The Effect of Wikipedia on Tourist Choices
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Universality, Similarity, and Translation in the Wikipedia Inter-Language Link Network
In Search of the Ur-Wikipedia: Universality, Similarity, and Translation in the Wikipedia Inter-language Link Network Morten Warncke-Wang1, Anuradha Uduwage1, Zhenhua Dong2, John Riedl1 1GroupLens Research Dept. of Computer Science and Engineering 2Dept. of Information Technical Science University of Minnesota Nankai University Minneapolis, Minnesota Tianjin, China {morten,uduwage,riedl}@cs.umn.edu [email protected] ABSTRACT 1. INTRODUCTION Wikipedia has become one of the primary encyclopaedic in- The world: seven seas separating seven continents, seven formation repositories on the World Wide Web. It started billion people in 193 nations. The world's knowledge: 283 in 2001 with a single edition in the English language and has Wikipedias totalling more than 20 million articles. Some since expanded to more than 20 million articles in 283 lan- of the content that is contained within these Wikipedias is guages. Criss-crossing between the Wikipedias is an inter- probably shared between them; for instance it is likely that language link network, connecting the articles of one edition they will all have an article about Wikipedia itself. This of Wikipedia to another. We describe characteristics of ar- leads us to ask whether there exists some ur-Wikipedia, a ticles covered by nearly all Wikipedias and those covered by set of universal knowledge that any human encyclopaedia only a single language edition, we use the network to under- will contain, regardless of language, culture, etc? With such stand how we can judge the similarity between Wikipedias a large number of Wikipedia editions, what can we learn based on concept coverage, and we investigate the flow of about the knowledge in the ur-Wikipedia? translation between a selection of the larger Wikipedias. -
Volunteer Contributions to Wikipedia Increased During COVID-19 Mobility Restrictions
Volunteer contributions to Wikipedia increased during COVID-19 mobility restrictions Thorsten Ruprechter1,*, Manoel Horta Ribeiro2, Tiago Santos1, Florian Lemmerich3, Markus Strohmaier3,4, Robert West2, and Denis Helic1 1Graz University of Technology, 8010 Graz, Austria 2EPFL, 1015 Lausanne, Switzerland 3RWTH Aachen University, 52062 Aachen, Germany 4GESIS – Leibniz Institute for the Social Sciences, 50667 Cologne, Germany *Corresponding author ([email protected]) Wikipedia, the largest encyclopedia ever created, is a global initiative driven by volunteer contribu- tions. When the COVID-19 pandemic broke out and mobility restrictions ensued across the globe, it was unclear whether Wikipedia volunteers would become less active in the face of the pandemic, or whether they would rise to meet the increased demand for high-quality information despite the added stress inflicted by this crisis. Analyzing 223 million edits contributed from 2018 to 2020 across twelve Wikipedia language editions, we find that Wikipedia’s global volunteer community responded remarkably to the pandemic, substantially increasing both productivity and the number of newcom- ers who joined the community. For example, contributions to the English Wikipedia increased by over 20% compared to the expectation derived from pre-pandemic data. Our work sheds light on the response of a global volunteer population to the COVID-19 crisis, providing valuable insights into the behavior of critical online communities under stress. Wikipedia is the world’s largest encyclopedia, one of the most prominent volunteer-based information systems in existence [18, 29], and one of the most popular destinations on the Web [2]. On an average day in 2019, users from around the world visited Wikipedia about 530 million times and editors voluntarily contributed over 870 thousand edits to one of Wikipedia’s language editions (Supplementary Table 1). -
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. -
Omnipedia: Bridging the Wikipedia Language
Omnipedia: Bridging the Wikipedia Language Gap Patti Bao*†, Brent Hecht†, Samuel Carton†, Mahmood Quaderi†, Michael Horn†§, Darren Gergle*† *Communication Studies, †Electrical Engineering & Computer Science, §Learning Sciences Northwestern University {patti,brent,sam.carton,quaderi}@u.northwestern.edu, {michael-horn,dgergle}@northwestern.edu ABSTRACT language edition contains its own cultural viewpoints on a We present Omnipedia, a system that allows Wikipedia large number of topics [7, 14, 15, 27]. On the other hand, readers to gain insight from up to 25 language editions of the language barrier serves to silo knowledge [2, 4, 33], Wikipedia simultaneously. Omnipedia highlights the slowing the transfer of less culturally imbued information similarities and differences that exist among Wikipedia between language editions and preventing Wikipedia’s 422 language editions, and makes salient information that is million monthly visitors [12] from accessing most of the unique to each language as well as that which is shared information on the site. more widely. We detail solutions to numerous front-end and algorithmic challenges inherent to providing users with In this paper, we present Omnipedia, a system that attempts a multilingual Wikipedia experience. These include to remedy this situation at a large scale. It reduces the silo visualizing content in a language-neutral way and aligning effect by providing users with structured access in their data in the face of diverse information organization native language to over 7.5 million concepts from up to 25 strategies. We present a study of Omnipedia that language editions of Wikipedia. At the same time, it characterizes how people interact with information using a highlights similarities and differences between each of the multilingual lens. -
Title of Thesis: ABSTRACT CLASSIFYING BIAS
ABSTRACT Title of Thesis: CLASSIFYING BIAS IN LARGE MULTILINGUAL CORPORA VIA CROWDSOURCING AND TOPIC MODELING Team BIASES: Brianna Caljean, Katherine Calvert, Ashley Chang, Elliot Frank, Rosana Garay Jáuregui, Geoffrey Palo, Ryan Rinker, Gareth Weakly, Nicolette Wolfrey, William Zhang Thesis Directed By: Dr. David Zajic, Ph.D. Our project extends previous algorithmic approaches to finding bias in large text corpora. We used multilingual topic modeling to examine language-specific bias in the English, Spanish, and Russian versions of Wikipedia. In particular, we placed Spanish articles discussing the Cold War on a Russian-English viewpoint spectrum based on similarity in topic distribution. We then crowdsourced human annotations of Spanish Wikipedia articles for comparison to the topic model. Our hypothesis was that human annotators and topic modeling algorithms would provide correlated results for bias. However, that was not the case. Our annotators indicated that humans were more perceptive of sentiment in article text than topic distribution, which suggests that our classifier provides a different perspective on a text’s bias. CLASSIFYING BIAS IN LARGE MULTILINGUAL CORPORA VIA CROWDSOURCING AND TOPIC MODELING by Team BIASES: Brianna Caljean, Katherine Calvert, Ashley Chang, Elliot Frank, Rosana Garay Jáuregui, Geoffrey Palo, Ryan Rinker, Gareth Weakly, Nicolette Wolfrey, William Zhang Thesis submitted in partial fulfillment of the requirements of the Gemstone Honors Program, University of Maryland, 2018 Advisory Committee: Dr. David Zajic, Chair Dr. Brian Butler Dr. Marine Carpuat Dr. Melanie Kill Dr. Philip Resnik Mr. Ed Summers © Copyright by Team BIASES: Brianna Caljean, Katherine Calvert, Ashley Chang, Elliot Frank, Rosana Garay Jáuregui, Geoffrey Palo, Ryan Rinker, Gareth Weakly, Nicolette Wolfrey, William Zhang 2018 Acknowledgements We would like to express our sincerest gratitude to our mentor, Dr. -
Multilingual Ranking of Wikipedia Articles with Quality and Popularity Assessment in Different Topics
computers Article Multilingual Ranking of Wikipedia Articles with Quality and Popularity Assessment in Different Topics Włodzimierz Lewoniewski * , Krzysztof W˛ecel and Witold Abramowicz Department of Information Systems, Pozna´nUniversity of Economics and Business, 61-875 Pozna´n,Poland * Correspondence: [email protected]; Tel.: +48-(61)-639-27-93 Received: 10 May 2019; Accepted: 13 August 2019; Published: 14 August 2019 Abstract: On Wikipedia, articles about various topics can be created and edited independently in each language version. Therefore, the quality of information about the same topic depends on the language. Any interested user can improve an article and that improvement may depend on the popularity of the article. The goal of this study is to show what topics are best represented in different language versions of Wikipedia using results of quality assessment for over 39 million articles in 55 languages. In this paper, we also analyze how popular selected topics are among readers and authors in various languages. We used two approaches to assign articles to various topics. First, we selected 27 main multilingual categories and analyzed all their connections with sub-categories based on information extracted from over 10 million categories in 55 language versions. To classify the articles to one of the 27 main categories, we took into account over 400 million links from articles to over 10 million categories and over 26 million links between categories. In the second approach, we used data from DBpedia and Wikidata. We also showed how the results of the study can be used to build local and global rankings of the Wikipedia content. -
Florida State University Libraries
)ORULGD6WDWH8QLYHUVLW\/LEUDULHV 2020 Wiki-Donna: A Contribution to a More Gender-Balanced History of Italian Literature Online Zoe D'Alessandro Follow this and additional works at DigiNole: FSU's Digital Repository. For more information, please contact [email protected] THE FLORIDA STATE UNIVERSITY COLLEGE OF ARTS & SCIENCES WIKI-DONNA: A CONTRIBUTION TO A MORE GENDER-BALANCED HISTORY OF ITALIAN LITERATURE ONLINE By ZOE D’ALESSANDRO A Thesis submitted to the Department of Modern Languages and Linguistics in partial fulfillment of the requirements for graduation with Honors in the Major Degree Awarded: Spring, 2020 The members of the Defense Committee approve the thesis of Zoe D’Alessandro defended on April 20, 2020. Dr. Silvia Valisa Thesis Director Dr. Celia Caputi Outside Committee Member Dr. Elizabeth Coggeshall Committee Member Introduction Last year I was reading Una donna (1906) by Sibilla Aleramo, one of the most important works in Italian modern literature and among the very first explicitly feminist works in the Italian language. Wanting to know more about it, I looked it up on Wikipedia. Although there exists a full entry in the Italian Wikipedia (consisting of a plot summary, publishing information, and external links), the corresponding page in the English Wikipedia consisted only of a short quote derived from a book devoted to gender studies, but that did not address that specific work in great detail. As in-depth and ubiquitous as Wikipedia usually is, I had never thought a work as important as this wouldn’t have its own page. This discovery prompted the question: if this page hadn’t been translated, what else was missing? And was this true of every entry for books across languages, or more so for women writers? My work in expanding the entry for Una donna was the beginning of my exploration into the presence of Italian women writers in the Italian and English Wikipedias, and how it relates back to canon, Wikipedia, and gender studies. -
The Case of Croatian Wikipedia: Encyclopaedia of Knowledge Or Encyclopaedia for the Nation?
The Case of Croatian Wikipedia: Encyclopaedia of Knowledge or Encyclopaedia for the Nation? 1 Authorial statement: This report represents the evaluation of the Croatian disinformation case by an external expert on the subject matter, who after conducting a thorough analysis of the Croatian community setting, provides three recommendations to address the ongoing challenges. The views and opinions expressed in this report are those of the author and do not necessarily reflect the official policy or position of the Wikimedia Foundation. The Wikimedia Foundation is publishing the report for transparency. Executive Summary Croatian Wikipedia (Hr.WP) has been struggling with content and conduct-related challenges, causing repeated concerns in the global volunteer community for more than a decade. With support of the Wikimedia Foundation Board of Trustees, the Foundation retained an external expert to evaluate the challenges faced by the project. The evaluation, conducted between February and May 2021, sought to assess whether there have been organized attempts to introduce disinformation into Croatian Wikipedia and whether the project has been captured by ideologically driven users who are structurally misaligned with Wikipedia’s five pillars guiding the traditional editorial project setup of the Wikipedia projects. Croatian Wikipedia represents the Croatian standard variant of the Serbo-Croatian language. Unlike other pluricentric Wikipedia language projects, such as English, French, German, and Spanish, Serbo-Croatian Wikipedia’s community was split up into Croatian, Bosnian, Serbian, and the original Serbo-Croatian wikis starting in 2003. The report concludes that this structure enabled local language communities to sort by points of view on each project, often falling along political party lines in the respective regions. -
Mapping the Nature of Content and Processes on the English Wikipedia
‘Creating Knowledge’: Mapping the nature of content and processes on the English Wikipedia Report submitted by Sohnee Harshey M. Phil Scholar, Advanced Centre for Women's Studies Tata Institute of Social Sciences Mumbai April 2014 Study Commissioned by the Higher Education Innovation and Research Applications (HEIRA) Programme, Centre for the Study of Culture and Society, Bangalore as part of an initiative on ‘Mapping Digital Humanities in India’, in collaboration with the Centre for Internet and Society, Bangalore. Supported by the Ford Foundation’s ‘Pathways to Higher Education Programme’ (2009-13) Introduction Run a search on Google and one of the first results to show up would be a Wikipedia entry. So much so, that from ‘googled it’, the phrase ‘wikied it’ is catching up with students across university campuses. The Wikipedia, which is a ‘collaboratively edited, multilingual, free Internet encyclopedia’1, is hugely popular simply because of the range and extent of topics covered in a format that is now familiar to most people using the internet. It is not unknown that the ‘quick ready reference’ nature of Wikipedia makes it a popular source even for those in the higher education system-for quick information and even as a starting point for academic writing. Since there is no other source which is freely available on the internet-both in terms of access and information, the content from Wikipedia is thrown up when one runs searches on Google, Yahoo or other search engines. With Wikipedia now accessible on phones, the rate of distribution of information as well as the rate of access have gone up; such use necessitates that the content on this platform must be neutral and at the same time sensitive to the concerns of caste, gender, ethnicity, race etc. -
Does Wikipedia Matter? the Effect of Wikipedia on Tourist Choices Marit Hinnosaar, Toomas Hinnosaar, Michael Kummer, and Olga Slivko Discus Si On Paper No
Dis cus si on Paper No. 15-089 Does Wikipedia Matter? The Effect of Wikipedia on Tourist Choices Marit Hinnosaar, Toomas Hinnosaar, Michael Kummer, and Olga Slivko Dis cus si on Paper No. 15-089 Does Wikipedia Matter? The Effect of Wikipedia on Tourist Choices Marit Hinnosaar, Toomas Hinnosaar, Michael Kummer, and Olga Slivko First version: December 2015 This version: September 2017 Download this ZEW Discussion Paper from our ftp server: http://ftp.zew.de/pub/zew-docs/dp/dp15089.pdf Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar. Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW. Does Wikipedia Matter? The Effect of Wikipedia on Tourist Choices ∗ Marit Hinnosaar† Toomas Hinnosaar‡ Michael Kummer§ Olga Slivko¶ First version: December 2015 This version: September 2017 September 25, 2017 Abstract We document a causal influence of online user-generated information on real- world economic outcomes. In particular, we conduct a randomized field experiment to test whether additional information on Wikipedia about cities affects tourists’ choices of overnight visits. -
Flags and Banners
Flags and Banners A Wikipedia Compilation by Michael A. Linton Contents 1 Flag 1 1.1 History ................................................. 2 1.2 National flags ............................................. 4 1.2.1 Civil flags ........................................... 8 1.2.2 War flags ........................................... 8 1.2.3 International flags ....................................... 8 1.3 At sea ................................................. 8 1.4 Shapes and designs .......................................... 9 1.4.1 Vertical flags ......................................... 12 1.5 Religious flags ............................................. 13 1.6 Linguistic flags ............................................. 13 1.7 In sports ................................................ 16 1.8 Diplomatic flags ............................................ 18 1.9 In politics ............................................... 18 1.10 Vehicle flags .............................................. 18 1.11 Swimming flags ............................................ 19 1.12 Railway flags .............................................. 20 1.13 Flagpoles ............................................... 21 1.13.1 Record heights ........................................ 21 1.13.2 Design ............................................. 21 1.14 Hoisting the flag ............................................ 21 1.15 Flags and communication ....................................... 21 1.16 Flapping ................................................ 23 1.17 See also ............................................... -
Natural Language Processing for Similar Languages, Varieties, and Dialects: a Survey
Natural Language Engineering (2020), 26, pp. 595–612 doi:10.1017/S1351324920000492 SURVEY PAPER Natural language processing for similar languages, varieties, and dialects: A survey Marcos Zampieri 1,∗ Preslav Nakov 2 and Yves Scherrer 3 1Rochester Institute of Technology, USA, 2Qatar Computing Research Institute, HBKU, Qatar and 3University of Helsinki, Finland ∗ Corresponding author. E-mail: [email protected] Abstract There has been a lot of recent interest in the natural language processing (NLP) community in the com- putational processing of language varieties and dialects, with the aim to improve the performance of applications such as machine translation, speech recognition, and dialogue systems. Here, we attempt to survey this growing field of research, with focus on computational methods for processing similar lan- guages, varieties, and dialects. In particular, we discuss the most important challenges when dealing with diatopic language variation, and we present some of the available datasets, the process of data collection, and the most common data collection strategies used to compile datasets for similar languages, varieties, and dialects. We further present a number of studies on computational methods developed and/or adapted for preprocessing, normalization, part-of-speech tagging, and parsing similar languages, language vari- eties, and dialects. Finally, we discuss relevant applications such as language and dialect identification and machine translation for closely related languages, language varieties, and dialects. Keywords: Dialects; similar languages; language varieties; language identification machine; translation parsing 1. Introduction Variation is intrinsic to human language and it is manifested in different ways. There are four generally accepted dimensions of language variation, namely diaphasic, diastratic, diachronic,and diatopic.