What We Stand For
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Modeling Popularity and Reliability of Sources in Multilingual Wikipedia
information Article Modeling Popularity and Reliability of Sources in Multilingual Wikipedia Włodzimierz Lewoniewski * , Krzysztof W˛ecel and Witold Abramowicz Department of Information Systems, Pozna´nUniversity of Economics and Business, 61-875 Pozna´n,Poland; [email protected] (K.W.); [email protected] (W.A.) * Correspondence: [email protected] Received: 31 March 2020; Accepted: 7 May 2020; Published: 13 May 2020 Abstract: One of the most important factors impacting quality of content in Wikipedia is presence of reliable sources. By following references, readers can verify facts or find more details about described topic. A Wikipedia article can be edited independently in any of over 300 languages, even by anonymous users, therefore information about the same topic may be inconsistent. This also applies to use of references in different language versions of a particular article, so the same statement can have different sources. In this paper we analyzed over 40 million articles from the 55 most developed language versions of Wikipedia to extract information about over 200 million references and find the most popular and reliable sources. We presented 10 models for the assessment of the popularity and reliability of the sources based on analysis of meta information about the references in Wikipedia articles, page views and authors of the articles. Using DBpedia and Wikidata we automatically identified the alignment of the sources to a specific domain. Additionally, we analyzed the changes of popularity and reliability in time and identified growth leaders in each of the considered months. The results can be used for quality improvements of the content in different languages versions of Wikipedia. -
What We Can Learn from Wikipedia: Why We Should Jump on Board
Collaborative Librarianship Volume 10 Issue 1 Article 2 6-15-2018 What we can learn from Wikipedia: Why we should jump on board Lori Bowen Ayre Galecia Group, [email protected] Follow this and additional works at: https://digitalcommons.du.edu/collaborativelibrarianship Part of the Library and Information Science Commons Recommended Citation Ayre, Lori Bowen (2018) "What we can learn from Wikipedia: Why we should jump on board," Collaborative Librarianship: Vol. 10 : Iss. 1 , Article 2. Available at: https://digitalcommons.du.edu/collaborativelibrarianship/vol10/iss1/2 This Columns is brought to you for free and open access by Digital Commons @ DU. It has been accepted for inclusion in Collaborative Librarianship by an authorized editor of Digital Commons @ DU. For more information, please contact [email protected],[email protected]. Ayre: What We Can Learn from Wikipedia Technology Matters What We Can Learn from Wikipedia: Why We Should Jump Onboard Lori Bowen Ayre ([email protected]) The Galecia Group In an effort to fight conspiracy theories from 4. Wikipedia’s editors should treat each propagating uncontested on YouTube, Susan other with respect and civility Wojcicki, YouTube CEO, announced that con- 5. Wikipedia has no firm rules spiracy videos would be accompanied by “infor- mation cues” to provide an alternate viewpoint. Wikipedia content is a product of the effort of The announcement came during a panel at “hundreds of thousands of people” who write, South by Southwest on March 20th, 2018. improve, and update articles in an effort to keep it “neutral and supported by reliable re- The authoritative resource that would be called sources.”3 It is overwhelmingly made up of vol- upon to both define conspiracy theories and unteer editors with a smaller cadre of volunteers provide the alternative viewpoint on those theo- who have some additional editorial authority. -
Wikimedia Movement Strategy Process
Wikimedia Movement Strategy Process Gereon Kalkuhl, Nicole Ebber Pre-Wikimania meeting Berlin, June 10, 2017 Stephan Kunz, CC Zero, Unsplash The most notable source of knowledge in the world Beko, CC BY-SA 4.0, Wikimedia Commons 10 thousands of volunteers Martin Kraft, CC BY-SA 4.0, Wikimedia Commons st Data is the oil of the 21 century Addshore, CC Zero, Wikimedia Commons Conserving the past: Wiki loves monuments Shreemilabaj, CC BY-SA 4.0, Wikimedia Commons Shaping copyright Sebastiaan Ter-Burg, CC BY 2.0, Wikimedia Commons Changing society Together with GLAMs Romaine, CC Zero, Wikimedia Commons We’ve come a long way. But where do we go from here? What do we want to build or achieve together over the next 15 years? Niccolò Caranti, CC BY-SA 4.0, Wikimedia Commons Wikimania 2016 in Esino Lario Katherine Maher new WMF ED Wikimedia Movement Strategy Niccolò Caranti, CC BY-SA 4.0, Wikimedia Commons Timeline Before Wikimania Develop Wikimedia’s global strategic direction; “Which mountain do we want to climb?” After Wikimania How can we fill this direction with life? (roles, responsibilities, resources, goals); “How do we climb this mountain?” Andrew Neel, CC Zero, Unsplash Goals for this process As a movement, identify a cohesive direction that aligns and inspires us all on our path to 2030. Build trust within our movement through participation in an open process based on shared power. Better understand the people and institutions that form our movement, those we are not yet reaching, and how their needs may change over the next 13 years. -
Amplifying the Impact of Open Access: Wikipedia and the Diffusion of Science
(forthcoming in the Journal of the Association for Information Science and Technology) Amplifying the Impact of Open Access: Wikipedia and the Diffusion of Science Misha Teplitskiy Grace Lu Eamon Duede Dept. of Sociology and KnowledgeLab Computation Institute and KnowledgeLab University of Chicago KnowledgeLab University of Chicago [email protected] University of Chicago [email protected] (773) 834-4787 [email protected] (773) 834-4787 5735 South Ellis Avenue (773) 834-4787 5735 South Ellis Avenue Chicago, Illinois 60637 5735 South Ellis Avenue Chicago, Illinois 60637 Chicago, Illinois 60637 Abstract With the rise of Wikipedia as a first-stop source for scientific knowledge, it is important to compare its representation of that knowledge to that of the academic literature. Here we identify the 250 most heavi- ly used journals in each of 26 research fields (4,721 journals, 19.4M articles in total) indexed by the Scopus database, and test whether topic, academic status, and accessibility make articles from these journals more or less likely to be referenced on Wikipedia. We find that a journal’s academic status (im- pact factor) and accessibility (open access policy) both strongly increase the probability of its being ref- erenced on Wikipedia. Controlling for field and impact factor, the odds that an open access journal is referenced on the English Wikipedia are 47% higher compared to paywall journals. One of the implica- tions of this study is that a major consequence of open access policies is to significantly amplify the dif- fusion of science, through an intermediary like Wikipedia, to a broad audience. Word count: 7894 Introduction Wikipedia, one of the most visited websites in the world1, has become a destination for information of all kinds, including information about science (Heilman & West, 2015; Laurent & Vickers, 2009; Okoli, Mehdi, Mesgari, Nielsen, & Lanamäki, 2014; Spoerri, 2007). -
The Future of Wikimedia and Why New Zealand Museums Should Pay Attention
The Future of Wikimedia and Why New Zealand Museums Should Pay Attention Susan Tolich On the 21st of May it was announced that Mike Dickison will be assuming the position as Aotearoa’s frst Wikipedia-at-large. Tis new role will entail several placements at GLAM institutions around the country where Dickison will act as a ‘Wikipedian in Residence.’ Tis position does not involve editing Wikipedia on behalf of the organisations but focuses on training staf in how to contribute and engage with all parts of Wikimedia and its editing community. Wikipedia is just one of the projects run by the non-proft Wikimedia Foundation; others include Wikimedia Commons and Wikidata. Troughout his career Dickison has had years of experience advocating for Wikipedia to be used in the GLAM sector and has hosted various events to improve the representation of New Zealand endemic species and female scientists on the site.1 While Dickson is the frst Wikipedian-at-large in New Zealand he is part of a much larger global movement which works towards creating a freely accessed ‘sum of all knowledge.’ Wikimedians have partnered with GLAM institutions around the world since 2010 with the mission of ‘connecting audiences to open knowledge, ideas and creativity on a global scale.’2 Other Wikipedian-in-residence projects have ranged from creating documentary photography of Carpathian folk lore, to upskilling librarians in the Ivory Coast to be able to promote their heritage using Wikimedia platforms. It was eforts such as these that also delivered Te Metropolitan Museum 1 Mike Dickison, “New Zealand Wikimedian at large,” Giant Flightless Bird (Blog), 21 May 2018, http://www.giantfightlessbirds.com/2018/05/new-zealand-wikipedian-at-large/ 2 Katherine Maher and Loic Tallon, “Wikimedia and the Met: a shared digital vision,” Medium, 20 April 2018, https://medium.com/freely-sharing-the-sum-of-all-knowledge/wikimedia-and -the-met-a-shared-digital-vision-f91b59eab2e9. -
Language-Agnostic Relation Extraction from Abstracts in Wikis
information Article Language-Agnostic Relation Extraction from Abstracts in Wikis Nicolas Heist, Sven Hertling and Heiko Paulheim * ID Data and Web Science Group, University of Mannheim, Mannheim 68131, Germany; [email protected] (N.H.); [email protected] (S.H.) * Correspondence: [email protected] Received: 5 February 2018; Accepted: 28 March 2018; Published: 29 March 2018 Abstract: Large-scale knowledge graphs, such as DBpedia, Wikidata, or YAGO, can be enhanced by relation extraction from text, using the data in the knowledge graph as training data, i.e., using distant supervision. While most existing approaches use language-specific methods (usually for English), we present a language-agnostic approach that exploits background knowledge from the graph instead of language-specific techniques and builds machine learning models only from language-independent features. We demonstrate the extraction of relations from Wikipedia abstracts, using the twelve largest language editions of Wikipedia. From those, we can extract 1.6 M new relations in DBpedia at a level of precision of 95%, using a RandomForest classifier trained only on language-independent features. We furthermore investigate the similarity of models for different languages and show an exemplary geographical breakdown of the information extracted. In a second series of experiments, we show how the approach can be transferred to DBkWik, a knowledge graph extracted from thousands of Wikis. We discuss the challenges and first results of extracting relations from a larger set of Wikis, using a less formalized knowledge graph. Keywords: relation extraction; knowledge graphs; Wikipedia; DBpedia; DBkWik; Wiki farms 1. Introduction Large-scale knowledge graphs, such as DBpedia [1], Freebase [2], Wikidata [3], or YAGO [4], are usually built using heuristic extraction methods, by exploiting crowd-sourcing processes, or both [5]. -
Defending Democracy & Kristin Skare Orgeret Nordicom-Information
Defending Defending Oslo 8–11 August 2013 Democracy The 2013 NordMedia conference in Oslo marked the 40 years that had passed since the very first Nordic media conference. To acknowledge this 40-year anniversary, it made sense to have a conference theme that dealt with a major and important topic: Defending Democracy. Nordic Defending and Global Diversities in Media and Journalism. Focusing on the rela- tionship between journalism, other media practices and democracy, the plenary sessions raised questions such as: Democracy & Edited by What roles do media and journalism play in democratization • Kristin Skare Orgeret processes and what roles should they play? Nordic and Global Diversities How does the increasingly complex and omnipresent media in Media and Journalism • Hornmoen Harald field affect conditions for freedom of speech? This special issue contains the keynote speeches of Natalie Fenton, Stephen Ward and Ib Bondebjerg. A number of the conference papers have been revised and edited to become articles. Together, the articles presented should give the reader an idea of the breadth and depth of Edited by current Nordic scholarship in the area. Harald Hornmoen & Kristin Skare Orgeret SPECIAL ISSUE Nordicom Review | Volume 35 | August 2014 Nordicom-Information | Volume 36 | Number 2 | August 2014 Nordicom-Information Nordicom Review University of Gothenburg 2014 issue Special Box 713, SE 405 30 Göteborg, Sweden Telephone +46 31 786 00 00 (op.) | Fax +46 31 786 46 55 www.nordicom.gu.se | E-mail: [email protected] SPECIAL ISSUE Nordicom Review | Volume 35 | August 2014 Nordicom-Information | Volume 36 | Number 2 | August 2014 Nordicom Review Journal from the Nordic Information Centre for Media and Communication Research Editor NORDICOM invites media researchers to contri- Ulla Carlsson bute scientific articles, reviews, and debates. -
Introduction: Connections
Wikipedia @ 20 • ::Wikipedia @ 20 Introduction: Connections Joseph Reagle, Jackie Koerner Published on: Oct 15, 2020 License: Creative Commons Attribution 4.0 International License (CC-BY 4.0) Wikipedia @ 20 • ::Wikipedia @ 20 Introduction: Connections Twenty years ago, Wikipedia set out on its path to provide humanity with free access to the sum of all knowledge. Even if this is a mission that can’t be finished, Wikipedia has made remarkable progress toward the impossible. How so? Wikipedia is an encyclopedia built on a wiki. And never has an application (gathering the sum of human knowledge) been so suited to its medium (easily interconnected web pages). Encyclopedias have long been reliant on interconnections. In 1755, the Encyclopédie’s Denis Diderot wrote that the use of cross-references (or renvois) was “the most important part of our encyclopedia scheme.”1 This feature allowed the Encyclopédie’s editors to depict the connective tissue of Enlightenment knowledge and to dodge state and church authorities by way of facetious and satirical references. For example, they linked to articles on the Christian rite of communion, wherein “the body and blood of Christ” is consumed, from the article on “Cannibals.” At the onset of each new informational medium—from paper, to microfilm, to silicon—connectivity was the impetus. Among the documentalists of the early twentieth century, there was Wilhelm Ostwald’s Brücke, a bridge, and Suzanne Briet’s indice, an indicator. Such documentalists advanced indexing and classification schemes to improve interconnections between information. Then, on the cusp of the digital age, Vannevar Bush famously wrote of the power of an electromechanical memex laced with “associative trails.”2 This inspired the hyperlinks of the 1960s and the URLs of the 1990s. -
A Complete, Longitudinal and Multi-Language Dataset of the Wikipedia Link Networks
WikiLinkGraphs: A Complete, Longitudinal and Multi-Language Dataset of the Wikipedia Link Networks Cristian Consonni David Laniado Alberto Montresor DISI, University of Trento Eurecat, Centre Tecnologic` de Catalunya DISI, University of Trento [email protected] [email protected] [email protected] Abstract and result in a huge conceptual network. According to Wiki- pedia policies2 (Wikipedia contributors 2018e), when a con- Wikipedia articles contain multiple links connecting a sub- cept is relevant within an article, the article should include a ject to other pages of the encyclopedia. In Wikipedia par- link to the page corresponding to such concept (Borra et al. lance, these links are called internal links or wikilinks. We present a complete dataset of the network of internal Wiki- 2015). Therefore, the network between articles may be seen pedia links for the 9 largest language editions. The dataset as a giant mind map, emerging from the links established by contains yearly snapshots of the network and spans 17 years, the community. Such graph is not static but is continuously from the creation of Wikipedia in 2001 to March 1st, 2018. growing and evolving, reflecting the endless collaborative While previous work has mostly focused on the complete hy- process behind it. perlink graph which includes also links automatically gener- The English Wikipedia includes over 163 million con- ated by templates, we parsed each revision of each article to nections between its articles. This huge graph has been track links appearing in the main text. In this way we obtained exploited for many purposes, from natural language pro- a cleaner network, discarding more than half of the links and cessing (Yeh et al. -
Contributors
Wikipedia @ 20 • ::Wikipedia @ 20 Contributors Published on: Oct 15, 2020 License: Creative Commons Attribution 4.0 International License (CC-BY 4.0) Wikipedia @ 20 • ::Wikipedia @ 20 Contributors Phoebe Ayers is the librarian for electrical engineering and computer science at the Massachusetts Institute of Technology Libraries. She has been a Wikipedian since 2003, is a former member of the Wikimedia Foundation Board of Trustees, and is the coauthor of How Wikipedia Works: And How You Can Be a Part of It (No Starch Press, 2008). Omer Benjakob is a journalist and researcher based in Israel. He was born in New York and raised in Tel Aviv. His work focuses on Wikipedia and the politics of knowledge in the digital age. He covers the online encyclopedia for Haaretz—Israel’s sole paper of record—in English and Hebrew. His work has also appeared in Wired UK. His academic research focuses on Wikipedia’s ties to science, and he works with scientists from the Weizmann Institute of Science and the Centre de recherches interdisciplinaires to map the growth of knowledge online. He’s pursuing an MA from Tel Aviv University’s Cohn Institute for the History and Philosophy of Science and Ideas. Yochai Benkler is the Berkman Professor of Entrepreneurial Legal Studies at Harvard Law School and codirector of the Berkman Klein Center for Internet & Society at Harvard University. He has been a leading scholar on the impact of the internet on the networked economy and society since the 1990s, with a particular focus on commons, cooperation, and decentralization. His books include Network Propaganda: Manipulation, Disinformation, and Radicalization in American Politics (Oxford University Press, 2018) and The Wealth of Networks: How Social Production Transforms Markets and Freedom (Yale University Press, 2006). -
Offline Product / Content Experience on the Ground Research Reports Money
Ofine context descriptive value web KA video content Lite/Kolibri/Learning Equality Internet in a product feedback Box video content device product feedback software compressed ZIM content product users product / content Orange product Foundation Medical practitioner local content money money (remote product users medical help clinic) users Patient product Wikifundi product Khan Academy content (remote product specs Wikimedicine clinic) money ? project content curation money distribution Columbia research research reports Kiwix / team Prisoners OpenZIM ZIM content build Kiwix app ofine editing capability product feedback compressed ZIM contentcompressed ZIM content software compressed ZIM content software Other Kiwix money RACHEL Kiwix content reusers research reports Rachel product and content Grant based money distributors (Gabriel compressedmoney ZIM content Thullen) money ofine product / content users Governments Experience App and users WMF Grants Play store content re sellers WMF Experience Phone Education Wikipedia App resalers WMF School training School administrator Partnerships teachers (low s (low ofine product / content resource resource WMF school) school) Phone Product manufacturer WMF ofine product / content s Comms app with content users product feedback users NGOs and online teaching tools Unicef content (wiki edu) product / content Wikipedia app user (Android) distribution Mobile network experience on the ground operators Students Other ofine (low Wikipedia Endless Wikipedia resource editors apps school) XOWA Egranary ofine product / content Content Wif access curators Refugees points (Wikipedia 1.0). -
WIKIMEDIA TECHNICAL AREAS Wikimedia Technical Areas
WIKIMEDIA TECHNICAL AREAS Wikimedia Technical Areas MediaWiki Skins MediaWiki Extensions Mobile Apps Web and REST APIs Templates Gadgets and User MediaWiki Core Desktop Apps Machine Learning Bots scripts Cloud Services Site Operations Quality Assurance / Continuous Integration Translation Design Documentation MediaWiki Extensions ● Extends the functionality of MediaWiki software ● Most recommended area for newcomers to get started ● Help develop new or improve existing extensions Skills required: PHP, jQuery, Javascript, CSS/ LESS, MySQL/MariaDB MediaWiki Extensions Extension Echo ● Provides a notification system that can be used by other extensions too ● Mentors Moriel and Matt attending Wikimania Screenshot of Echo notification extension. CC BY-SA 4.0 Ethanlee16 Mobile Apps ● Available for Wikipedia and Wikimedia Commons ● Wikimedia Commons App ○ Allows uploading, or viewing nearby missing pictures ○ Featured project for new developers ● Mentor Vojtěch Dostál attending Wikimania Skills required: Objective-C/Swift (IOS), Java (Android) Commons app screenshot CC BY-SA 3.0 Yuvipanda Desktop Apps Kiwix ● A third party, offline content reader ● Allows access to Wikipedia content through Zim file format ● Featured project for new developers ● Mentors Matthieu, Emmanuel, Stephane attending Wikimania Screenshot of Kiwix running Wikipedia on an OLPC laptop. CC BY-SA 3.0, Victor Grigas Skills required: Swift (IOS), HTML5/JS (browser extension), Java (Android), C++/Python (tools & common codebase) Desktop Apps Huggle ● An anti-vandalism tool that