Analyzing Wikidata Transclusion on English Wikipedia
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Cultural Anthropology Through the Lens of Wikipedia: Historical Leader Networks, Gender Bias, and News-Based Sentiment
Cultural Anthropology through the Lens of Wikipedia: Historical Leader Networks, Gender Bias, and News-based Sentiment Peter A. Gloor, Joao Marcos, Patrick M. de Boer, Hauke Fuehres, Wei Lo, Keiichi Nemoto [email protected] MIT Center for Collective Intelligence Abstract In this paper we study the differences in historical World View between Western and Eastern cultures, represented through the English, the Chinese, Japanese, and German Wikipedia. In particular, we analyze the historical networks of the World’s leaders since the beginning of written history, comparing them in the different Wikipedias and assessing cultural chauvinism. We also identify the most influential female leaders of all times in the English, German, Spanish, and Portuguese Wikipedia. As an additional lens into the soul of a culture we compare top terms, sentiment, emotionality, and complexity of the English, Portuguese, Spanish, and German Wikinews. 1 Introduction Over the last ten years the Web has become a mirror of the real world (Gloor et al. 2009). More recently, the Web has also begun to influence the real world: Societal events such as the Arab spring and the Chilean student unrest have drawn a large part of their impetus from the Internet and online social networks. In the meantime, Wikipedia has become one of the top ten Web sites1, occasionally beating daily newspapers in the actuality of most recent news. Be it the resignation of German national soccer team captain Philipp Lahm, or the downing of Malaysian Airlines flight 17 in the Ukraine by a guided missile, the corresponding Wikipedia page is updated as soon as the actual event happened (Becker 2012. -
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. -
Librarians As Wikimedia Movement Organizers in Spain: an Interpretive Inquiry Exploring Activities and Motivations
Librarians as Wikimedia Movement Organizers in Spain: An interpretive inquiry exploring activities and motivations by Laurie Bridges and Clara Llebot Laurie Bridges Oregon State University https://orcid.org/0000-0002-2765-5440 Clara Llebot Oregon State University https://orcid.org/0000-0003-3211-7396 Citation: Bridges, L., & Llebot, C. (2021). Librarians as Wikimedia Movement Organizers in Spain: An interpretive inQuiry exploring activities and motivations. First Monday, 26(6/7). https://doi.org/10.5210/fm.v26i3.11482 Abstract How do librarians in Spain engage with Wikipedia (and Wikidata, Wikisource, and other Wikipedia sister projects) as Wikimedia Movement Organizers? And, what motivates them to do so? This article reports on findings from 14 interviews with 18 librarians. The librarians interviewed were multilingual and contributed to Wikimedia projects in Castilian (commonly referred to as Spanish), Catalan, BasQue, English, and other European languages. They reported planning and running Wikipedia events, developing partnerships with local Wikimedia chapters, motivating citizens to upload photos to Wikimedia Commons, identifying gaps in Wikipedia content and filling those gaps, transcribing historic documents and adding them to Wikisource, and contributing data to Wikidata. Most were motivated by their desire to preserve and promote regional languages and culture, and a commitment to open access and open education. Introduction This research started with an informal conversation in 2018 about the popularity of Catalan Wikipedia, Viquipèdia, between two library coworkers in the United States, the authors of this article. Our conversation began with a sense of wonder about Catalan Wikipedia, which is ranked twentieth by number of articles, out of 300 different language Wikipedias (Meta contributors, 2020). -
Text Corpora and the Challenge of Newly Written Languages
Proceedings of the 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020), pages 111–120 Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020 c European Language Resources Association (ELRA), licensed under CC-BY-NC Text Corpora and the Challenge of Newly Written Languages Alice Millour∗, Karen¨ Fort∗y ∗Sorbonne Universite´ / STIH, yUniversite´ de Lorraine, CNRS, Inria, LORIA 28, rue Serpente 75006 Paris, France, 54000 Nancy, France [email protected], [email protected] Abstract Text corpora represent the foundation on which most natural language processing systems rely. However, for many languages, collecting or building a text corpus of a sufficient size still remains a complex issue, especially for corpora that are accessible and distributed under a clear license allowing modification (such as annotation) and further resharing. In this paper, we review the sources of text corpora usually called upon to fill the gap in low-resource contexts, and how crowdsourcing has been used to build linguistic resources. Then, we present our own experiments with crowdsourcing text corpora and an analysis of the obstacles we encountered. Although the results obtained in terms of participation are still unsatisfactory, we advocate that the effort towards a greater involvement of the speakers should be pursued, especially when the language of interest is newly written. Keywords: text corpora, dialectal variants, spelling, crowdsourcing 1. Introduction increasing number of speakers (see for instance, the stud- Speakers from various linguistic communities are increas- ies on specific languages carried by Rivron (2012) or Soria ingly writing their languages (Outinoff, 2012), and the In- et al. -
Proceedings of the 3Rd Workshop on Building and Using Comparable
Workshop Programme Saturday, May 22, 2010 9:00-9:15 Opening Remarks Invited talk 9:15 Comparable Corpora Within and Across Languages, Word Frequency Lists and the KELLY Project Adam Kilgarriff 10:30 Break Session 1: Building Comparable Corpora 11:00 Analysis and Evaluation of Comparable Corpora for Under Resourced Areas of Ma- chine Translation Inguna Skadin¸a, Andrejs Vasil¸jevs, Raivis Skadin¸š, Robert Gaizauskas, Dan Tufi¸s and Tatiana Gornostay 11:30 Statistical Corpus and Language Comparison Using Comparable Corpora Thomas Eckart and Uwe Quasthoff 12:00 Wikipedia as Multilingual Source of Comparable Corpora Pablo Gamallo and Isaac González López 12:30 Trillions of Comparable Documents Pascale Fung, Emmanuel Prochasson and Simon Shi 13:00 Lunch break i Saturday, May 22, 2010 (continued) Session 2: Parallel and Comparable Corpora for Machine Translation 14:30 Improving Machine Translation Performance Using Comparable Corpora Andreas Eisele and Jia Xu 15:00 Building a Large English-Chinese Parallel Corpus from Comparable Patents and its Experimental Application to SMT Bin Lu, Tao Jiang, Kapo Chow and Benjamin K. Tsou 15:30 Automatic Terminologically-Rich Parallel Corpora Construction José João Almeida and Alberto Simões 16:00 Break Session 3: Contrastive Analysis 16:30 Foreign Language Examination Corpus for L2-Learning Studies Piotr Banski´ and Romuald Gozdawa-Goł˛ebiowski 17:00 Lexical Analysis of Pre and Post Revolution Discourse in Portugal Michel Généreux, Amália Mendes, L. Alice Santos Pereira and M. Fernanda Bace- lar do Nascimento -
Reciprocal Enrichment Between Basque Wikipedia and Machine Translation
Reciprocal Enrichment Between Basque Wikipedia and Machine Translation Inaki˜ Alegria, Unai Cabezon, Unai Fernandez de Betono,˜ Gorka Labaka, Aingeru Mayor, Kepa Sarasola and Arkaitz Zubiaga Abstract In this chapter, we define a collaboration framework that enables Wikipe- dia editors to generate new articles while they help development of Machine Trans- lation (MT) systems by providing post-edition logs. This collaboration framework was tested with editors of Basque Wikipedia. Their post-editing of Computer Sci- ence articles has been used to improve the output of a Spanish to Basque MT system called Matxin. For the collaboration between editors and researchers, we selected a set of 100 articles from the Spanish Wikipedia. These articles would then be used as the source texts to be translated into Basque using the MT engine. A group of volunteers from Basque Wikipedia reviewed and corrected the raw MT translations. This collaboration ultimately produced two main benefits: (i) the change logs that would potentially help improve the MT engine by using an automated statistical post-editing system , and (ii) the growth of Basque Wikipedia. The results show that this process can improve the accuracy of an Rule Based MT (RBMT) system in nearly 10% benefiting from the post-edition of 50,000 words in the Computer Inaki˜ Alegria Ixa Group, University of the Basque Country UPV/EHU, e-mail: [email protected] Unai Cabezon Ixa Group, University of the Basque Country, e-mail: [email protected] Unai Fernandez de Betono˜ Basque Wikipedia and University of the Basque Country, e-mail: [email protected] Gorka Labaka Ixa Group, University of the Basque Country, e-mail: [email protected] Aingeru Mayor Ixa Group, University of the Basque Country, e-mail: [email protected] Kepa Sarasola Ixa Group, University of the Basque CountryU, e-mail: [email protected] Arkaitz Zubiaga Basque Wikipedia and Queens College, CUNY, CS Department, Blender Lab, New York, e-mail: [email protected] 1 2 Alegria et al. -
ORES: Lowering Barriers with Participatory Machine Learning in Wikipedia
ORES: Lowering Barriers with Participatory Machine Learning in Wikipedia AARON HALFAKER∗, Microsoft, USA R. STUART GEIGER†, University of California, San Diego, USA Algorithmic systems—from rule-based bots to machine learning classifiers—have a long history of supporting the essential work of content moderation and other curation work in peer production projects. From counter- vandalism to task routing, basic machine prediction has allowed open knowledge projects like Wikipedia to scale to the largest encyclopedia in the world, while maintaining quality and consistency. However, conversations about how quality control should work and what role algorithms should play have generally been led by the expert engineers who have the skills and resources to develop and modify these complex algorithmic systems. In this paper, we describe ORES: an algorithmic scoring service that supports real-time scoring of wiki edits using multiple independent classifiers trained on different datasets. ORES decouples several activities that have typically all been performed by engineers: choosing or curating training data, building models to serve predictions, auditing predictions, and developing interfaces or automated agents that act on those predictions. This meta-algorithmic system was designed to open up socio-technical conversations about algorithms in Wikipedia to a broader set of participants. In this paper, we discuss the theoretical mechanisms of social change ORES enables and detail case studies in participatory machine learning around ORES from the 5 years since its deployment. CCS Concepts: • Networks → Online social networks; • Computing methodologies → Supervised 148 learning by classification; • Applied computing → Sociology; • Software and its engineering → Software design techniques; • Computer systems organization → Cloud computing; Additional Key Words and Phrases: Wikipedia; Reflection; Machine learning; Transparency; Fairness; Algo- rithms; Governance ACM Reference Format: Aaron Halfaker and R. -
Critical Point of View: a Wikipedia Reader
w ikipedia pedai p edia p Wiki CRITICAL POINT OF VIEW A Wikipedia Reader 2 CRITICAL POINT OF VIEW A Wikipedia Reader CRITICAL POINT OF VIEW 3 Critical Point of View: A Wikipedia Reader Editors: Geert Lovink and Nathaniel Tkacz Editorial Assistance: Ivy Roberts, Morgan Currie Copy-Editing: Cielo Lutino CRITICAL Design: Katja van Stiphout Cover Image: Ayumi Higuchi POINT OF VIEW Printer: Ten Klei Groep, Amsterdam Publisher: Institute of Network Cultures, Amsterdam 2011 A Wikipedia ISBN: 978-90-78146-13-1 Reader EDITED BY Contact GEERT LOVINK AND Institute of Network Cultures NATHANIEL TKACZ phone: +3120 5951866 INC READER #7 fax: +3120 5951840 email: [email protected] web: http://www.networkcultures.org Order a copy of this book by sending an email to: [email protected] A pdf of this publication can be downloaded freely at: http://www.networkcultures.org/publications Join the Critical Point of View mailing list at: http://www.listcultures.org Supported by: The School for Communication and Design at the Amsterdam University of Applied Sciences (Hogeschool van Amsterdam DMCI), the Centre for Internet and Society (CIS) in Bangalore and the Kusuma Trust. Thanks to Johanna Niesyto (University of Siegen), Nishant Shah and Sunil Abraham (CIS Bangalore) Sabine Niederer and Margreet Riphagen (INC Amsterdam) for their valuable input and editorial support. Thanks to Foundation Democracy and Media, Mondriaan Foundation and the Public Library Amsterdam (Openbare Bibliotheek Amsterdam) for supporting the CPOV events in Bangalore, Amsterdam and Leipzig. (http://networkcultures.org/wpmu/cpov/) Special thanks to all the authors for their contributions and to Cielo Lutino, Morgan Currie and Ivy Roberts for their careful copy-editing. -
A Wikipedia Reader
UvA-DARE (Digital Academic Repository) Critical point of view: a Wikipedia reader Lovink, G.; Tkacz, N. Publication date 2011 Document Version Final published version Link to publication Citation for published version (APA): Lovink, G., & Tkacz, N. (2011). Critical point of view: a Wikipedia reader. (INC reader; No. 7). Institute of Network Cultures. http://www.networkcultures.org/_uploads/%237reader_Wikipedia.pdf General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl) Download date:05 Oct 2021 w ikipedia pedai p edia p Wiki CRITICAL POINT OF VIEW A Wikipedia Reader 2 CRITICAL POINT OF VIEW A Wikipedia Reader CRITICAL POINT OF VIEW 3 Critical Point of View: A Wikipedia Reader Editors: Geert Lovink -
Analyzing Wikidata Transclusion on English Wikipedia
Analyzing Wikidata Transclusion on English Wikipedia Isaac Johnson Wikimedia Foundation [email protected] Abstract. Wikidata is steadily becoming more central to Wikipedia, not just in maintaining interlanguage links, but in automated popula- tion of content within the articles themselves. It is not well understood, however, how widespread this transclusion of Wikidata content is within Wikipedia. This work presents a taxonomy of Wikidata transclusion from the perspective of its potential impact on readers and an associated in- depth analysis of Wikidata transclusion within English Wikipedia. It finds that Wikidata transclusion that impacts the content of Wikipedia articles happens at a much lower rate (5%) than previous statistics had suggested (61%). Recommendations are made for how to adjust current tracking mechanisms of Wikidata transclusion to better support metrics and patrollers in their evaluation of Wikidata transclusion. Keywords: Wikidata · Wikipedia · Patrolling 1 Introduction Wikidata is steadily becoming more central to Wikipedia, not just in maintaining interlanguage links, but in automated population of content within the articles themselves. This transclusion of Wikidata content within Wikipedia can help to reduce maintenance of certain facts and links by shifting the burden to main- tain up-to-date, referenced material from each individual Wikipedia to a single repository, Wikidata. Current best estimates suggest that, as of August 2020, 62% of Wikipedia ar- ticles across all languages transclude Wikidata content. This statistic ranges from Arabic Wikipedia (arwiki) and Basque Wikipedia (euwiki), where nearly 100% of articles transclude Wikidata content in some form, to Japanese Wikipedia (jawiki) at 38% of articles and many small wikis that lack any Wikidata tran- sclusion. -
DLDP Digital Language Survival Kit
The Digital Language Diversity Project Digital Language Survival Kit The DLDP Recommendations to Improve Digital Vitality The DLDP Recommendations to Improve Digital Vitality Imprint The DLDP Digital Language Survival Kit Authors: Klara Ceberio Berger, Antton Gurrutxaga Hernaiz, Paola Baroni, Davyth Hicks, Eleonore Kruse, Vale- ria Quochi, Irene Russo, Tuomo Salonen, Anneli Sarhimaa, Claudia Soria This work has been carried out in the framework of The Digital Language Diversity Project (w ww. dldp.eu), funded by the European Union under the Erasmus+ Programme (Grant Agreement no. 2015-1-IT02-KA204- 015090) © 2018 This work is licensed under a Creative Commons Attribution 4.0 International License. Cover design: Eleonore Kruse Disclaimer This publication reflects only the authors’ view and the Erasmus+ National Agency and the Com- mission are not responsible for any use that may be made of the information it contains. www.dldp.eu www.facebook.com/digitallanguagediversity [email protected] www.twitter.com/dldproject 2 The DLDP Recommendations to Improve Digital Vitality Recommendations at a Glance Digital Capacity Recommendations Indicator Level Recommendations Digital Literacy 2,3 Increasing digital literacy among your native language-speaking community 2,3 Promote the upskilling of language mentors, activists or dissemi- nators 2,3 Establish initiatives to inform and educate speakers about how to acquire and use particular communication and content creation skills 2 Teaching digital literacy to children in your language community through -
Using Natural Language Generation to Bootstrap Missing Wikipedia Articles
Semantic Web 0 (0) 1 1 IOS Press 1 1 2 2 3 3 4 Using Natural Language Generation to 4 5 5 6 Bootstrap Missing Wikipedia Articles: 6 7 7 8 8 9 A Human-centric Perspective 9 10 10 * 11 Lucie-Aimée Kaffee , Pavlos Vougiouklis and Elena Simperl 11 12 School of Electronics and Computer Science, University of Southampton, UK 12 13 E-mails: [email protected], [email protected], [email protected] 13 14 14 15 15 16 16 17 17 Abstract. Nowadays natural language generation (NLG) is used in everything from news reporting and chatbots to social media 18 18 management. Recent advances in machine learning have made it possible to train NLG systems that seek to achieve human- 19 19 level performance in text writing and summarisation. In this paper, we propose such a system in the context of Wikipedia and 20 evaluate it with Wikipedia readers and editors. Our solution builds upon the ArticlePlaceholder, a tool used in 14 under-resourced 20 21 Wikipedia language versions, which displays structured data from the Wikidata knowledge base on empty Wikipedia pages. 21 22 We train a neural network to generate a ’introductory sentence from the Wikidata triples shown by the ArticlePlaceholder, and 22 23 explore how Wikipedia users engage with it. The evaluation, which includes an automatic, a judgement-based, and a task-based 23 24 component, shows that the summary sentences score well in terms of perceived fluency and appropriateness for Wikipedia, and 24 25 can help editors bootstrap new articles.