A Complete, Longitudinal and Multi-Language Dataset of the Wikipedia Link Networks
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1 Wikipedia: an Effective Anarchy Dariusz Jemielniak, Ph.D
Wikipedia: An Effective Anarchy Dariusz Jemielniak, Ph.D. Kozminski University [email protected] Paper presented at the Society for Applied Anthropology conference in Baltimore, MD (USA), 27-31 March, 2012 (work in progress) This paper is the first report from a virtual ethnographic study (Hine, 2000; Kozinets, 2010) of Wikipedia community conducted 2006-2012, by the use of participative methods, and relying on an narrative analysis of Wikipedia organization (Czarniawska, 2000; Boje, 2001; Jemielniak & Kostera, 2010). It serves as a general introduction to Wikipedia community, and is also a basis for a discussion of a book in progress, which is going to address the topic. Contrarily to a common misconception, Wikipedia was not the first “wiki” in the world. “Wiki” (originated from Hawaiian word for “quick” or “fast”, and named after “Wiki Wiki Shuttle” on Honolulu International Airport) is a website technology based on a philosophy of tracking changes added by the users, with a simplified markup language (allowing easy additions of, e.g. bold, italics, or tables, without the need to learn full HTML syntax), and was originally created and made public in 1995 by Ward Cunningam, as WikiWikiWeb. WikiWikiWeb was an attractive choice among enterprises and was used for communication, collaborative ideas development, documentation, intranet, knowledge management, etc. It grew steadily in popularity, when Jimmy “Jimbo” Wales, then the CEO of Bomis Inc., started up his encyclopedic project in 2000: Nupedia. Nupedia was meant to be an online encyclopedia, with free content, and written by experts. In an attempt to meet the standards set by professional encyclopedias, the creators of Nupedia based it on a peer-review process, and not a wiki-type software. -
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. -
The Culture of Wikipedia
Good Faith Collaboration: The Culture of Wikipedia Good Faith Collaboration The Culture of Wikipedia Joseph Michael Reagle Jr. Foreword by Lawrence Lessig The MIT Press, Cambridge, MA. Web edition, Copyright © 2011 by Joseph Michael Reagle Jr. CC-NC-SA 3.0 Purchase at Amazon.com | Barnes and Noble | IndieBound | MIT Press Wikipedia's style of collaborative production has been lauded, lambasted, and satirized. Despite unease over its implications for the character (and quality) of knowledge, Wikipedia has brought us closer than ever to a realization of the centuries-old Author Bio & Research Blog pursuit of a universal encyclopedia. Good Faith Collaboration: The Culture of Wikipedia is a rich ethnographic portrayal of Wikipedia's historical roots, collaborative culture, and much debated legacy. Foreword Preface to the Web Edition Praise for Good Faith Collaboration Preface Extended Table of Contents "Reagle offers a compelling case that Wikipedia's most fascinating and unprecedented aspect isn't the encyclopedia itself — rather, it's the collaborative culture that underpins it: brawling, self-reflexive, funny, serious, and full-tilt committed to the 1. Nazis and Norms project, even if it means setting aside personal differences. Reagle's position as a scholar and a member of the community 2. The Pursuit of the Universal makes him uniquely situated to describe this culture." —Cory Doctorow , Boing Boing Encyclopedia "Reagle provides ample data regarding the everyday practices and cultural norms of the community which collaborates to 3. Good Faith Collaboration produce Wikipedia. His rich research and nuanced appreciation of the complexities of cultural digital media research are 4. The Puzzle of Openness well presented. -
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. -
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). -
Jimmy Wales and Larry Sanger, It Is the Largest, Fastest-Growing and Most Popular General Reference Work Currently Available on the Internet
Tomasz „Polimerek” Ganicz Wikimedia Polska WikipediaWikipedia andand otherother WikimediaWikimedia projectsprojects WhatWhat isis Wikipedia?Wikipedia? „Imagine„Imagine aa worldworld inin whichwhich everyevery singlesingle humanhuman beingbeing cancan freelyfreely shareshare inin thethe sumsum ofof allall knowledge.knowledge. That'sThat's ourour commitment.”commitment.” JimmyJimmy „Jimbo”„Jimbo” Wales Wales –– founder founder ofof WikipediaWikipedia As defined by itself: Wikipedia is a free multilingual, open content encyclopedia project operated by the non-profit Wikimedia Foundation. Its name is a blend of the words wiki (a technology for creating collaborative websites) and encyclopedia. Launched in January 2001 by Jimmy Wales and Larry Sanger, it is the largest, fastest-growing and most popular general reference work currently available on the Internet. OpenOpen and and free free content content RichardRichard StallmanStallman definition definition of of free free software: software: „The„The wordword "free""free" inin ourour namename doesdoes notnot referrefer toto price;price; itit refersrefers toto freedom.freedom. First,First, thethe freedomfreedom toto copycopy aa programprogram andand redistributeredistribute itit toto youryour neighbors,neighbors, soso thatthat theythey cancan useuse itit asas wellwell asas you.you. Second,Second, thethe freedomfreedom toto changechange aa program,program, soso ththatat youyou cancan controlcontrol itit insteadinstead ofof itit controllingcontrolling you;you; forfor this,this, thethe sourcesource -
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. -
Wikipedia Editing History in Dbpedia Fabien Gandon, Raphael Boyer, Olivier Corby, Alexandre Monnin
Wikipedia editing history in DBpedia Fabien Gandon, Raphael Boyer, Olivier Corby, Alexandre Monnin To cite this version: Fabien Gandon, Raphael Boyer, Olivier Corby, Alexandre Monnin. Wikipedia editing history in DB- pedia : extracting and publishing the encyclopedia editing activity as linked data. IEEE/WIC/ACM International Joint Conference on Web Intelligence (WI’ 16), Oct 2016, Omaha, United States. hal- 01359575 HAL Id: hal-01359575 https://hal.inria.fr/hal-01359575 Submitted on 2 Sep 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Wikipedia editing history in DBpedia extracting and publishing the encyclopedia editing activity as linked data Fabien Gandon, Raphael Boyer, Olivier Corby, Alexandre Monnin Université Côte d’Azur, Inria, CNRS, I3S, France Wimmics, Sophia Antipolis, France [email protected] Abstract— DBpedia is a huge dataset essentially extracted example, the French editing history dump represents 2TB of from the content and structure of Wikipedia. We present a new uncompressed data. This data extraction is performed by extraction producing a linked data representation of the editing stream in Node.js with a MongoDB instance. It takes 4 days to history of Wikipedia pages. This supports custom querying and extract 55 GB of RDF in turtle on 8 Intel(R) Xeon(R) CPU E5- combining with other data providing new indicators and insights. -
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]. -
Parallel Creation of Gigaword Corpora for Medium Density Languages – an Interim Report
Parallel creation of gigaword corpora for medium density languages – an interim report Peter´ Halacsy,´ Andras´ Kornai, Peter´ Nemeth,´ Daniel´ Varga Budapest University of Technology Media Research Center H-1111 Budapest Stoczek u 2 {hp,kornai,pnemeth,daniel}@mokk.bme.hu Abstract For increased speed in developing gigaword language resources for medium resource density languages we integrated several FOSS tools in the HUN* toolkit. While the speed and efficiency of the resulting pipeline has surpassed our expectations, our experience in developing LDC-style resource packages for Uzbek and Kurdish makes clear that neither the data collection nor the subsequent processing stages can be fully automated. 1. Introduction pages (http://technorati.com/weblog/2007/ So far only a select few languages have been fully in- 04/328.html, April 2007) as a proxy for widely tegrated in the bloodstream of modern communications, available material. While the picture is somewhat distorted commerce, research, and education. Most research, devel- by artificial languages (Esperanto, Volapuk,¨ Basic En- opment, and even the language- and area-specific profes- glish) which have enthusiastic wikipedia communities but sional societies in computational linguistics (CL), natural relatively few webpages elsewhere, and by regional lan- language processing (NLP), and information retrieval (IR), guages in the European Union (Catalan, Galician, Basque, target English, the FIGS languages (French, Italian, Ger- Luxembourgish, Breton, Welsh, Lombard, Piedmontese, man, Spanish),