two methods of text representation: based on references and word content. Using them authors introduced joint representation that has been used to build groups of similar articles based on Kohonen Self-Organizing Maps. To fulfill efficiency of the data processing, authors performed dimensionality reduction of raw data using Principal Component Analysis performed on similarity matrix. Changing the granularity of SOM network allows to build hierarchical [[categories]] and find significant relations between articles in documents repository.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Szymański, Julian. (2011). "[[Creating Categories for Wikipedia Articles Using Self-Organizing Maps]]".DOI: 10.1109/CCCA.2011.6031483. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Szymański |first1=Julian |title=Creating Categories for Wikipedia Articles Using Self-Organizing Maps |date=2011 |doi=10.1109/CCCA.2011.6031483 |url=https://wikipediaquality.com/wiki/Creating_Categories_for_Wikipedia_Articles_Using_Self-Organizing_Maps}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Szymański, Julian. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Creating_Categories_for_Wikipedia_Articles_Using_Self-Organizing_Maps">Creating Categories for Wikipedia Articles Using Self-Organizing Maps</a>&amp;quot;.DOI: 10.1109/CCCA.2011.6031483. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Naomihttps://wikipediaquality.com/index.php?title=The_Intelligible_as_a_New_World%3F_Wikipedia_Versus_the_Eighteenth-Century_Encyclopedie&diff=25214The Intelligible as a New World? Wikipedia Versus the Eighteenth-Century Encyclopedie2020-08-18T07:03:49Z<p>Naomi: Embed for English Wikipedia, HTML</p>
<hr />
<div>{{Infobox work<br />
| title = The Intelligible as a New World? Wikipedia Versus the Eighteenth-Century Encyclopedie<br />
| date = 2011<br />
| authors = [[Sanja Perovic]]<br />
| doi = 10.3366/para.2011.0003<br />
| link = https://www.euppublishing.com/doi/abs/10.3366/para.2011.0003<br />
}}<br />
'''The Intelligible as a New World? Wikipedia Versus the Eighteenth-Century Encyclopedie''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Sanja Perovic]].<br />
<br />
== Overview ==<br />
For some time now, certain theorists have been urging us to move beyond text-based understandings of culture to consider the impact of new media on the structure and organization of knowledge. This article, however, reconsiders the usual priority given to digital media by comparing [[Wikipedia]], the free, user-led online Encyclopedia, with Diderot and D'Alembert's eighteenth-century Encyclopedie. It begins by suggesting that the dichotomy between information system and text is not sufficient for describing the differences between the two. It then considers more closely the type of critical thinking presupposed by the Encyclopedie. It concludes by raising the question of the role of judgement in making sense of any encyclopedia in a modern world in which knowledge systems only coexist on the condition of being partially blind to one another.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Perovic, Sanja. (2011). "[[The Intelligible as a New World? Wikipedia Versus the Eighteenth-Century Encyclopedie]]". Edinburgh University Press 22 George Square, Edinburgh EH8 9LF UK. DOI: 10.3366/para.2011.0003. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Perovic |first1=Sanja |title=The Intelligible as a New World? Wikipedia Versus the Eighteenth-Century Encyclopedie |date=2011 |doi=10.3366/para.2011.0003 |url=https://wikipediaquality.com/wiki/The_Intelligible_as_a_New_World?_Wikipedia_Versus_the_Eighteenth-Century_Encyclopedie |journal=Edinburgh University Press 22 George Square, Edinburgh EH8 9LF UK}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Perovic, Sanja. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/The_Intelligible_as_a_New_World?_Wikipedia_Versus_the_Eighteenth-Century_Encyclopedie">The Intelligible as a New World? Wikipedia Versus the Eighteenth-Century Encyclopedie</a>&amp;quot;. Edinburgh University Press 22 George Square, Edinburgh EH8 9LF UK. DOI: 10.3366/para.2011.0003. <br />
</nowiki><br />
</code></div>Naomihttps://wikipediaquality.com/index.php?title=The_Richness_and_Reach_of_Wikinomics:_is_the_Free_Web-Based_Encyclopedia_Wikipedia_Only_for_the_Rich_Countries%3F&diff=25213The Richness and Reach of Wikinomics: is the Free Web-Based Encyclopedia Wikipedia Only for the Rich Countries?2020-08-18T07:02:38Z<p>Naomi: infobox</p>
<hr />
<div>{{Infobox work<br />
| title = The Richness and Reach of Wikinomics: is the Free Web-Based Encyclopedia Wikipedia Only for the Rich Countries?<br />
| date = 2007<br />
| authors = [[Morten Rask]]<br />
| link = https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID996158_code681353.pdf?abstractid=996158&amp;mirid=5<br />
}}<br />
'''The Richness and Reach of Wikinomics: is the Free Web-Based Encyclopedia Wikipedia Only for the Rich Countries?''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Morten Rask]].<br />
<br />
== Overview ==<br />
In this paper, a model of the patterns of correlation in [[Wikipedia]], reach and richness, lays the foundation for studying whether or not the free web-based encyclopedia Wikipedia is only for developed countries. Wikipedia is used in this paper, as an illustrative case study for the enormous rise of the so-called Web 2.0 applications, a subject which has become associated with many golden promises: Instead of being at the outskirts of the global economy, the development of free or low-cost internet-based content and applications, makes it possible for poor, emerging, and transition countries to compete and collaborate on the same level as developed countries. Based upon data from 12 different Wikipedia language editions, authors find that the central structural effect is on the level of human development in the current country. In other words, Wikipedia is in general, more for rich countries than for less developed countries. It is suggested that policy makers make investments in increasing the general level of literacy, education, and standard of living in their country. The main managerial implication for businesses, that will expand their [[social network]] applications to other countries, is to use the model of the patterns of correlation in Wikipedia, reach and richness, as a market screening and monitoring model.</div>Naomihttps://wikipediaquality.com/index.php?title=Visualizing_Co-Authorship_Networks_in_Online_Wikipedia&diff=25212Visualizing Co-Authorship Networks in Online Wikipedia2020-08-18T07:01:29Z<p>Naomi: Adding categories</p>
<hr />
<div>{{Infobox work<br />
| title = Visualizing Co-Authorship Networks in Online Wikipedia<br />
| date = 2006<br />
| authors = [[Robert P. Biuk-Aghai]]<br />
| doi = 10.1109/ISCIT.2006.339838<br />
| link = http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4141483<br />
}}<br />
'''Visualizing Co-Authorship Networks in Online Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2006, written by [[Robert P. Biuk-Aghai]].<br />
<br />
== Overview ==<br />
The [[Wikipedia]] online user-contributed encyclopedia has rapidly become a highly popular and widely used online reference source. However, perceiving the complex relationships in the network of articles and other entities in Wikipedia is far from easy. Authors introduce the notion of using co-authorship of articles to determine relationship between articles, and present the WikiVis information visualization system which visualizes this and other types of relationships in the Wikipedia database in 3D graph form. A 3D star layout and a 3D nested cone tree layout are presented for displaying relationships between entities and between [[categories]], respectively. A novel 3D pinboard layout is presented for displaying search results.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Biuk-Aghai, Robert P.. (2006). "[[Visualizing Co-Authorship Networks in Online Wikipedia]]".DOI: 10.1109/ISCIT.2006.339838. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Biuk-Aghai |first1=Robert P. |title=Visualizing Co-Authorship Networks in Online Wikipedia |date=2006 |doi=10.1109/ISCIT.2006.339838 |url=https://wikipediaquality.com/wiki/Visualizing_Co-Authorship_Networks_in_Online_Wikipedia}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Biuk-Aghai, Robert P.. (2006). &amp;quot;<a href="https://wikipediaquality.com/wiki/Visualizing_Co-Authorship_Networks_in_Online_Wikipedia">Visualizing Co-Authorship Networks in Online Wikipedia</a>&amp;quot;.DOI: 10.1109/ISCIT.2006.339838. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Naomihttps://wikipediaquality.com/index.php?title=A_Method_for_Recommending_the_Most_Appropriate_Expansion_of_Acronyms_Using_Wikipedia&diff=25211A Method for Recommending the Most Appropriate Expansion of Acronyms Using Wikipedia2020-08-18T06:59:28Z<p>Naomi: Int.links</p>
<hr />
<div>'''A Method for Recommending the Most Appropriate Expansion of Acronyms Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Dongjin Choi]], [[Juhyun Shin]], [[Eunji Lee]] and [[Pankoo Kim]].<br />
<br />
== Overview ==<br />
Over the years, many researchers have been studied to detect expansions of acronyms in texts by using linguistic and syntactical approaches in order to overcome disambiguation problems. Acronym is an abbreviation formed which is composed of initial components of single or multiple words. These initial components bring huge mistakes when a machine conducts experiments to find meaning from given texts. Detecting expansions of acronyms is not a big issue now days. The problem is that a polysemous acronym. In order to solve this problem, this paper proposes a method to recommend the most related expansion of acronym through analyzing co-occurrence words by using [[Wikipedia]]. Authors goal is not finding acronym definition or expansion but recommending the most appropriate expansion of given acronyms.</div>Naomihttps://wikipediaquality.com/index.php?title=Why_We_Read_Wikipedia&diff=25210Why We Read Wikipedia2020-08-18T06:57:24Z<p>Naomi: Adding embed</p>
<hr />
<div>{{Infobox work<br />
| title = Why We Read Wikipedia<br />
| date = 2017<br />
| authors = [[Philipp Singer]]<br />[[Florian Lemmerich]]<br />[[Robert West]]<br />[[Leila Zia]]<br />[[Ellery Wulczyn]]<br />[[Markus Strohmaier]]<br />[[Jure Leskovec]]<br />
| doi = 10.1145/3038912.3052716<br />
| link = https://dl.acm.org/citation.cfm?id=3052716<br />
| plink = https://arxiv.org/pdf/1702.05379<br />
}}<br />
'''Why We Read Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Philipp Singer]], [[Florian Lemmerich]], [[Robert West]], [[Leila Zia]], [[Ellery Wulczyn]], [[Markus Strohmaier]] and [[Jure Leskovec]].<br />
<br />
== Overview ==<br />
Wikipedia is one of the most popular sites on the Web, with millions of users relying on it to satisfy a broad range of information needs every day. Although it is crucial to understand what exactly these needs are in order to be able to meet them, little is currently known about why users visit [[Wikipedia]]. The goal of this paper is to fill this gap by combining a survey of Wikipedia readers with a log-based analysis of user activity. Based on an initial series of user surveys, authors build a taxonomy of Wikipedia use cases along several dimensions, capturing users' motivations to visit Wikipedia, the depth of knowledge they are seeking, and their knowledge of the topic of interest prior to visiting Wikipedia. Then, authors quantify the prevalence of these use cases via a large-scale user survey conducted on live Wikipedia with almost 30,000 responses. Authors analyses highlight the variety of factors driving users to Wikipedia, such as current events, media coverage of a topic, personal curiosity, work or school assignments, or boredom. Finally, authors match survey responses to the respondents' digital traces in Wikipedia's server logs, enabling the discovery of behavioral patterns associated with specific use cases. For instance, authors observe long and fast-paced page sequences across topics for users who are bored or exploring randomly, whereas those using Wikipedia for work or school spend more time on individual articles focused on topics such as science. Authors findings advance understanding of reader motivations and behavior on Wikipedia and can have implications for developers aiming to improve Wikipedia's user experience, editors striving to cater to their readers' needs, third-party services (such as search engines) providing access to Wikipedia content, and researchers aiming to build tools such as recommendation engines.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Singer, Philipp; Lemmerich, Florian; West, Robert; Zia, Leila; Wulczyn, Ellery; Strohmaier, Markus; Leskovec, Jure. (2017). "[[Why We Read Wikipedia]]". International World Wide Web Conferences Steering Committee. DOI: 10.1145/3038912.3052716. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Singer |first1=Philipp |last2=Lemmerich |first2=Florian |last3=West |first3=Robert |last4=Zia |first4=Leila |last5=Wulczyn |first5=Ellery |last6=Strohmaier |first6=Markus |last7=Leskovec |first7=Jure |title=Why We Read Wikipedia |date=2017 |doi=10.1145/3038912.3052716 |url=https://wikipediaquality.com/wiki/Why_We_Read_Wikipedia |journal=International World Wide Web Conferences Steering Committee}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Singer, Philipp; Lemmerich, Florian; West, Robert; Zia, Leila; Wulczyn, Ellery; Strohmaier, Markus; Leskovec, Jure. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Why_We_Read_Wikipedia">Why We Read Wikipedia</a>&amp;quot;. International World Wide Web Conferences Steering Committee. DOI: 10.1145/3038912.3052716. <br />
</nowiki><br />
</code></div>Naomihttps://wikipediaquality.com/index.php?title=Evaluating_a_Statistical_Ccg_Parser_on_Wikipedia&diff=25209Evaluating a Statistical Ccg Parser on Wikipedia2020-08-18T06:55:02Z<p>Naomi: Adding embed</p>
<hr />
<div>{{Infobox work<br />
| title = Evaluating a Statistical Ccg Parser on Wikipedia<br />
| date = 2009<br />
| authors = [[Matthew Honnibal]]<br />[[Joel Nothman]]<br />[[James R. Curran]]<br />
| doi = 10.3115/1699765.1699771<br />
| link = http://dl.acm.org/ft_gateway.cfm?id=1699771&amp;type=pdf<br />
}}<br />
'''Evaluating a Statistical Ccg Parser on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Matthew Honnibal]], [[Joel Nothman]] and [[James R. Curran]].<br />
<br />
== Overview ==<br />
The vast majority of parser evaluation is conducted on the 1984 Wall Street Journal (WSJ). In-domain evaluation of this kind is important for system development, but gives little indication about how the parser will perform on many practical problems. [[Wikipedia]] is an interesting domain for parsing that has so far been under-explored. Authors present statistical parsing results that for the first time provide information about what sort of performance a user parsing Wikipedia text can expect.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Honnibal, Matthew; Nothman, Joel; Curran, James R.. (2009). "[[Evaluating a Statistical Ccg Parser on Wikipedia]]". Association for Computational Linguistics. DOI: 10.3115/1699765.1699771. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Honnibal |first1=Matthew |last2=Nothman |first2=Joel |last3=Curran |first3=James R. |title=Evaluating a Statistical Ccg Parser on Wikipedia |date=2009 |doi=10.3115/1699765.1699771 |url=https://wikipediaquality.com/wiki/Evaluating_a_Statistical_Ccg_Parser_on_Wikipedia |journal=Association for Computational Linguistics}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Honnibal, Matthew; Nothman, Joel; Curran, James R.. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Evaluating_a_Statistical_Ccg_Parser_on_Wikipedia">Evaluating a Statistical Ccg Parser on Wikipedia</a>&amp;quot;. Association for Computational Linguistics. DOI: 10.3115/1699765.1699771. <br />
</nowiki><br />
</code></div>Naomihttps://wikipediaquality.com/index.php?title=Beyond_the_Encyclopedia:_Collective_Memories_in_Wikipedia&diff=25208Beyond the Encyclopedia: Collective Memories in Wikipedia2020-08-18T06:52:54Z<p>Naomi: cats.</p>
<hr />
<div>{{Infobox work<br />
| title = Beyond the Encyclopedia: Collective Memories in Wikipedia<br />
| date = 2014<br />
| authors = [[Michela Ferron]]<br />[[Paolo Massa]]<br />
| doi = 10.1177/1750698013490590<br />
| link = http://journals.sagepub.com/doi/abs/10.1177/1750698013490590<br />
}}<br />
'''Beyond the Encyclopedia: Collective Memories in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Michela Ferron]] and [[Paolo Massa]].<br />
<br />
== Overview ==<br />
Collective memory processes have been studied from many different perspectives. For example, while psychology has investigated collaborative recall in small groups, other research traditions have focused on flashbulb memories or on the cultural processes involved in the formation of collective memories of entire nations. In this article, considering the online encyclopedia [[Wikipedia]] as a global memory place, authors analyze online commemoration patterns of traumatic events. Authors extracted 88 articles and [[talk pages]] related to traumatic events, and using logistic regression, authors analyzed their edit activity comparing it with more than 370,000 other Wikipedia pages. Results show that the relative amount of edits during anniversaries can significantly distinguish between pages related to traumatic events and other pages. The logistic regression results, together with the transcription of a group of messages exchanged by the users during the anniversaries of the September 11 attacks and the Virginia Tech massacre, su...<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Ferron, Michela; Massa, Paolo. (2014). "[[Beyond the Encyclopedia: Collective Memories in Wikipedia]]". SAGE Publications. DOI: 10.1177/1750698013490590. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Ferron |first1=Michela |last2=Massa |first2=Paolo |title=Beyond the Encyclopedia: Collective Memories in Wikipedia |date=2014 |doi=10.1177/1750698013490590 |url=https://wikipediaquality.com/wiki/Beyond_the_Encyclopedia:_Collective_Memories_in_Wikipedia |journal=SAGE Publications}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Ferron, Michela; Massa, Paolo. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Beyond_the_Encyclopedia:_Collective_Memories_in_Wikipedia">Beyond the Encyclopedia: Collective Memories in Wikipedia</a>&amp;quot;. SAGE Publications. DOI: 10.1177/1750698013490590. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Naomihttps://wikipediaquality.com/index.php?title=Quantifying_National_Information_Interests_Using_the_Activity_of_Wikipedia_Editors&diff=25207Quantifying National Information Interests Using the Activity of Wikipedia Editors2020-08-18T06:50:56Z<p>Naomi: Embed</p>
<hr />
<div>{{Infobox work<br />
| title = Quantifying National Information Interests Using the Activity of Wikipedia Editors<br />
| date = 2015<br />
| authors = [[Fariba Karimi]]<br />[[Ludvig Bohlin]]<br />[[Anna Samoilenko]]<br />[[Martin Rosvall]]<br />[[Andrea Lancichinetti]]<br />
| link = <br />
}}<br />
'''Quantifying National Information Interests Using the Activity of Wikipedia Editors''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Fariba Karimi]], [[Ludvig Bohlin]], [[Anna Samoilenko]], [[Martin Rosvall]] and [[Andrea Lancichinetti]].<br />
<br />
== Overview ==<br />
Authors live in a "global village" where electronic communication has eliminated the geographical barriers of information exchange. With global information exchange, the road is open to worldwide convergence of opinions and interests. However, it remains unknown to what extent interests actually have become global. To address how interests differ between countries, authors analyze the information exchange in [[Wikipedia]], the largest online collaborative encyclopedia. From the editing activity in Wikipedia, authors extract the interest profiles of editors from different countries. Based on a statistical null model for interest profiles, authors create a network of significant links between countries with similar interests. Authors show that countries are divided into 18 clusters with similar interest profiles in which language, geography, and historical background polarize the interests. Despite the opportunities of global communication, the results suggest that people nevertheless care about local information.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Karimi, Fariba; Bohlin, Ludvig; Samoilenko, Anna; Rosvall, Martin; Lancichinetti, Andrea. (2015). "[[Quantifying National Information Interests Using the Activity of Wikipedia Editors]]".<br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Karimi |first1=Fariba |last2=Bohlin |first2=Ludvig |last3=Samoilenko |first3=Anna |last4=Rosvall |first4=Martin |last5=Lancichinetti |first5=Andrea |title=Quantifying National Information Interests Using the Activity of Wikipedia Editors |date=2015 |url=https://wikipediaquality.com/wiki/Quantifying_National_Information_Interests_Using_the_Activity_of_Wikipedia_Editors}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Karimi, Fariba; Bohlin, Ludvig; Samoilenko, Anna; Rosvall, Martin; Lancichinetti, Andrea. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Quantifying_National_Information_Interests_Using_the_Activity_of_Wikipedia_Editors">Quantifying National Information Interests Using the Activity of Wikipedia Editors</a>&amp;quot;.<br />
</nowiki><br />
</code></div>Naomihttps://wikipediaquality.com/index.php?title=Wikipedia_as_Frame_Information_Repository&diff=25206Wikipedia as Frame Information Repository2020-08-18T06:49:48Z<p>Naomi: Embed</p>
<hr />
<div>{{Infobox work<br />
| title = Wikipedia as Frame Information Repository<br />
| date = 2009<br />
| authors = [[Sara Tonelli]]<br />[[Claudio Giuliano]]<br />
| doi = 10.3115/1699510.1699547<br />
| link = http://dl.acm.org/citation.cfm?id=1699510.1699547<br />
| plink = https://www.semanticscholar.org/paper/Wikipedia-as-Frame-Information-Repository-Tonelli-Giuliano/d43720e843415cb1fdfa69ccde8f70c93d746b2e<br />
}}<br />
'''Wikipedia as Frame Information Repository''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Sara Tonelli]] and [[Claudio Giuliano]].<br />
<br />
== Overview ==<br />
In this paper, authors address the issue of automatic extending lexical resources by exploiting existing knowledge repositories. In particular, authors deal with the new task of linking FrameNet and [[Wikipedia]] using a word sense disambiguation system that, for a given pair frame -- lexical unit (F, l), finds the Wikipage that best expresses the the meaning of l. The mapping can be exploited to straightforwardly acquire new example sentences and new lexical units, both for English and for all languages available in Wikipedia. In this way, it is possible to easily acquire good-quality data as a starting point for the creation of FrameNet in new languages. The evaluation reported both for the monolingual and the [[multilingual]] expansion of FrameNet shows that the approach is promising.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Tonelli, Sara; Giuliano, Claudio. (2009). "[[Wikipedia as Frame Information Repository]]". Association for Computational Linguistics. DOI: 10.3115/1699510.1699547. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Tonelli |first1=Sara |last2=Giuliano |first2=Claudio |title=Wikipedia as Frame Information Repository |date=2009 |doi=10.3115/1699510.1699547 |url=https://wikipediaquality.com/wiki/Wikipedia_as_Frame_Information_Repository |journal=Association for Computational Linguistics}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Tonelli, Sara; Giuliano, Claudio. (2009). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedia_as_Frame_Information_Repository">Wikipedia as Frame Information Repository</a>&amp;quot;. Association for Computational Linguistics. DOI: 10.3115/1699510.1699547. <br />
</nowiki><br />
</code></div>Naomihttps://wikipediaquality.com/index.php?title=Quantitative_Analysis_of_the_Wikipedia_Community_of_Users&diff=25205Quantitative Analysis of the Wikipedia Community of Users2020-08-18T06:47:18Z<p>Naomi: cats.</p>
<hr />
<div>{{Infobox work<br />
| title = Quantitative Analysis of the Wikipedia Community of Users<br />
| date = 2007<br />
| authors = [[Felipe Ortega]]<br />[[Jesus M. Gonzalez-Barahona]]<br />
| link = https://link.springer.com/content/pdf/10.1057%2Fcrr.2016.3.pdf<br />
| plink = https://www.researchgate.net/profile/Felipe_Ortega2/publication/200772797_Quantitative_analysis_of_the_wikipedia_community_of_users/links/02e7e5242e2a4008fb000000.pdf<br />
}}<br />
'''Quantitative Analysis of the Wikipedia Community of Users''' - scientific work related to [[Wikipedia quality]] published in 2007, written by [[Felipe Ortega]] and [[Jesus M. Gonzalez-Barahona]].<br />
<br />
== Overview ==<br />
Many activities of editors in [[Wikipedia]] can be traced using its database dumps, which register detailed information about every single change to every article. Several researchers have used this information to gain knowledge about the production process of articles, and about activity patterns of authors. In this analysis, authors have focused on one of those previous works, by Kittur et al. First, authors have followed the same methodology with more recent and comprehensive data. Then, authors have extended this methodology to precisely identify which fraction of authors are producing most of the changes in Wikipedia’s articles, and how the behaviour of these authors evolves over time. This enabled us not only to validate some of the previous results, but also to find new interesting evidences. Authors have found that the analysis of sysops is not a good method for estimating different levels of contributions, since it is dependent on the policy for electing them (which changes over time and for each language). Moreover,we have found new activity patterns classifying authors by their contributions during specific periods of time, instead of using their total number of contributions over the whole life of Wikipedia. Finally, authors present a tool that automates this extended methodology, implementing a quick and complete quantitative analysis of every language edition in Wikipedia.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Ortega, Felipe; Gonzalez-Barahona, Jesus M.. (2007). "[[Quantitative Analysis of the Wikipedia Community of Users]]".<br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Ortega |first1=Felipe |last2=Gonzalez-Barahona |first2=Jesus M. |title=Quantitative Analysis of the Wikipedia Community of Users |date=2007 |url=https://wikipediaquality.com/wiki/Quantitative_Analysis_of_the_Wikipedia_Community_of_Users}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Ortega, Felipe; Gonzalez-Barahona, Jesus M.. (2007). &amp;quot;<a href="https://wikipediaquality.com/wiki/Quantitative_Analysis_of_the_Wikipedia_Community_of_Users">Quantitative Analysis of the Wikipedia Community of Users</a>&amp;quot;.<br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Naomihttps://wikipediaquality.com/index.php?title=Crowd_Governance:_the_Monitoring_Role_of_Wikipedia_in_the_Financial_Market&diff=25204Crowd Governance: the Monitoring Role of Wikipedia in the Financial Market2020-08-18T06:44:22Z<p>Naomi: + embed code</p>
<hr />
<div>{{Infobox work<br />
| title = Crowd Governance: the Monitoring Role of Wikipedia in the Financial Market<br />
| date = 2014<br />
| authors = [[Weifang Wu]]<br />[[Xiaoquan Michael Zhang]]<br />[[Rong Zheng]]<br />
| link = http://misrc.umn.edu/wise/2014_Papers/110.pdf<br />
}}<br />
'''Crowd Governance: the Monitoring Role of Wikipedia in the Financial Market''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Weifang Wu]], [[Xiaoquan Michael Zhang]] and [[Rong Zheng]].<br />
<br />
== Overview ==<br />
In this study, authors explore whether [[Wikipedia]] plays a governance role in the financial market by reducing the information disadvantage of individual investors. Authors hypothesize that the aggregation of information on Wikipedia enables individual investors to collectively monitor insiders and institutional investors. Using the creation of a firm Wikipedia page as an information event, empirical results support hypothesis and further show that the governance effect is stronger for firms with higher institutional ownership concentration as well as those with more intensive insider trading activity. Taken together, these findings support the view that Wikipedia helps mitigate the information asymmetry among individual investors, institutional investors, and corporate insiders.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Wu, Weifang; Zhang, Xiaoquan Michael; Zheng, Rong. (2014). "[[Crowd Governance: the Monitoring Role of Wikipedia in the Financial Market]]".<br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Wu |first1=Weifang |last2=Zhang |first2=Xiaoquan Michael |last3=Zheng |first3=Rong |title=Crowd Governance: the Monitoring Role of Wikipedia in the Financial Market |date=2014 |url=https://wikipediaquality.com/wiki/Crowd_Governance:_the_Monitoring_Role_of_Wikipedia_in_the_Financial_Market}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Wu, Weifang; Zhang, Xiaoquan Michael; Zheng, Rong. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Crowd_Governance:_the_Monitoring_Role_of_Wikipedia_in_the_Financial_Market">Crowd Governance: the Monitoring Role of Wikipedia in the Financial Market</a>&amp;quot;.<br />
</nowiki><br />
</code></div>Naomihttps://wikipediaquality.com/index.php?title=Tractable_Probabilistic_Knowledge_Bases:_Wikipedia_and_Beyond&diff=25203Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond2020-08-18T06:41:26Z<p>Naomi: cat.</p>
<hr />
<div>{{Infobox work<br />
| title = Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond<br />
| date = 2014<br />
| authors = [[Mathias Niepert]]<br />[[Pedro M. Domingos]]<br />
| link = https://dl.acm.org/citation.cfm?id=2908353<br />
}}<br />
'''Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Mathias Niepert]] and [[Pedro M. Domingos]].<br />
<br />
== Overview ==<br />
Building large-scale knowledge bases from a variety of data sources is a longstanding goal of AI research. However, existing approaches either ignore the uncertainty inherent to knowledge extracted from text, the web, and other sources, or lack a consistent probabilistic semantics with tractable inference. To address this problem, authors present a framework for tractable probabilistic knowledge bases (TPKBs). TPKBs consist of a hierarchy of classes of objects and a hierarchy of classes of object pairs such that attributes and relations are independent conditioned on those classes. These characteristics facilitate both tractable probabilistic reasoning and tractable maximum-likelihood parameter learning. TPKBs feature a rich query language that allows one to express and infer complex relationships between classes, relations, objects, and their attributes. The queries are translated to sequences of operations in a relational database facilitating query execution times in the sub-second range. Authors demonstrate the power of TPKBs by leveraging large data sets extracted from [[Wikipedia]] to learn their structure and parameters. The resulting TPKB models a distribution over millions of objects and billions of parameters. Authors apply the TPKB to entity resolution and object linking problems and show that the TPKB can accurately align large knowledge bases and integrate triples from open IE projects.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Niepert, Mathias; Domingos, Pedro M.. (2014). "[[Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond]]". AAAI Press. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Niepert |first1=Mathias |last2=Domingos |first2=Pedro M. |title=Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond |date=2014 |url=https://wikipediaquality.com/wiki/Tractable_Probabilistic_Knowledge_Bases:_Wikipedia_and_Beyond |journal=AAAI Press}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Niepert, Mathias; Domingos, Pedro M.. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Tractable_Probabilistic_Knowledge_Bases:_Wikipedia_and_Beyond">Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond</a>&amp;quot;. AAAI Press. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Naomihttps://wikipediaquality.com/index.php?title=Detecting_Biased_Statements_in_Wikipedia&diff=25202Detecting Biased Statements in Wikipedia2020-08-18T06:38:24Z<p>Naomi: Infobox work</p>
<hr />
<div>{{Infobox work<br />
| title = Detecting Biased Statements in Wikipedia<br />
| date = 2018<br />
| authors = [[Christoph Hube]]<br />[[Besnik Fetahu]]<br />
| doi = 10.1145/3184558.3191640<br />
| link = https://dl.acm.org/citation.cfm?doid=3184558.3191640<br />
}}<br />
'''Detecting Biased Statements in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Christoph Hube]] and [[Besnik Fetahu]].<br />
<br />
== Overview ==<br />
Quality in [[Wikipedia]] is enforced through a set of editing policies and guidelines recommended for [[Wikipedia editors]]. Neutral point of view (NPOV) is one of the main principles in Wikipedia, which ensures that for controversial information all possible points of view are represented proportionally. Furthermore, language used in Wikipedia should be neutral and not opinionated. However, due to the large number of Wikipedia articles and its operating principle based on a voluntary basis of Wikipedia editors; quality assurances and Wikipedia guidelines cannot always be enforced. Currently, there are more than 40,000 articles, which are flagged with NPOV or similar quality tags. Furthermore, these represent only the portion of articles for which such quality issues are explicitly flagged by the Wikipedia editors, however, the real number may be higher considering that only a small percentage of articles are of good quality or featured as categorized by Wikipedia. In this work, authors focus on the case of language bias at the sentence level in Wikipedia. Language bias is a hard problem, as it represents a subjective task and usually the linguistic cues are subtle and can be determined only through its context. Authors propose a supervised classification approach, which relies on an automatically created lexicon of bias words, and other syntactical and semantic characteristics of biased statements. Authors experimentally evaluate approach on a dataset consisting of biased and unbiased statements, and show that authors are able to detect biased statements with an accuracy of 74%. Furthermore, authors show that competitors that determine bias words are not suitable for detecting biased statements, which authors outperform with a relative improvement of over 20%.</div>Naomihttps://wikipediaquality.com/index.php?title=Libguides._What_About_Wikipedia%3F._About&diff=25201Libguides. What About Wikipedia?. About2020-08-18T06:35:57Z<p>Naomi: Adding embed</p>
<hr />
<div>{{Infobox work<br />
| title = Libguides. What About Wikipedia?. About<br />
| date = 2011<br />
| authors = [[Maria de Jesus Ayala-Schueneman]]<br />
| link = http://libguides.tamuk.edu/content.php?pid=164882&amp;sid=1390914<br />
}}<br />
'''Libguides. What About Wikipedia?. About''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Maria de Jesus Ayala-Schueneman]].<br />
<br />
== 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 />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Ayala-Schueneman, Maria de Jesus. (2011). "[[Libguides. What About Wikipedia?. About]]". Texas A&M University-Kingsville Library. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Ayala-Schueneman |first1=Maria de Jesus |title=Libguides. What About Wikipedia?. About |date=2011 |url=https://wikipediaquality.com/wiki/Libguides._What_About_Wikipedia?._About |journal=Texas A&M University-Kingsville Library}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Ayala-Schueneman, Maria de Jesus. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Libguides._What_About_Wikipedia?._About">Libguides. What About Wikipedia?. About</a>&amp;quot;. Texas A&M University-Kingsville Library. <br />
</nowiki><br />
</code></div>Naomihttps://wikipediaquality.com/index.php?title=A_Wikipedia_Literature_Review&diff=25200A Wikipedia Literature Review2020-08-18T06:33:22Z<p>Naomi: + Infobox work</p>
<hr />
<div>{{Infobox work<br />
| title = A Wikipedia Literature Review<br />
| date = 2011<br />
| authors = [[Owen S. Martin]]<br />
| link = https://lawrepository.ualr.edu/cgi/viewcontent.cgi?article=1270&amp;context=appellatepracticeprocess<br />
| plink = https://arxiv.org/abs/1110.5863<br />
}}<br />
'''A Wikipedia Literature Review''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Owen S. Martin]].<br />
<br />
== Overview ==<br />
This paper was originally designed as a literature review for a doctoral dissertation focusing on [[Wikipedia]]. This exposition gives the structure of Wikipedia and the latest trends in Wikipedia research.</div>Naomihttps://wikipediaquality.com/index.php?title=Web_Video_Categorization_based_on_Wikipedia_Categories_and_Content-Duplicated_Open_Resources&diff=25199Web Video Categorization based on Wikipedia Categories and Content-Duplicated Open Resources2020-08-18T06:31:32Z<p>Naomi: + infobox</p>
<hr />
<div>{{Infobox work<br />
| title = Web Video Categorization based on Wikipedia Categories and Content-Duplicated Open Resources<br />
| date = 2010<br />
| authors = [[Zhineng Chen]]<br />[[Juan Cao]]<br />[[Yicheng Song]]<br />[[Yongdong Zhang]]<br />[[Jintao Li]]<br />
| doi = 10.1145/1873951.1874162<br />
| link = http://dl.acm.org/citation.cfm?id=1873951.1874162<br />
| plink = https://www.researchgate.net/profile/Zhineng_Chen/publication/45933096_Web_Video_Categorization_based_on_Wikipedia_Categories_and_Content-Duplicated_Open_Resources/links/0912f5094e4ef1d657000000.pdf<br />
}}<br />
'''Web Video Categorization based on Wikipedia Categories and Content-Duplicated Open Resources''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Zhineng Chen]], [[Juan Cao]], [[Yicheng Song]], [[Yongdong Zhang]] and [[Jintao Li]].<br />
<br />
== Overview ==<br />
This paper presents a novel approach for web video categorization by leveraging [[Wikipedia categories]] (WikiCs) and open resources describing the same content as the video, i.e., content-duplicated open resources (CDORs). Note that current approaches only collect CDORs within one or a few media forms and ignore CDORs of other forms. Authors explore all these resources by utilizing WikiCs and commercial search engines. Given a web video, its discriminative [[Wikipedia]] concepts are first identified and classified. Then a textual query is constructed and from which CDORs are collected. Based on these CDORs, authors propose to categorize web videos in the space spanned by WikiCs rather than that spanned by raw tags. Experimental results demonstrate the effectiveness of both the proposed CDOR collection method and the WikiC voting categorization algorithm. In addition, the categorization model built based on both WikiCs and CDORs achieves better performance compared with the models built based on only one of them as well as state-of-the-art approach.</div>Naomihttps://wikipediaquality.com/index.php?title=A_Textual_Approach_based_on_Passages_Using_Ir-N_in_Wikipediamm_Task_2008&diff=25198A Textual Approach based on Passages Using Ir-N in Wikipediamm Task 20082020-08-18T06:29:27Z<p>Naomi: Categories</p>
<hr />
<div>{{Infobox work<br />
| title = A Textual Approach based on Passages Using Ir-N in Wikipediamm Task 2008<br />
| date = 2008<br />
| authors = [[Sergio Navarro]]<br />[[Rafael Muñoz]]<br />[[Fernando Llopis]]<br />
| link = http://ceur-ws.org/Vol-1174/CLEF2008wn-ImageCLEF-NavarroEt2008e.pdf<br />
| plink = https://www.researchgate.net/profile/Fernando_Llopis/publication/228968320_A_Textual_Approach_Based_on_Passages_Using_IR-n_in_WikipediaMM_Task_2008/links/0c960520d1babecc7e000000.pdf<br />
}}<br />
'''A Textual Approach based on Passages Using Ir-N in Wikipediamm Task 2008''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Sergio Navarro]], [[Rafael Muñoz]] and [[Fernando Llopis]].<br />
<br />
== Overview ==<br />
In this paper authors have focused efforts on comparing the behaviour of two relevance feedback methods in this task - LCA and PRF - and in checking if passage based information rerieval (IR) system is useful in a competition with small sized documents. Furthermore authors have added an adaptation to this domain based on decompound in single terms those file names which use a Camel Case notation. Authors base decision on the belief that the most meaningful information of an image file appointed by a human is on the file name itself. Thus, it is important to make visible this terms when they are hidden in a compounded file name. Finally authors have added a geographical query expansion and a visual concept expansion. Authors have obtained a 29th place within a total of 77 runs with baseline run - which only used the passage IR system -, and a 3rd place obtained with best run - which used the passage IR system with Camel Case decompounding -. It shows us on one hand the usefulness of passage based IR system in this domain, and on the other hand it confirms belief in the existence of specially meaningful information within the file names. In the the relevance feedback respect, authors have obtained contradictory results about the suitability of LCA or PRF to the task, but authors have found that LCA has a more robust behavior than PRF.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Navarro, Sergio; Muñoz, Rafael; Llopis, Fernando. (2008). "[[A Textual Approach based on Passages Using Ir-N in Wikipediamm Task 2008]]".<br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Navarro |first1=Sergio |last2=Muñoz |first2=Rafael |last3=Llopis |first3=Fernando |title=A Textual Approach based on Passages Using Ir-N in Wikipediamm Task 2008 |date=2008 |url=https://wikipediaquality.com/wiki/A_Textual_Approach_based_on_Passages_Using_Ir-N_in_Wikipediamm_Task_2008}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Navarro, Sergio; Muñoz, Rafael; Llopis, Fernando. (2008). &amp;quot;<a href="https://wikipediaquality.com/wiki/A_Textual_Approach_based_on_Passages_Using_Ir-N_in_Wikipediamm_Task_2008">A Textual Approach based on Passages Using Ir-N in Wikipediamm Task 2008</a>&amp;quot;.<br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Naomihttps://wikipediaquality.com/index.php?title=Automatic_Selection_of_Reference_Pages_in_Wikipedia_for_Improving_Targeted_Entities_Disambiguation&diff=25197Automatic Selection of Reference Pages in Wikipedia for Improving Targeted Entities Disambiguation2020-08-18T06:26:48Z<p>Naomi: Adding infobox</p>
<hr />
<div>{{Infobox work<br />
| title = Automatic Selection of Reference Pages in Wikipedia for Improving Targeted Entities Disambiguation<br />
| date = 2014<br />
| authors = [[Takuya Makino]]<br />
| doi = 10.3115/v1/E14-4021<br />
| link = http://aclweb.org/anthology/E/E14/E14-4021.pdf<br />
}}<br />
'''Automatic Selection of Reference Pages in Wikipedia for Improving Targeted Entities Disambiguation''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Takuya Makino]].<br />
<br />
== Overview ==<br />
In Targeted Entity Disambiguation setting, authors take (i) a set of entity names which belong to the same domain (target entities), (ii) candidate mentions of the given entities which are texts that contain the target entities as input, and then determine which ones are true mentions of “target entity”. For example, given the names of IT companies, including Apple, authors determine Apple in a mention denotes an IT company or not. Prior work proposed a graph based model. This model ranks all candidate mentions based on scores which denote the degree of relevancy to target entities. Furthermore, this graph based model could utilize reference pages of target entities. However, human annotators must select reference pages in advance. Authors propose an automatic method that can select reference pages. Authors formalize the selection problem of reference pages as an Integer Linear Programming problem. Authors show that model works as well as the prior work that manually selected reference pages.</div>Naomihttps://wikipediaquality.com/index.php?title=Use_and_Perception_of_Wikipedia_Among_Medical_Students_in_a_Nigerian_University&diff=25196Use and Perception of Wikipedia Among Medical Students in a Nigerian University2020-08-18T06:24:54Z<p>Naomi: cats.</p>
<hr />
<div>{{Infobox work<br />
| title = Use and Perception of Wikipedia Among Medical Students in a Nigerian University<br />
| date = 2014<br />
| authors = [[Esharenana E. Adomi]]<br />[[Samuel Emeka Adigwe]]<br />
| doi = 10.4018/ijdldc.2014040101<br />
| link = http://dl.acm.org/citation.cfm?id=2687139<br />
}}<br />
'''Use and Perception of Wikipedia Among Medical Students in a Nigerian University''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Esharenana E. Adomi]] and [[Samuel Emeka Adigwe]].<br />
<br />
== Overview ==<br />
This study explored the use and perception of [[Wikipedia]] among medical students in a Nigerian university. Descriptive survey design was adopted using questionnaire as instrument to collect data from 60 medical students who were in their fourth year at Delta State University, Abraka, Nigeria. Data obtained were analysed with frequency counts and percentages. The study revealed that 91.7% of the medical students have used Wikipedia; 76.4% of them could not indicate precisely the number of times they have used it; 50.9% of the students use Wikipedia to complement lecture notes, 43.6% for research project as well as to complete class assignment, 14% of them use it to modify content of articles; a majority have good knowledge of the structure and content of the site; the challenges faced by the students are scantiness of information of some articles, unavailability of/inability to obtain articles on some topics from the site, and inaccuracy/un[[reliability]] of content of articles.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Adomi, Esharenana E.; Adigwe, Samuel Emeka. (2014). "[[Use and Perception of Wikipedia Among Medical Students in a Nigerian University]]". IGI Global. DOI: 10.4018/ijdldc.2014040101. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Adomi |first1=Esharenana E. |last2=Adigwe |first2=Samuel Emeka |title=Use and Perception of Wikipedia Among Medical Students in a Nigerian University |date=2014 |doi=10.4018/ijdldc.2014040101 |url=https://wikipediaquality.com/wiki/Use_and_Perception_of_Wikipedia_Among_Medical_Students_in_a_Nigerian_University |journal=IGI Global}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Adomi, Esharenana E.; Adigwe, Samuel Emeka. (2014). &amp;quot;<a href="https://wikipediaquality.com/wiki/Use_and_Perception_of_Wikipedia_Among_Medical_Students_in_a_Nigerian_University">Use and Perception of Wikipedia Among Medical Students in a Nigerian University</a>&amp;quot;. IGI Global. DOI: 10.4018/ijdldc.2014040101. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Naomihttps://wikipediaquality.com/index.php?title=A_Semantic_Approach_for_Question_Classification_Using_Wordnet_and_Wikipedia&diff=25195A Semantic Approach for Question Classification Using Wordnet and Wikipedia2020-08-18T06:22:43Z<p>Naomi: Adding embed</p>
<hr />
<div>{{Infobox work<br />
| title = A Semantic Approach for Question Classification Using Wordnet and Wikipedia<br />
| date = 2010<br />
| authors = [[Santosh Kumar Ray]]<br />[[Shailendra Singh]]<br />[[Bhagwati P. Joshi]]<br />
| doi = 10.1016/j.patrec.2010.06.012<br />
| link = http://dl.acm.org/citation.cfm?id=1851110<br />
}}<br />
'''A Semantic Approach for Question Classification Using Wordnet and Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Santosh Kumar Ray]], [[Shailendra Singh]] and [[Bhagwati P. Joshi]].<br />
<br />
== Overview ==<br />
Question Answering Systems, unlike search engines, are providing answers to the users' questions in succinct form which requires the prior knowledge of the expectation of the user. Question classification module of a Question Answering System plays a very important role in determining the expectations of the user. In the literature, incorrect question classification has been cited as one of the major factors for the poor performance of the Question Answering Systems and this emphasizes on the importance of question classification module designing. In this article, authors have proposed a question classification method that exploits the powerful semantic [[features]] of the [[WordNet]] and the vast knowledge repository of the [[Wikipedia]] to describe informative terms explicitly. Authors have trained system over a standard set of 5500 questions (by UIUC) and then tested it over five TREC question collections. Authors have compared results with some standard results reported in the literature and observed a significant improvement in the accuracy of question classification. The question classification accuracy suggests the effectiveness of the method which is promising in the field of open-domain question classification. Judging the correctness of the answer is an important issue in the field of [[question answering]]. In this article, authors are extending question classification as one of the heuristics for answer validation. Authors are proposing a World Wide Web based solution for answer validation where answers returned by open-domain Question Answering Systems can be validated using online resources such as Wikipedia and [[Google]]. Authors have applied several heuristics for answer validation task and tested them against some popular web based open-domain Question Answering Systems over a collection of 500 questions collected from standard sources such as TREC, the Worldbook, and the Worldfactbook. The proposed method seems to be promising for automatic answer validation task.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Ray, Santosh Kumar; Singh, Shailendra; Joshi, Bhagwati P.. (2010). "[[A Semantic Approach for Question Classification Using Wordnet and Wikipedia]]". Elsevier Science Inc.. DOI: 10.1016/j.patrec.2010.06.012. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Ray |first1=Santosh Kumar |last2=Singh |first2=Shailendra |last3=Joshi |first3=Bhagwati P. |title=A Semantic Approach for Question Classification Using Wordnet and Wikipedia |date=2010 |doi=10.1016/j.patrec.2010.06.012 |url=https://wikipediaquality.com/wiki/A_Semantic_Approach_for_Question_Classification_Using_Wordnet_and_Wikipedia |journal=Elsevier Science Inc.}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Ray, Santosh Kumar; Singh, Shailendra; Joshi, Bhagwati P.. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/A_Semantic_Approach_for_Question_Classification_Using_Wordnet_and_Wikipedia">A Semantic Approach for Question Classification Using Wordnet and Wikipedia</a>&amp;quot;. Elsevier Science Inc.. DOI: 10.1016/j.patrec.2010.06.012. <br />
</nowiki><br />
</code></div>Naomihttps://wikipediaquality.com/index.php?title=Automatic_Mapping_of_User_Tags_to_Wikipedia_Concepts&diff=25194Automatic Mapping of User Tags to Wikipedia Concepts2020-08-18T06:21:06Z<p>Naomi: + cat.</p>
<hr />
<div>{{Infobox work<br />
| title = Automatic Mapping of User Tags to Wikipedia Concepts<br />
| date = 2015<br />
| authors = [[Arash Joorabchi]]<br />[[Michael English]]<br />[[Abdulhussain E. Mahdi]]<br />
| doi = 10.1177/0165551515586669<br />
| link = https://dl.acm.org/citation.cfm?id=2879234<br />
}}<br />
'''Automatic Mapping of User Tags to Wikipedia Concepts''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Arash Joorabchi]], [[Michael English]] and [[Abdulhussain E. Mahdi]].<br />
<br />
== Overview ==<br />
The uncontrolled nature of user-assigned tags makes them prone to various inconsistencies caused by spelling variations, synonyms, acronyms and hyponyms. These inconsistencies in turn lead to some of the common problems associated with the use of folksonomies such as the tag explosion phenomenon. Mapping user tags to their corresponding [[Wikipedia]] articles, as well-formed concepts, offers multifaceted benefits to the process of subject metadata generation and management in a wide range of online environments. These include normalization of inconsistencies, elimination of personal tags and improvement of the interchangeability of existing subject metadata. In this article, authors propose a machine learning-based method capable of automatic mapping of user tags to their equivalent Wikipedia concepts. Authors have demonstrated the application of the proposed method and evaluated its performance using the currently most popular computer programming Q&A website, StackOverflow.com, as test platform. Currently, around 20 million posts in StackOverflow are annotated with about 37,000 unique user tags, from which authors have chosen a subset of 1256 tags to evaluate the accuracy performance of proposed mapping method. Authors have evaluated the performance of method using the standard [[information retrieval]] [[measures]] of precision, recall and F1. Depending on the machine learning-based classification algorithm used as part of the mapping process, F1 scores as high as 99.6% were achieved.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Joorabchi, Arash; English, Michael; Mahdi, Abdulhussain E.. (2015). "[[Automatic Mapping of User Tags to Wikipedia Concepts]]". SAGE Publications. DOI: 10.1177/0165551515586669. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Joorabchi |first1=Arash |last2=English |first2=Michael |last3=Mahdi |first3=Abdulhussain E. |title=Automatic Mapping of User Tags to Wikipedia Concepts |date=2015 |doi=10.1177/0165551515586669 |url=https://wikipediaquality.com/wiki/Automatic_Mapping_of_User_Tags_to_Wikipedia_Concepts |journal=SAGE Publications}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Joorabchi, Arash; English, Michael; Mahdi, Abdulhussain E.. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Automatic_Mapping_of_User_Tags_to_Wikipedia_Concepts">Automatic Mapping of User Tags to Wikipedia Concepts</a>&amp;quot;. SAGE Publications. DOI: 10.1177/0165551515586669. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Naomihttps://wikipediaquality.com/index.php?title=Assessing_Quality_Score_of_Wikipedia_Article_Using_Mutual_Evaluation_of_Editors_and_Texts&diff=25193Assessing Quality Score of Wikipedia Article Using Mutual Evaluation of Editors and Texts2020-08-18T06:18:40Z<p>Naomi: Basic information on Assessing Quality Score of Wikipedia Article Using Mutual Evaluation of Editors and Texts</p>
<hr />
<div>'''Assessing Quality Score of Wikipedia Article Using Mutual Evaluation of Editors and Texts''' - scientific work related to Wikipedia quality published in 2013, written by Yu Suzuki and Masatoshi Yoshikawa.<br />
<br />
== Overview ==<br />
In this paper, authors propose a method for assessing quality scores of Wikipedia articles by mutually evaluating editors and texts. Survival ratio based approach is a major approach to assessing article quality. In this approach, when a text survives beyond multiple edits, the text is assessed as good quality, because poor quality texts have a high probability of being deleted by editors. However, many vandals, low quality editors, delete good quality texts frequently, which improperly decreases the survival ratios of good quality texts. As a result, many good quality texts are unfairly assessed as poor quality. In method, authors consider editor quality score for calculating text quality score, and decrease the impact on text quality by vandals. Using this improvement, the accuracy of the text quality score should be improved. However, an inherent problem with this idea is that the editor quality scores are calculated by the text quality scores. To solve this problem, authors mutually calculate the editor and text quality scores until they converge. In this paper, authors prove that the text quality score converges. Authors did experimental evaluation, and confirmed that proposed method could accurately assess the text quality scores.</div>Naomihttps://wikipediaquality.com/index.php?title=Research_on_Building_Chinese_Ontology_from_Wikipedia_Category_System&diff=25192Research on Building Chinese Ontology from Wikipedia Category System2020-08-18T06:17:23Z<p>Naomi: Adding embed</p>
<hr />
<div>{{Infobox work<br />
| title = Research on Building Chinese Ontology from Wikipedia Category System<br />
| date = 2008<br />
| authors = [[Song Qianqian]]<br />
| link = http://en.cnki.com.cn/Article_en/CJFDTOTAL-XTIB200802011.htm<br />
}}<br />
'''Research on Building Chinese Ontology from Wikipedia Category System''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Song Qianqian]].<br />
<br />
== Overview ==<br />
Authors take the category system in [[Wikipedia]] as a conceptual network.Authors label the semantic relations between [[categories]] using methods based on connectivity in the network and lexico-syntactic matching.As a result authors are able to derive a Chinese [[ontology]] containing a large amount of subsumption,i,e.is-a,relations.Finally,an experimental was carried out which compared the human subject extraction results to system result,and the recall and the precision showed that model do a good job.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Qianqian, Song. (2008). "[[Research on Building Chinese Ontology from Wikipedia Category System]]".<br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Qianqian |first1=Song |title=Research on Building Chinese Ontology from Wikipedia Category System |date=2008 |url=https://wikipediaquality.com/wiki/Research_on_Building_Chinese_Ontology_from_Wikipedia_Category_System}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Qianqian, Song. (2008). &amp;quot;<a href="https://wikipediaquality.com/wiki/Research_on_Building_Chinese_Ontology_from_Wikipedia_Category_System">Research on Building Chinese Ontology from Wikipedia Category System</a>&amp;quot;.<br />
</nowiki><br />
</code></div>Naomihttps://wikipediaquality.com/index.php?title=What_I_Know_Is...:_Establishing_Credibility_on_Wikipedia_Talk_Pages&diff=25191What I Know Is...: Establishing Credibility on Wikipedia Talk Pages2020-08-18T06:15:05Z<p>Naomi: Infobox work</p>
<hr />
<div>{{Infobox work<br />
| title = What I Know Is...: Establishing Credibility on Wikipedia Talk Pages<br />
| date = 2010<br />
| authors = [[Meghan Oxley]]<br />[[Jonathan T. Morgan]]<br />[[Mark Zachry]]<br />[[Brian Hutchinson]]<br />
| doi = 10.1145/1832772.1832805<br />
| link = http://dl.acm.org/citation.cfm?id=1832772.1832805<br />
}}<br />
'''What I Know Is...: Establishing Credibility on Wikipedia Talk Pages''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Meghan Oxley]], [[Jonathan T. Morgan]], [[Mark Zachry]] and [[Brian Hutchinson]].<br />
<br />
== Overview ==<br />
This poster presents a new theoretical framework and research method for studying the relationship between specific types of authority claims and the attempts of contributors to establish [[credibility]] in online, collaborative environments. Authors describe a content analysis method for coding authority claims based on linguistic and rhetorical cues in naturally occurring, text-based discourse. Authors present results from a preliminary analysis of a sample of [[Wikipedia]] talk page discussions focused on recent news events. This method provides a novel framework for capturing and understanding these persuasion-oriented behaviors, and shows potential as a tool for online communication research, including automated text analysis using trained [[natural language processing]] systems.</div>Naomihttps://wikipediaquality.com/index.php?title=Wikipedian:_a_Social_Identity_Between_Work_and_Contribution&diff=25190Wikipedian: a Social Identity Between Work and Contribution2020-08-18T06:13:38Z<p>Naomi: Embed for English Wikipedia, HTML</p>
<hr />
<div>{{Infobox work<br />
| title = Wikipedian: a Social Identity Between Work and Contribution<br />
| date = 2018<br />
| authors = [[Léo Joubert]]<br />
| doi = 10.1145/3233391.3233969<br />
| link = http://dl.acm.org/ft_gateway.cfm?id=3233969&amp;ftid=1990379&amp;dwn=1<br />
}}<br />
'''Wikipedian: a Social Identity Between Work and Contribution''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Léo Joubert]].<br />
<br />
== Overview ==<br />
Contributors to the [[Wikipedia]] "free encyclopedia" identify themselves and are identified as "[[Wikipedians]]". A Wikipedian does not leave his job when he becomes a Wikipedian. Nor does he become a Wikipedian in his workplace. The worker's identity and the Wikipedian identity coexist in the social identity of an individual. On which patterns does this coexistence between worker's identity and Wikipedian identity operate? Beyond the differences specific to the social identity of each contributor, authors will try to show that singulars transactions all take place according to a finite number of patterns that it is possible to count. At this stage of analysis, authors are able to distinguish five identity patterns: employment, learning center, alternative development, continuity in upset, parallel arena. Authors model aims to better understanding of why a contributor stay in Wikipedia and identifies himself as a contributor.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Joubert, Léo. (2018). "[[Wikipedian: a Social Identity Between Work and Contribution]]". ACM Press. DOI: 10.1145/3233391.3233969. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Joubert |first1=Léo |title=Wikipedian: a Social Identity Between Work and Contribution |date=2018 |doi=10.1145/3233391.3233969 |url=https://wikipediaquality.com/wiki/Wikipedian:_a_Social_Identity_Between_Work_and_Contribution |journal=ACM Press}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Joubert, Léo. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/Wikipedian:_a_Social_Identity_Between_Work_and_Contribution">Wikipedian: a Social Identity Between Work and Contribution</a>&amp;quot;. ACM Press. DOI: 10.1145/3233391.3233969. <br />
</nowiki><br />
</code></div>Naomihttps://wikipediaquality.com/index.php?title=More_Than_2_Billion_Pairs_of_Eyeballs:_Why_Aren%E2%80%99T_You_Sharing_Medical_Knowledge_on_Wikipedia%3F&diff=25189More Than 2 Billion Pairs of Eyeballs: Why Aren’T You Sharing Medical Knowledge on Wikipedia?2020-08-18T06:11:48Z<p>Naomi: Categories</p>
<hr />
<div>{{Infobox work<br />
| title = More Than 2 Billion Pairs of Eyeballs: Why Aren’T You Sharing Medical Knowledge on Wikipedia?<br />
| date = 2018<br />
| authors = [[Heather Murray]]<br />
| doi = 10.1136/bmjebm-2018-111040<br />
| link = https://ebm.bmj.com/content/early/2018/08/14/bmjebm-2018-111040<br />
}}<br />
'''More Than 2 Billion Pairs of Eyeballs: Why Aren’T You Sharing Medical Knowledge on Wikipedia?''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Heather Murray]].<br />
<br />
== Overview ==<br />
Wikipedia is the largest knowledge dissemination platform in the world. The English-language medical pages registered more than 2.4 billion visits in 2017, eclipsing websites like those of WHO, the NHS and WebMD.1 The lay language focus of the site obviously attracts patients, but surveys show that medical trainees at all levels report regular use.2 3 Health professionals also regularly visit [[Wikipedia]], once referred to as a ‘guilty secret’ of doctors and academics.4 The first step in knowledge translation is to put information where the people who want it can access it. Your patients are reading Wikipedia and your students are studying with Wikipedia. You have used it too, although you might not admit it in a crowd. And yet health researchers and policy-makers aren’t sharing their knowledge there. Instead, many reinvent the wheel: showcasing fancy, expensive new websites running parallel to the world’s most frequently used medical information resource.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Murray, Heather. (2018). "[[More Than 2 Billion Pairs of Eyeballs: Why Aren’T You Sharing Medical Knowledge on Wikipedia?]]". Royal Society of Medicine. DOI: 10.1136/bmjebm-2018-111040. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Murray |first1=Heather |title=More Than 2 Billion Pairs of Eyeballs: Why Aren’T You Sharing Medical Knowledge on Wikipedia? |date=2018 |doi=10.1136/bmjebm-2018-111040 |url=https://wikipediaquality.com/wiki/More_Than_2_Billion_Pairs_of_Eyeballs:_Why_Aren’T_You_Sharing_Medical_Knowledge_on_Wikipedia? |journal=Royal Society of Medicine}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Murray, Heather. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/More_Than_2_Billion_Pairs_of_Eyeballs:_Why_Aren’T_You_Sharing_Medical_Knowledge_on_Wikipedia?">More Than 2 Billion Pairs of Eyeballs: Why Aren’T You Sharing Medical Knowledge on Wikipedia?</a>&amp;quot;. Royal Society of Medicine. DOI: 10.1136/bmjebm-2018-111040. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]<br />
[[Category:English Wikipedia]]</div>Naomihttps://wikipediaquality.com/index.php?title=The_Evolution_of_Knowledge_Creation_Online:_Wikipedia_and_Knowledge_Processes&diff=25188The Evolution of Knowledge Creation Online: Wikipedia and Knowledge Processes2020-08-18T06:09:04Z<p>Naomi: cats.</p>
<hr />
<div>{{Infobox work<br />
| title = The Evolution of Knowledge Creation Online: Wikipedia and Knowledge Processes<br />
| date = 2015<br />
| authors = [[Ruqin Ren]]<br />
| doi = 10.1145/2788993.2791320<br />
| link = https://dl.acm.org/citation.cfm?id=2791320<br />
}}<br />
'''The Evolution of Knowledge Creation Online: Wikipedia and Knowledge Processes''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Ruqin Ren]].<br />
<br />
== Overview ==<br />
Using the evolutionary theory framework of the variation, retention, selection process, this paper explains the self-organized knowledge production behaviors online, with [[Wikipedia]] as an example. Evolution is presented as a trial-and-error process that produces a progressive accumulation of knowledge. The underlying theoretical assumption is that even though online communities feature very different characteristics than traditional organizations, the basic processes of trial-and-error learning in evolutionary theory still apply to the new forms of organizations. Based on the theory of self-organization system and evolution theory, the processes of variation and selection are explained in depth with examples observed on Wikipedia. The study presents a nested hierarchy of vicarious selectors that plays an important role in online knowledge creation.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Ren, Ruqin. (2015). "[[The Evolution of Knowledge Creation Online: Wikipedia and Knowledge Processes]]".DOI: 10.1145/2788993.2791320. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Ren |first1=Ruqin |title=The Evolution of Knowledge Creation Online: Wikipedia and Knowledge Processes |date=2015 |doi=10.1145/2788993.2791320 |url=https://wikipediaquality.com/wiki/The_Evolution_of_Knowledge_Creation_Online:_Wikipedia_and_Knowledge_Processes}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Ren, Ruqin. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/The_Evolution_of_Knowledge_Creation_Online:_Wikipedia_and_Knowledge_Processes">The Evolution of Knowledge Creation Online: Wikipedia and Knowledge Processes</a>&amp;quot;.DOI: 10.1145/2788993.2791320. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Naomihttps://wikipediaquality.com/index.php?title=Wikipedia-Based_Extraction_of_Lightweight_Ontologies_for_Concept_Level_Annotation&diff=25187Wikipedia-Based Extraction of Lightweight Ontologies for Concept Level Annotation2020-08-18T06:06:06Z<p>Naomi: + wikilinks</p>
<hr />
<div>'''Wikipedia-Based Extraction of Lightweight Ontologies for Concept Level Annotation''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Elshaimaa Ali]] and [[Michael Lauruhn]].<br />
<br />
== Overview ==<br />
This poster describes a project under development. Authors propose a framework for automating the construction of lightweight ontologies for semantic annotations. Lightweight [[ontology]] is defined as the ontology that does not have to include all the components expressed with formal languages such as concept taxonomies, formal axioms, disjoint and exhaustive decomposition of concepts. (Giunchiglia and Zaihrayeu 2009). However, manual enhancement of the ontology through the addition of axioms, rules, disjoint sets, etc., is possible for future reasoning purposes. The purpose behind this research is to evaluate possible means for efficiently annotating domain-specific content using open ontology sources.</div>Naomihttps://wikipediaquality.com/index.php?title=Social_Construction_of_Knowledge_in_Wikipedia&diff=25186Social Construction of Knowledge in Wikipedia2020-08-18T06:04:43Z<p>Naomi: + category</p>
<hr />
<div>{{Infobox work<br />
| title = Social Construction of Knowledge in Wikipedia<br />
| date = 2015<br />
| authors = [[Noriko Hara]]<br />[[Jylisa Doney]]<br />
| doi = 10.5210/fm.v20i6.5869<br />
| link = http://firstmonday.org/ojs/index.php/fm/article/view/5869/4572<br />
}}<br />
'''Social Construction of Knowledge in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Noriko Hara]] and [[Jylisa Doney]].<br />
<br />
== Overview ==<br />
This paper investigates how knowledge is constructed collaboratively in a crowd-sourced environment. More specifically, the study presented in this paper empirically analyzes online discussions in regard to [[Wikipedia]] entries on the Fukushima Nuclear Power Plant Disaster that occurred in March 2011 in Japan. For this study, authors examined the encyclopedia articles in both the English and Japanese versions of Wikipedia. The findings indicate similarities and differences between the two [[language versions]]. The implications of the study for collaborative knowledge production are also discussed.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Hara, Noriko; Doney, Jylisa. (2015). "[[Social Construction of Knowledge in Wikipedia]]".DOI: 10.5210/fm.v20i6.5869. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Hara |first1=Noriko |last2=Doney |first2=Jylisa |title=Social Construction of Knowledge in Wikipedia |date=2015 |doi=10.5210/fm.v20i6.5869 |url=https://wikipediaquality.com/wiki/Social_Construction_of_Knowledge_in_Wikipedia}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Hara, Noriko; Doney, Jylisa. (2015). &amp;quot;<a href="https://wikipediaquality.com/wiki/Social_Construction_of_Knowledge_in_Wikipedia">Social Construction of Knowledge in Wikipedia</a>&amp;quot;.DOI: 10.5210/fm.v20i6.5869. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]<br />
[[Category:English Wikipedia]]<br />
[[Category:Japanese Wikipedia]]</div>Naomihttps://wikipediaquality.com/index.php?title=Smart_Cities_Depictions_in_Wikipedia_Articles:_Reflections_from_a_Text_Analysis_Approach&diff=25185Smart Cities Depictions in Wikipedia Articles: Reflections from a Text Analysis Approach2020-08-18T06:02:55Z<p>Naomi: + infobox</p>
<hr />
<div>{{Infobox work<br />
| title = Smart Cities Depictions in Wikipedia Articles: Reflections from a Text Analysis Approach<br />
| date = 2018<br />
| authors = [[Felippe Cronemberger]]<br />[[J. Ramon Gil-Garcia]]<br />[[Felipe Xavier Costa]]<br />[[Theresa A. Pardo]]<br />
| doi = 10.1145/3209415.3209508<br />
| link = <br />
}}<br />
'''Smart Cities Depictions in Wikipedia Articles: Reflections from a Text Analysis Approach''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Felippe Cronemberger]], [[J. Ramon Gil-Garcia]], [[Felipe Xavier Costa]] and [[Theresa A. Pardo]].<br />
<br />
== Overview ==<br />
Discussions about smart cities continue to attract much attention from researchers, practitioners and citizens at large. As the literature continues to grow the number of [[indicators]] and frameworks for "smartness" are becoming more robust and perspectives on what makes a city smart continue to evolve. This exploratory research analyzes text from 51 [[Wikipedia]] articles that describe cities considered "smart" according to three globally recognized and independent rankings of cities. By comparing findings on word frequencies and word associations found in Wikipedia articles with terminology found in academic literature on smart cities, this study intends to determine to what extent data about smart cities produced through Wikipedia's crowdsourcing approach relates to theoretical developments in the field. This inductive approach may open avenues to the application of automated text analysis methods in theorizing and empirical efforts with information produced about smart cities. This exploratory work may facilitate conceptual understanding of the properties and [[features]] of smart cities and may also open avenues to future applications of alternative conceptualization methods.</div>Naomihttps://wikipediaquality.com/index.php?title=Handling_Flammable_Materials:_Wikipedia_Biographies_of_Living_Persons_as_Contentious_Objects&diff=24965Handling Flammable Materials: Wikipedia Biographies of Living Persons as Contentious Objects2020-06-29T05:29:30Z<p>Naomi: Category</p>
<hr />
<div>{{Infobox work<br />
| title = Handling Flammable Materials: Wikipedia Biographies of Living Persons as Contentious Objects<br />
| date = 2011<br />
| authors = [[Elisabeth Joyce]]<br />[[Brian S. Butler]]<br />[[Jacqueline C. Pike]]<br />
| doi = 10.1145/1940761.1940765<br />
| link = https://dl.acm.org/citation.cfm?id=1940765<br />
}}<br />
'''Handling Flammable Materials: Wikipedia Biographies of Living Persons as Contentious Objects''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Elisabeth Joyce]], [[Brian S. Butler]] and [[Jacqueline C. Pike]].<br />
<br />
== Overview ==<br />
Common ground. Shared interests. Collective goals. Much has been said about the power of technology to bring people together around commonalities to form groups, teams, and communities. Yet, the same technologies can also be used to bring together individuals with fundamentally irreconcilable differences. In these cases, the question is not how to construct systems that build on commonality, but rather how to manage artifacts that by their very nature provide affordances for conflict. In this paper authors examine how Biographies of Living Persons (BLP) in [[Wikipedia]] exemplify contentious objects, both in terms of their [[features]] and their consequences. Authors draw from discussions of risk management and resilience to outline four approaches that groups can use to manage contentious objects (risk avoidance, risk minimization, threat reduction, and conflict management). Description of the policies, structures, and systems surrounding Biographies of Living Persons in Wikipedia illustrate how application of these approaches enable the creation and existence of large collection of contentions objects, without undermining the viability of the larger socio-technical system.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Joyce, Elisabeth; Butler, Brian S.; Pike, Jacqueline C.. (2011). "[[Handling Flammable Materials: Wikipedia Biographies of Living Persons as Contentious Objects]]".DOI: 10.1145/1940761.1940765. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Joyce |first1=Elisabeth |last2=Butler |first2=Brian S. |last3=Pike |first3=Jacqueline C. |title=Handling Flammable Materials: Wikipedia Biographies of Living Persons as Contentious Objects |date=2011 |doi=10.1145/1940761.1940765 |url=https://wikipediaquality.com/wiki/Handling_Flammable_Materials:_Wikipedia_Biographies_of_Living_Persons_as_Contentious_Objects}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Joyce, Elisabeth; Butler, Brian S.; Pike, Jacqueline C.. (2011). &amp;quot;<a href="https://wikipediaquality.com/wiki/Handling_Flammable_Materials:_Wikipedia_Biographies_of_Living_Persons_as_Contentious_Objects">Handling Flammable Materials: Wikipedia Biographies of Living Persons as Contentious Objects</a>&amp;quot;.DOI: 10.1145/1940761.1940765. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Naomihttps://wikipediaquality.com/index.php?title=Extracting_and_Mapping_Industry_4.0_Technologies_Using_Wikipedia&diff=24964Extracting and Mapping Industry 4.0 Technologies Using Wikipedia2020-06-29T05:26:46Z<p>Naomi: + categories</p>
<hr />
<div>{{Infobox work<br />
| title = Extracting and Mapping Industry 4.0 Technologies Using Wikipedia<br />
| date = 2018<br />
| authors = [[Filippo Chiarello]]<br />[[Leonello Trivelli]]<br />[[Andrea Bonaccorsi]]<br />[[Gualtiero Fantoni]]<br />
| doi = 10.1016/j.compind.2018.04.006<br />
| link = https://www.sciencedirect.com/science/article/pii/S0166361517306176<br />
}}<br />
'''Extracting and Mapping Industry 4.0 Technologies Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Filippo Chiarello]], [[Leonello Trivelli]], [[Andrea Bonaccorsi]] and [[Gualtiero Fantoni]].<br />
<br />
== Overview ==<br />
Abstract The explosion of the interest in the industry 4.0 generated a hype on both academia and business: the former is attracted for the opportunities given by the emergence of such a new field, the latter is pulled by incentives and national investment plans. The Industry 4.0 technological field is not new but it is highly heterogeneous (actually it is the aggregation point of more than 30 different fields of the technology). For this reason, many stakeholders feel uncomfortable since they do not master the whole set of technologies, they manifested a lack of knowledge and problems of communication with other domains. Actually such problem is twofold, on one side a common vocabulary that helps domain experts to have a mutual understanding is missing Riel et al. [1], on the other side, an overall standardization effort would be beneficial to integrate existing terminologies in a reference architecture for the Industry 4.0 paradigm Smit et al. [2]. One of the basics for solving this issue is the creation of shared semantic for industry 4.0. The paper has an intermediate goal and focuses on the development of an enriched dictionary of Industry 4.0 enabling technologies, with definitions and links between them in order to help the user in actively surfing the new domains by starting from known elements to reach the most far away from his/her background and knowledge.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Chiarello, Filippo; Trivelli, Leonello; Bonaccorsi, Andrea; Fantoni, Gualtiero. (2018). "[[Extracting and Mapping Industry 4.0 Technologies Using Wikipedia]]". Elsevier BV. DOI: 10.1016/j.compind.2018.04.006. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Chiarello |first1=Filippo |last2=Trivelli |first2=Leonello |last3=Bonaccorsi |first3=Andrea |last4=Fantoni |first4=Gualtiero |title=Extracting and Mapping Industry 4.0 Technologies Using Wikipedia |date=2018 |doi=10.1016/j.compind.2018.04.006 |url=https://wikipediaquality.com/wiki/Extracting_and_Mapping_Industry_4.0_Technologies_Using_Wikipedia |journal=Elsevier BV}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Chiarello, Filippo; Trivelli, Leonello; Bonaccorsi, Andrea; Fantoni, Gualtiero. (2018). &amp;quot;<a href="https://wikipediaquality.com/wiki/Extracting_and_Mapping_Industry_4.0_Technologies_Using_Wikipedia">Extracting and Mapping Industry 4.0 Technologies Using Wikipedia</a>&amp;quot;. Elsevier BV. DOI: 10.1016/j.compind.2018.04.006. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Naomihttps://wikipediaquality.com/index.php?title=Research_Guides:_the_%22All_Red_Event%22:_Eastern%27s_Red_Reese_Wikipedia_Edit-A-Thon:_Writing_and_Citing&diff=24963Research Guides: the "All Red Event": Eastern's Red Reese Wikipedia Edit-A-Thon: Writing and Citing2020-06-29T05:24:08Z<p>Naomi: Adding wikilinks</p>
<hr />
<div>'''Research Guides: the "All Red Event": Eastern's Red Reese Wikipedia Edit-A-Thon: Writing and Citing''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[James Rosenzweig]].<br />
<br />
== Overview ==<br />
A guide giving information about the Red Reese [[Wikipedia]] article creation event hosted at Eastern's JFK Library this February, and providing resources to be used at the event. Some quick advice on editing Wikipedia articles</div>Naomihttps://wikipediaquality.com/index.php?title=Collective_Memory_Building_in_Wikipedia:_the_Case_of_North_African_Uprisings&diff=24962Collective Memory Building in Wikipedia: the Case of North African Uprisings2020-06-29T05:22:47Z<p>Naomi: Information about: Collective Memory Building in Wikipedia: the Case of North African Uprisings</p>
<hr />
<div>'''Collective Memory Building in Wikipedia: the Case of North African Uprisings''' - scientific work related to Wikipedia quality published in 2011, written by Michela Ferron and Paolo Massa.<br />
<br />
== Overview ==<br />
Since December 2010, a series of protests and uprisings have shocked North African countries such as Tunisia, Egypt, Libya, Syria, Yemen and more. In this paper, focusing mainly on the Egyptian revolution, authors provide evidence of the intense edit activity occurred during these uprisings on the related Wikipedia pages. Thousands of people provided their contribution on the content pages and discussed improvements and disagreements on the associated talk pages as the traumatic events unfolded. Authors propose to interpret this phenomenon as a process of collective memory building and argue how on Wikipedia this can be studied empirically and quantitatively in real time. Authors explore and suggest possible directions for future research on collective memory formation of traumatic and controversial events in Wikipedia.</div>Naomihttps://wikipediaquality.com/index.php?title=Empirical_Analysis_of_User_Participation_in_Online_Communities:_the_Case_of_Wikipedia&diff=24961Empirical Analysis of User Participation in Online Communities: the Case of Wikipedia2020-06-29T05:20:47Z<p>Naomi: Embed</p>
<hr />
<div>{{Infobox work<br />
| title = Empirical Analysis of User Participation in Online Communities: the Case of Wikipedia<br />
| date = 2010<br />
| authors = [[Giovanni Luca Ciampaglia]]<br />
| link = <br />
}}<br />
'''Empirical Analysis of User Participation in Online Communities: the Case of Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Giovanni Luca Ciampaglia]].<br />
<br />
== Overview ==<br />
Authors study the distribution of the activity period of users in five of the largest localized versions of the free, on- line encyclopedia [[Wikipedia]]. Authors find it to be consis- tent with a mixture of two truncated log-normal distri- butions. Using this model, the temporal evolution of these systems can be analyzed, showing that the statis- tical description is consistent over time. contributions: 1. Authors find all datasets to be consistent with the hypothesis that the lifetime of an user account is described by the su- perposition of two truncated log-normal distributions. An interpretation for this phenomenon is that two different regimes govern the participation of individuals to these versions of the Wikipedia project: the occasional users, who fail to find interest in the project after the first few at- tempts to contribute, and the long-term users, whose with- drawal is probably more related to external factors like the loss of personal incentives in contributing and similar causes. 2. Using model, authors characterize how the participation of users over time evolves, as the system ages. Authors find that the statistical description of the one-timers is stable over time, while the properties of the group of long-term users change as a consequence of the aging of the system. 3. Authors find evidence that the inter-edit time distribution de- cays with an heavy tail. In view of this finding, authors check that analysis is not affected by the choice of the pa- rameter used for determining when an user is to be con- sidered "inactive"; authors find that for the one-timers it has no quantitative effect. For the statistics of the long-lived users authors find instead a very good qualitative agreement.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Ciampaglia, Giovanni Luca. (2010). "[[Empirical Analysis of User Participation in Online Communities: the Case of Wikipedia]]".<br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Ciampaglia |first1=Giovanni Luca |title=Empirical Analysis of User Participation in Online Communities: the Case of Wikipedia |date=2010 |url=https://wikipediaquality.com/wiki/Empirical_Analysis_of_User_Participation_in_Online_Communities:_the_Case_of_Wikipedia}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Ciampaglia, Giovanni Luca. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Empirical_Analysis_of_User_Participation_in_Online_Communities:_the_Case_of_Wikipedia">Empirical Analysis of User Participation in Online Communities: the Case of Wikipedia</a>&amp;quot;.<br />
</nowiki><br />
</code></div>Naomihttps://wikipediaquality.com/index.php?title=Enabling_Entity_Retrieval_by_Exploiting_Wikipedia_as_a_Semantic_Knowledge_Source&diff=24960Enabling Entity Retrieval by Exploiting Wikipedia as a Semantic Knowledge Source2020-06-29T05:19:10Z<p>Naomi: Infobox work</p>
<hr />
<div>{{Infobox work<br />
| title = Enabling Entity Retrieval by Exploiting Wikipedia as a Semantic Knowledge Source<br />
| date = 2011<br />
| authors = [[Xia Lin]]<br />[[Sofia Jeon]]<br />
| link = https://dl.acm.org/citation.cfm?id=2395451<br />
}}<br />
'''Enabling Entity Retrieval by Exploiting Wikipedia as a Semantic Knowledge Source''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Xia Lin]] and [[Sofia Jeon]].<br />
<br />
== Overview ==<br />
This dissertation research, PanAnthropon FilmWorld, aims to demonstrate direct retrieval of entities and related facts by exploiting [[Wikipedia]] as a [[semantic knowledge]] source, with the film domain as its proof-of-concept domain of application. To this end, a semantic knowledge base concerning the film domain has been constructed with the data extracted/derived from 10,640 Wikipedia pages on films and additional pages on film awards. The knowledge base currently contains 209,266 entities and 2,345,931 entity-centric facts. Both the knowledge base and the corresponding semantic search interface are based on the coherent classification of entities. Entity-centric facts are also consistently represented as tuples. The semantic search interface (http://dlib.ischool.drexel.edu:8080/sofia/PA/) supports multiple types of semantic search functions, which go beyond the traditional keyword-based search function, including the main General Entity Retrieval Query (GERQ) function, which is concerned with retrieving all entities that match the specified entity type, subtype, and semantic conditions and thus corresponds to the main research problem. Two types of evaluation have been performed in order to evaluate (1) the quality of [[information extraction]] and (2) the effectiveness of [[information retrieval]] using the semantic interface. The first type of evaluation has been performed by inspecting 11,495 film-centric facts concerning 100 films. The results have confirmed high [[data quality]] with 99.96% average precision and 99.84% average recall. The second type of evaluation has been performed by conducting an experiment with human subjects. The experiment involved having the subjects perform a retrieval task by using both the PanAnthropon interface and the Internet Movie Database (IMDb) interface and comparing their task performance between the two interfaces. The results have confirmed higher effectiveness of the PanAnthropon interface vs. the IMDb interface (83.11% vs. 40.78% average precision; 83.55% vs. 40.26% average recall). Moreover, the subjects' responses to the post-task questionnaire indicate that the subjects found the PanAnthropon interface to be highly usable and easily understandable as well as highly effective. The main contribution from this research therefore consists in achieving the set research goal, namely, demonstrating the utility and feasibility of semantics-based direct entity retrieval.</div>Naomihttps://wikipediaquality.com/index.php?title=Token_Level_Code-Switching_Detection_Using_Wikipedia_as_a_Lexical_Resource&diff=24959Token Level Code-Switching Detection Using Wikipedia as a Lexical Resource2020-06-29T05:16:50Z<p>Naomi: Embed for English Wikipedia, HTML</p>
<hr />
<div>{{Infobox work<br />
| title = Token Level Code-Switching Detection Using Wikipedia as a Lexical Resource<br />
| date = 2017<br />
| authors = [[Daniel Claeser]]<br />[[Dennis Felske]]<br />[[Samantha Kent]]<br />
| doi = 10.1007/978-3-319-73706-5_16<br />
| link = https://link.springer.com/content/pdf/10.1007%2F978-3-319-73706-5_16.pdf<br />
}}<br />
'''Token Level Code-Switching Detection Using Wikipedia as a Lexical Resource''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Daniel Claeser]], [[Dennis Felske]] and [[Samantha Kent]].<br />
<br />
== Overview ==<br />
Authors present a novel lexicon-based classification approach for code-switching detection on [[Twitter]]. The main aim is to develop a simple lexical look-up classifier based on frequency information retrieved from [[Wikipedia]]. Authors evaluate the classifier using three [[different language]] pairs: Spanish-English, Dutch-English, and German-Turkish. The results indicate that figures for Spanish-English are competitive with current state of the art classifiers, even though the approach is simplistic and based solely on word frequency information.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Claeser, Daniel; Felske, Dennis; Kent, Samantha. (2017). "[[Token Level Code-Switching Detection Using Wikipedia as a Lexical Resource]]". Springer, Cham. DOI: 10.1007/978-3-319-73706-5_16. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Claeser |first1=Daniel |last2=Felske |first2=Dennis |last3=Kent |first3=Samantha |title=Token Level Code-Switching Detection Using Wikipedia as a Lexical Resource |date=2017 |doi=10.1007/978-3-319-73706-5_16 |url=https://wikipediaquality.com/wiki/Token_Level_Code-Switching_Detection_Using_Wikipedia_as_a_Lexical_Resource |journal=Springer, Cham}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Claeser, Daniel; Felske, Dennis; Kent, Samantha. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Token_Level_Code-Switching_Detection_Using_Wikipedia_as_a_Lexical_Resource">Token Level Code-Switching Detection Using Wikipedia as a Lexical Resource</a>&amp;quot;. Springer, Cham. DOI: 10.1007/978-3-319-73706-5_16. <br />
</nowiki><br />
</code></div>Naomihttps://wikipediaquality.com/index.php?title=Centrality_and_Content_Creation_in_Networks_-_the_Case_of_Economic_Topics_on_German_Wikipedia&diff=24958Centrality and Content Creation in Networks - the Case of Economic Topics on German Wikipedia2020-06-29T05:15:31Z<p>Naomi: Embed for English Wikipedia, HTML</p>
<hr />
<div>{{Infobox work<br />
| title = Centrality and Content Creation in Networks - the Case of Economic Topics on German Wikipedia<br />
| date = 2016<br />
| authors = [[Michael E. Kummer]]<br />[[Marianne Saam]]<br />[[Iassen Halatchliyski]]<br />[[George Giorgidze]]<br />
| doi = 10.1016/j.infoecopol.2016.06.002<br />
| link = http://www.sciencedirect.com/science/article/pii/S0167624516300403<br />
}}<br />
'''Centrality and Content Creation in Networks - the Case of Economic Topics on German Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Michael E. Kummer]], [[Marianne Saam]], [[Iassen Halatchliyski]] and [[George Giorgidze]].<br />
<br />
== Overview ==<br />
Authors analyze the role of local and global network positions for content contributions to articles belonging to the category “Economy” on the German [[Wikipedia]]. Observing a sample of 7635 articles over a period of 153 weeks authors measure their centrality both within this category and in the network of over one million Wikipedia articles. Authors analysis reveals that an additional link from the observed category is associated with around 140 bytes of additional content and with an increase in the number of authors by 0.5. The relation of links from outside the category to content creation is much weaker. Beyond the econometric analysis, study sheds light on how the discipline of economics is represented on German Wikipedia. Authors find non-neoclassical themes to be highly prevalent among the top articles.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Kummer, Michael E.; Saam, Marianne; Halatchliyski, Iassen; Giorgidze, George. (2016). "[[Centrality and Content Creation in Networks - the Case of Economic Topics on German Wikipedia]]". Elsevier. DOI: 10.1016/j.infoecopol.2016.06.002. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Kummer |first1=Michael E. |last2=Saam |first2=Marianne |last3=Halatchliyski |first3=Iassen |last4=Giorgidze |first4=George |title=Centrality and Content Creation in Networks - the Case of Economic Topics on German Wikipedia |date=2016 |doi=10.1016/j.infoecopol.2016.06.002 |url=https://wikipediaquality.com/wiki/Centrality_and_Content_Creation_in_Networks_-_the_Case_of_Economic_Topics_on_German_Wikipedia |journal=Elsevier}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Kummer, Michael E.; Saam, Marianne; Halatchliyski, Iassen; Giorgidze, George. (2016). &amp;quot;<a href="https://wikipediaquality.com/wiki/Centrality_and_Content_Creation_in_Networks_-_the_Case_of_Economic_Topics_on_German_Wikipedia">Centrality and Content Creation in Networks - the Case of Economic Topics on German Wikipedia</a>&amp;quot;. Elsevier. DOI: 10.1016/j.infoecopol.2016.06.002. <br />
</nowiki><br />
</code></div>Naomihttps://wikipediaquality.com/index.php?title=Entity_Linking_with_People_Entity_on_Wikipedia&diff=24957Entity Linking with People Entity on Wikipedia2020-06-29T05:13:18Z<p>Naomi: + cat.</p>
<hr />
<div>{{Infobox work<br />
| title = Entity Linking with People Entity on Wikipedia<br />
| date = 2017<br />
| authors = [[Weiqian Yan]]<br />[[Kanchan Khurad]]<br />
| link = http://journals.uic.edu/ojs/index.php/ojphi/article/download/7630/6151<br />
| plink = http://arxiv.org/pdf/1705.01042.pdf<br />
}}<br />
'''Entity Linking with People Entity on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Weiqian Yan]] and [[Kanchan Khurad]].<br />
<br />
== Overview ==<br />
This paper introduces a new model that uses [[named entity recognition]], coreference resolution, and entity linking techniques, to approach the task of linking people entities on [[Wikipedia]] people pages to their corresponding Wikipedia pages if applicable. Authors task is different from general and traditional entity linking because authors are working in a limited domain, namely, people entities, and authors are including pronouns as entities, whereas in the past, pronouns were never considered as entities in entity linking. Authors have built 2 models, both outperforms baseline model significantly. The purpose of project is to build a model that could be use to generate cleaner data for future entity linking tasks. Authors contribution include a clean data set consisting of 50Wikipedia people pages, and 2 entity linking models, specifically tuned for this domain.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Yan, Weiqian; Khurad, Kanchan. (2017). "[[Entity Linking with People Entity on Wikipedia]]".<br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Yan |first1=Weiqian |last2=Khurad |first2=Kanchan |title=Entity Linking with People Entity on Wikipedia |date=2017 |url=https://wikipediaquality.com/wiki/Entity_Linking_with_People_Entity_on_Wikipedia}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Yan, Weiqian; Khurad, Kanchan. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Entity_Linking_with_People_Entity_on_Wikipedia">Entity Linking with People Entity on Wikipedia</a>&amp;quot;.<br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Naomihttps://wikipediaquality.com/index.php?title=Produktion_Von_Naturwissenschaftlichen_Informationen_Im_Internet_Am_Beispiel_Von_Wikipedia&diff=24956Produktion Von Naturwissenschaftlichen Informationen Im Internet Am Beispiel Von Wikipedia2020-06-29T05:10:47Z<p>Naomi: + Embed</p>
<hr />
<div>{{Infobox work<br />
| title = Produktion Von Naturwissenschaftlichen Informationen Im Internet Am Beispiel Von Wikipedia<br />
| date = 2017<br />
| authors = [[Steffen Nestler]]<br />[[Marius Leckelt]]<br />[[Mitja D. Back]]<br />[[Ina von der Beck]]<br />[[Ulrike Cress]]<br />[[Aileen Oeberst]]<br />
| doi = 10.1026/0033-3042/a000360<br />
| link = https://econtent.hogrefe.com/doi/abs/10.1026/0033-3042/a000360<br />
}}<br />
'''Produktion Von Naturwissenschaftlichen Informationen Im Internet Am Beispiel Von Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Steffen Nestler]], [[Marius Leckelt]], [[Mitja D. Back]], [[Ina von der Beck]], [[Ulrike Cress]] and [[Aileen Oeberst]].<br />
<br />
== Overview ==<br />
Zusammenfassung. Im Internet konnen Laien nicht nur naturwissenschaftliche Informationen passiv rezipieren, sondern diese auch aktiv produzieren. Wie verarbeiten sie dabei Informationen, sofern sie unsicher und widerspruchlich sind? Wahrend Forschungsarbeiten dazu vorliegen, wie Rezipienten mit fragilen und konflikthaften Informationen und Theorien umgehen, ist bisher noch wenig zu den Einflussfaktoren auf die Produktion von naturwissenschaftlichen Informationen durch Laien im Internet bekannt. In unserem Beitrag zeigen wir verschiedene Einflussfaktoren auf und leiten Vorhersagen zum Produktionsverhalten und den resultierenden Textprodukten ab. Schlieslich illustrieren wir unsere Uberlegungen an der Online-Enzyklopadie [[Wikipedia]].<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Nestler, Steffen; Leckelt, Marius; Back, Mitja D.; Beck, Ina von der; Cress, Ulrike; Oeberst, Aileen. (2017). "[[Produktion Von Naturwissenschaftlichen Informationen Im Internet Am Beispiel Von Wikipedia]]". Hogrefe Verlag. DOI: 10.1026/0033-3042/a000360. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Nestler |first1=Steffen |last2=Leckelt |first2=Marius |last3=Back |first3=Mitja D. |last4=Beck |first4=Ina von der |last5=Cress |first5=Ulrike |last6=Oeberst |first6=Aileen |title=Produktion Von Naturwissenschaftlichen Informationen Im Internet Am Beispiel Von Wikipedia |date=2017 |doi=10.1026/0033-3042/a000360 |url=https://wikipediaquality.com/wiki/Produktion_Von_Naturwissenschaftlichen_Informationen_Im_Internet_Am_Beispiel_Von_Wikipedia |journal=Hogrefe Verlag}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Nestler, Steffen; Leckelt, Marius; Back, Mitja D.; Beck, Ina von der; Cress, Ulrike; Oeberst, Aileen. (2017). &amp;quot;<a href="https://wikipediaquality.com/wiki/Produktion_Von_Naturwissenschaftlichen_Informationen_Im_Internet_Am_Beispiel_Von_Wikipedia">Produktion Von Naturwissenschaftlichen Informationen Im Internet Am Beispiel Von Wikipedia</a>&amp;quot;. Hogrefe Verlag. DOI: 10.1026/0033-3042/a000360. <br />
</nowiki><br />
</code></div>Naomihttps://wikipediaquality.com/index.php?title=Integrating_Web_2.0_Resources_by_Wikipedia&diff=24955Integrating Web 2.0 Resources by Wikipedia2020-06-29T05:08:43Z<p>Naomi: + category</p>
<hr />
<div>{{Infobox work<br />
| title = Integrating Web 2.0 Resources by Wikipedia<br />
| date = 2010<br />
| authors = [[Chen Liu]]<br />[[Bing Cui]]<br />[[Anthony K. H. Tung]]<br />
| doi = 10.1145/1873951.1874058<br />
| link = http://dl.acm.org/citation.cfm?id=1874058<br />
}}<br />
'''Integrating Web 2.0 Resources by Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Chen Liu]], [[Bing Cui]] and [[Anthony K. H. Tung]].<br />
<br />
== Overview ==<br />
The concept of Web 2.0 becomes prevalent and popular in the past few years. People are able to share and manage their own resources in Web 2.0 Systems. The abundance of Web 2.0 resources in various media formats calls for better resource integration, intending to enrich user experience in both browsing and searching. Though the Web 2.0 resources are shown in various modalities, their tags act as an intuitive medium to connect resources together. However, tagging is by nature an ad hoc activity. They do often contain noises and are affected by the subjective inclination of taggers. Consequently, linking resources simply by tags will not be reliable. In this paper, authors propose an effective approach for linking tagged resources to concepts extracted from [[Wikipedia]], which has become a fairly reliable reference over the last few years. Compared to the tags, the concepts are therefore of higher quality. Empirical experiments were conducted, and the results validate the effectiveness of framework.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Liu, Chen; Cui, Bing; Tung, Anthony K. H.. (2010). "[[Integrating Web 2.0 Resources by Wikipedia]]".DOI: 10.1145/1873951.1874058. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Liu |first1=Chen |last2=Cui |first2=Bing |last3=Tung |first3=Anthony K. H. |title=Integrating Web 2.0 Resources by Wikipedia |date=2010 |doi=10.1145/1873951.1874058 |url=https://wikipediaquality.com/wiki/Integrating_Web_2.0_Resources_by_Wikipedia}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Liu, Chen; Cui, Bing; Tung, Anthony K. H.. (2010). &amp;quot;<a href="https://wikipediaquality.com/wiki/Integrating_Web_2.0_Resources_by_Wikipedia">Integrating Web 2.0 Resources by Wikipedia</a>&amp;quot;.DOI: 10.1145/1873951.1874058. <br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Naomihttps://wikipediaquality.com/index.php?title=Defense_Mechanism_or_Socialization_Tactic%3F_Improving_Wikipedia%27s_Notifications_to_Rejected_Contributors&diff=24954Defense Mechanism or Socialization Tactic? Improving Wikipedia's Notifications to Rejected Contributors2020-06-29T05:06:01Z<p>Naomi: Category</p>
<hr />
<div>{{Infobox work<br />
| title = Defense Mechanism or Socialization Tactic? Improving Wikipedia's Notifications to Rejected Contributors<br />
| date = 2012<br />
| authors = [[R. Stuart Geiger]]<br />[[Aaron Halfaker]]<br />[[Maryana Pinchuk]]<br />[[Steven Walling]]<br />
| link = http://files.grouplens.org/papers/defense-mechanism-icwsm.pdf<br />
}}<br />
'''Defense Mechanism or Socialization Tactic? Improving Wikipedia's Notifications to Rejected Contributors''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[R. Stuart Geiger]], [[Aaron Halfaker]], [[Maryana Pinchuk]] and [[Steven Walling]].<br />
<br />
== Overview ==<br />
Unlike traditional firms, open collaborative systems rely on volunteers to operate, and many communities struggle to maintain enough contributors to ensure the quality and quantity of content. However, [[Wikipedia]] has historically faced the exact opposite problem: too much participation, particularly from users who, knowingly or not, do not share the same norms as veteran [[Wikipedians]]. During its period of exponential growth, the Wikipedian community developed specialized socio-technical defense mechanisms to protect itself from the negatives of massive participation: spam, vandalism, falsehoods, and other damage. Yet recently, Wikipedia has faced a number of high-profile issues with recruiting and retaining new contributors. In this paper, authors first illustrate and describe the various defense mechanisms at work in Wikipedia, which authors hypothesize are inhibiting newcomer retention. Next, authors present results from an experiment aimed at increasing both the quantity and quality of editors by altering various elements of these defense mechanisms, specifically pre-scripted warnings and notifications that are sent to new editors upon reverting or rejecting contributions. Using logistic regressions to model new user activity, authors show which tactics work best for different populations of users based on their motivations when joining Wikipedia. In particular, authors found that personalized messages in which Wikipedians identified themselves in active voice and took direct responsibility for rejecting an editor’s contributions were much more successful across a variety of outcome metrics than the current messages, which typically use an institutional and passive voice.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Geiger, R. Stuart; Halfaker, Aaron; Pinchuk, Maryana; Walling, Steven. (2012). "[[Defense Mechanism or Socialization Tactic? Improving Wikipedia's Notifications to Rejected Contributors]]".<br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Geiger |first1=R. Stuart |last2=Halfaker |first2=Aaron |last3=Pinchuk |first3=Maryana |last4=Walling |first4=Steven |title=Defense Mechanism or Socialization Tactic? Improving Wikipedia's Notifications to Rejected Contributors |date=2012 |url=https://wikipediaquality.com/wiki/Defense_Mechanism_or_Socialization_Tactic?_Improving_Wikipedia's_Notifications_to_Rejected_Contributors}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Geiger, R. Stuart; Halfaker, Aaron; Pinchuk, Maryana; Walling, Steven. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Defense_Mechanism_or_Socialization_Tactic?_Improving_Wikipedia's_Notifications_to_Rejected_Contributors">Defense Mechanism or Socialization Tactic? Improving Wikipedia's Notifications to Rejected Contributors</a>&amp;quot;.<br />
</nowiki><br />
</code><br />
<br />
<br />
<br />
[[Category:Scientific works]]</div>Naomihttps://wikipediaquality.com/index.php?title=A_Practical_Approach_to_Language_Complexity:_a_Wikipedia_Case_Study&diff=24953A Practical Approach to Language Complexity: a Wikipedia Case Study2020-06-29T05:03:10Z<p>Naomi: + embed code</p>
<hr />
<div>{{Infobox work<br />
| title = A Practical Approach to Language Complexity: a Wikipedia Case Study<br />
| date = 2012<br />
| authors = [[Taha Yasseri]]<br />[[András Kornai]]<br />[[János Kertész]]<br />[[János Kertész]]<br />
| doi = 10.1371/journal.pone.0048386<br />
| link = https://dl.acm.org/citation.cfm?id=2908532<br />
| plink = http://arxiv.org/pdf/1204.2765.pdf<br />
}}<br />
'''A Practical Approach to Language Complexity: a Wikipedia Case Study''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Taha Yasseri]], [[András Kornai]], [[János Kertész]] and [[János Kertész]].<br />
<br />
== Overview ==<br />
In this paper authors present statistical analysis of English texts from [[Wikipedia]]. Authors try to address the issue of language complexity empirically by comparing the simple [[English Wikipedia]] (Simple) to comparable samples of the main English Wikipedia (Main). Simple is supposed to use a more simplified language with a limited vocabulary, and editors are explicitly requested to follow this guideline, yet in practice the vocabulary richness of both samples are at the same level. Detailed analysis of longer units (n-grams of words and part of speech tags) shows that the language of Simple is less complex than that of Main primarily due to the use of shorter sentences, as opposed to drastically simplified syntax or vocabulary. Comparing the two language varieties by the Gunning [[readability]] index supports this conclusion. Authors also report on the topical dependence of language complexity, that is, that the language is more advanced in conceptual articles compared to person-based (biographical) and object-based articles. Finally, authors investigate the relation between conflict and language complexity by analyzing the content of the [[talk pages]] associated to controversial and peacefully developing articles, concluding that controversy has the effect of reducing language complexity.<br />
<br />
== Embed ==<br />
=== Wikipedia Quality ===<br />
<code><br />
<nowiki><br />
Yasseri, Taha; Kornai, András; Kertész, János; Kertész, János. (2012). "[[A Practical Approach to Language Complexity: a Wikipedia Case Study]]". Public Library of Science. DOI: 10.1371/journal.pone.0048386. <br />
</nowiki><br />
</code><br />
<br />
=== English Wikipedia ===<br />
<code><br />
<nowiki><br />
{{cite journal |last1=Yasseri |first1=Taha |last2=Kornai |first2=András |last3=Kertész |first3=János |last4=Kertész |first4=János |title=A Practical Approach to Language Complexity: a Wikipedia Case Study |date=2012 |doi=10.1371/journal.pone.0048386 |url=https://wikipediaquality.com/wiki/A_Practical_Approach_to_Language_Complexity:_a_Wikipedia_Case_Study |journal=Public Library of Science}}<br />
</nowiki><br />
</code><br />
<br />
=== HTML ===<br />
<code><br />
<nowiki><br />
Yasseri, Taha; Kornai, András; Kertész, János; Kertész, János. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/A_Practical_Approach_to_Language_Complexity:_a_Wikipedia_Case_Study">A Practical Approach to Language Complexity: a Wikipedia Case Study</a>&amp;quot;. Public Library of Science. DOI: 10.1371/journal.pone.0048386. <br />
</nowiki><br />
</code></div>Naomi