What Motivates and Restricts Chinese Wikipedians to Contribute to 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. -
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. -
International Journal of Computational Linguistics
International Journal of Computational Linguistics & Chinese Language Processing Aims and Scope International Journal of Computational Linguistics and Chinese Language Processing (IJCLCLP) is an international journal published by the Association for Computational Linguistics and Chinese Language Processing (ACLCLP). This journal was founded in August 1996 and is published four issues per year since 2005. This journal covers all aspects related to computational linguistics and speech/text processing of all natural languages. Possible topics for manuscript submitted to the journal include, but are not limited to: • Computational Linguistics • Natural Language Processing • Machine Translation • Language Generation • Language Learning • Speech Analysis/Synthesis • Speech Recognition/Understanding • Spoken Dialog Systems • Information Retrieval and Extraction • Web Information Extraction/Mining • Corpus Linguistics • Multilingual/Cross-lingual Language Processing Membership & Subscriptions If you are interested in joining ACLCLP, please see appendix for further information. Copyright © The Association for Computational Linguistics and Chinese Language Processing International Journal of Computational Linguistics and Chinese Language Processing is published four issues per volume by the Association for Computational Linguistics and Chinese Language Processing. Responsibility for the contents rests upon the authors and not upon ACLCLP, or its members. Copyright by the Association for Computational Linguistics and Chinese Language Processing. All rights reserved. No part of this journal may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical photocopying, recording or otherwise, without prior permission in writing form from the Editor-in Chief. Cover Calligraphy by Professor Ching-Chun Hsieh, founding president of ACLCLP Text excerpted and compiled from ancient Chinese classics, dating back to 700 B.C. -
An Analysis of Contributions to Wikipedia from Tor
Are anonymity-seekers just like everybody else? An analysis of contributions to Wikipedia from Tor Chau Tran Kaylea Champion Andrea Forte Department of Computer Science & Engineering Department of Communication College of Computing & Informatics New York University University of Washington Drexel University New York, USA Seatle, USA Philadelphia, USA [email protected] [email protected] [email protected] Benjamin Mako Hill Rachel Greenstadt Department of Communication Department of Computer Science & Engineering University of Washington New York University Seatle, USA New York, USA [email protected] [email protected] Abstract—User-generated content sites routinely block contri- butions from users of privacy-enhancing proxies like Tor because of a perception that proxies are a source of vandalism, spam, and abuse. Although these blocks might be effective, collateral damage in the form of unrealized valuable contributions from anonymity seekers is invisible. One of the largest and most important user-generated content sites, Wikipedia, has attempted to block contributions from Tor users since as early as 2005. We demonstrate that these blocks have been imperfect and that thousands of attempts to edit on Wikipedia through Tor have been successful. We draw upon several data sources and analytical techniques to measure and describe the history of Tor editing on Wikipedia over time and to compare contributions from Tor users to those from other groups of Wikipedia users. Fig. 1. Screenshot of the page a user is shown when they attempt to edit the Our analysis suggests that although Tor users who slip through Wikipedia article on “Privacy” while using Tor. Wikipedia’s ban contribute content that is more likely to be reverted and to revert others, their contributions are otherwise similar in quality to those from other unregistered participants and to the initial contributions of registered users. -
China Date: 8 January 2007
Refugee Review Tribunal AUSTRALIA RRT RESEARCH RESPONSE Research Response Number: CHN31098 Country: China Date: 8 January 2007 Keywords: China – Taiwan Strait – 2006 Military exercises – Typhoons This response was prepared by the Country Research Section of the Refugee Review Tribunal (RRT) after researching publicly accessible information currently available to the RRT within time constraints. This response is not, and does not purport to be, conclusive as to the merit of any particular claim to refugee status or asylum. Questions 1. Is there corroborating information about military manoeuvres and exercises in Pingtan? 2. Is there any information specifically about the military exercise there in July 2006? 3. Is there any information about “Army day” on 1 August 2006? 4. What are the aquatic farming/fishing activities carried out in that area? 5. Has there been pollution following military exercises along the Taiwan Strait? 6. The delegate makes reference to independent information that indicates that from May until August 2006 China particularly the eastern coast was hit by a succession of storms and typhoons. The last one being the hardest to hit China in 50 years. Could I have information about this please? The delegate refers to typhoon Prapiroon. What information is available about that typhoon? 7. The delegate was of the view that military exercises would not be organised in typhoon season, particularly such a bad one. Is there any information to assist? RESPONSE 1. Is there corroborating information about military manoeuvres and exercises in Pingtan? 2. Is there any information specifically about the military exercise there in July 2006? There is a minor naval base in Pingtan and military manoeuvres are regularly held in the Taiwan Strait where Pingtan in located, especially in the June to August period. -
THE CASE of WIKIPEDIA Shane Greenstein Feng Zhu
NBER WORKING PAPER SERIES COLLECTIVE INTELLIGENCE AND NEUTRAL POINT OF VIEW: THE CASE OF WIKIPEDIA Shane Greenstein Feng Zhu Working Paper 18167 http://www.nber.org/papers/w18167 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 June 2012 The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer- reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2012 by Shane Greenstein and Feng Zhu. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. Collective Intelligence and Neutral Point of View: The Case of Wikipedia Shane Greenstein and Feng Zhu NBER Working Paper No. 18167 June 2012 JEL No. L17,L3,L86 ABSTRACT We examine whether collective intelligence helps achieve a neutral point of view using data from a decade of Wikipedia’s articles on US politics. Our null hypothesis builds on Linus’ Law, often expressed as “Given enough eyeballs, all bugs are shallow.” Our findings are consistent with a narrow interpretation of Linus’ Law, namely, a greater number of contributors to an article makes an article more neutral. No evidence supports a broad interpretation of Linus’ Law. Moreover, several empirical facts suggest the law does not shape many articles. The majority of articles receive little attention, and most articles change only mildly from their initial slant. -
A Natural Experiment at Chinese Wikipedia
American Economic Review 101 (June 2011): 1601–1615 http://www.aeaweb.org/articles.php?doi 10.1257/aer.101.4.1601 = Group Size and Incentives to Contribute: A Natural Experiment at Chinese Wikipedia By Xiaoquan Michael Zhang and Feng Zhu* ( ) Many public goods on the Internet today rely entirely on free user contributions. Popular examples include open source software development communities e.g., ( Linux, Apache , open content production e.g., Wikipedia, OpenCourseWare , and ) ( ) content sharing networks e.g., Flickr, YouTube . Several studies have examined the ( ) incentives that motivate these free contributors e.g., Josh Lerner and Jean Tirole ( 2002; Karim R. Lakhani and Eric von Hippel 2003 . ) In this paper, we examine the causal relationship between group size and incen- tives to contribute in the setting of Chinese Wikipedia, the Chinese language ver- sion of an online encyclopedia that relies entirely on voluntary contributions. The group at Chinese Wikipedia is composed of Chinese-speaking people in mainland China, Taiwan, Hong Kong, Singapore, and other regions in the world, who are aware of Chinese Wikipedia and have access to it. Our identification hinges on the exogenous reduction in group size at Chinese Wikipedia as a result of the block of Chinese Wikipedia in mainland China in October 2005. During the block, mainland Chinese could not use or contribute to Chinese Wikipedia, although contributors outside mainland China could continue to do so. We find that contribution levels of these nonblocked contributors decrease by 42.8 percent on average as a result of the block. We attribute the cause to social effects: contributors receive social benefits from their contributions, and the shrinking group size reduces these social benefits. -