Measures for Quality Assessment of Articles and Infoboxes in Multilingual Wikipedia
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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. -
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. -
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. -
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 ............................................... -
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. -
Dictionary of Slovenian Exonyms Explanatory Notes
GEOGRAFSKI INŠTITUT ANTONA MELIKA ZNANSTVENORAZISKOVALNI CENTER SLOVENSKE AKADEMIJE ZNANOSTI IN UMETNOSTI ANTON MELIK GEOGRAPHICAL INSTITUTE OF ZRC SAZU DICTIONARY OF SLOVENIAN EXONYMS EXPLANATORY NOTES Drago Kladnik Drago Perko AMGI ZRC SAZU Ljubljana 2013 1 Preface The geocoded collection of Slovenia exonyms Zbirka slovenskih eksonimov and the dictionary of Slovenina exonyms Slovar slovenskih eksonimov have been set up as part of the research project Slovenski eksonimi: metodologija, standardizacija, GIS (Slovenian Exonyms: Methodology, Standardization, GIS). They include more than 5,000 of the most frequently used exonyms that were collected from more than 50,000 documented various forms of these types of geographical names. The dictionary contains thirty-four categories and has been designed as a contribution to further standardization of Slovenian exonyms, which can be added to on an ongoing basis and used to find information on Slovenian exonym usage. Currently, their use is not standardized, even though analysis of the collected material showed that the differences are gradually becoming smaller. The standardization of public, professional, and scholarly use will allow completely unambiguous identification of individual features and items named. By determining the etymology of the exonyms included, we have prepared the material for their final standardization, and by systematically documenting them we have ensured that this important aspect of the Slovenian language will not sink into oblivion. The results of this research will not only help preserve linguistic heritage as an important aspect of Slovenian cultural heritage, but also help preserve national identity. Slovenian exonyms also enrich the international treasury of such names and are undoubtedly important part of the world’s linguistic heritage. -
Is Wikipedia Really Neutral? a Sentiment Perspective Study of War-Related Wikipedia Articles Since 1945
PACLIC 29 Is Wikipedia Really Neutral? A Sentiment Perspective Study of War-related Wikipedia Articles since 1945 Yiwei Zhou, Alexandra I. Cristea and Zachary Roberts Department of Computer Science University of Warwick Coventry, United Kingdom fYiwei.Zhou, A.I.Cristea, [email protected] Abstract include books, journal articles, newspapers, web- pages, sound recordings2, etc. Although a “Neutral Wikipedia is supposed to be supporting the 3 “Neutral Point of View”. Instead of accept- point of view” (NPOV) is Wikipedia’s core content ing this statement as a fact, the current paper policy, we believe sentiment expression is inevitable analyses its veracity by specifically analysing in this user-generated content. Already in (Green- a typically controversial (negative) topic, such stein and Zhu, 2012), researchers have raised doubt as war, and answering questions such as “Are about Wikipedia’s neutrality, as they pointed out there sentiment differences in how Wikipedia that “Wikipedia achieves something akin to a NPOV articles in different languages describe the across articles, but not necessarily within them”. same war?”. This paper tackles this chal- Moreover, people of different language backgrounds lenge by proposing an automatic methodology based on article level and concept level senti- share different cultures and sources of information. ment analysis on multilingual Wikipedia arti- These differences have reflected on the style of con- cles. The results obtained so far show that rea- tributions (Pfeil et al., 2006) and the type of informa- sons such as people’s feelings of involvement tion covered (Callahan and Herring, 2011). Further- and empathy can lead to sentiment expression more, Wikipedia webpages actually allow to con- differences across multilingual Wikipedia on tain opinions, as long as they come from reliable au- war-related topics; the more people contribute thors4. -
Wikimedia Foundation Metrics Meeting 25 February 2016 Agenda
Wikimedia Foundation metrics meeting 25 February 2016 Agenda Welcome Wikipedia 15 review Community update Strategic planning update Metrics Feature: Individual Engagement Grant grantees Product demo Questions & answers Photo by Jack Delano, Public domain Welcome! Requisition hires: Contractors, interns & volunteers: ● Sarah Roth - Talent & Culture - SF ● Sara Benson - Talent & Culture - SF ● Chuck Roslof - Legal - SF (conversion) ● Adrianne Davidson - Talent & Culture - US ● Guillaume Lederrey - Discovery - Switzerland ● Kevin Jacobsen - Legal - SF ● Joseph Seddon - Advancement - UK (conv.) ● Riccardo Ciccioli - Technology - Spain ● Chris Koerner - CE - MO ● Jaime Villagomez - Finance - SF Anniversaries Tony Le (5 yrs) Leila Zia (2 yrs) Geoff Brigham (5 yrs) Rachel Stallman (2 yrs) Jon Robson (4 yrs) Megan Neisler (2 yrs) Mark Holmquist (4 yrs) Karen Zwicker (2 yrs) Ed Sanders (3 yrs) Tyler Cipriani (1 yr) Greg Grossmeier (3 yrs) Joseph Allemandou (1 yr) Amy Vossbrinck (3 yrs) Eric Evans (1 yr) Wikipedia 15 15 years of Wikimedians creating an amazing example of generosity and collaboration Wikipedia 15 Photo by, Nirzar Pangarkar CC by sa 3.0 850+ PRESS STORIES IN NEARLY 80 COUNTRIES And it was really good news: 81.5% of English-language press was positive, just 12.4% negative 188 COMMUNITY EVENTS IN 78 COUNTRIES Cakes! Edit-a-thons! Fondue illustrations! OVER 6.5 MM VISITS TO BIRTHDAY SITE IN FIRST WEEK 1.4 Million visits on birthday day alone 98.2K UNIQUE HAPPY BIRTHDAY WISHES ON TWITTER Wikipedia was also the #1 Science & Technology -
Gender Gap in Wikipedia Editing
5 Gender Gap in Wikipedia Editing A Cross Language Comparison Paolo Massa and Asta Zelenkauskaite INTRODUCTION According to various surveys, the percentage of women editing Wikipedia barely reaches 10 percent.1, 2 The issue of gender distribution on Wikipedia was first brought to public attention by an article in the New York Times on January 2011. The article, titled “Define Gender Gap? Look Up Wikipedia’s Contributor List,”3 started by highlighting how, in just ten years, the Wikipedia community accomplished some remarkable goals, such as reaching more than 3.5 million articles in English and starting an online encyclopedia in more than 250 languages. Yet Wikipedia failed to reach at least a minimal gender balance: according to the United Nations and Maas- tricht University 2010 reported survey, less than 13 percent of contributors were female. A more recent survey carried out in 2011 by the Wikimedia Foundation, the nonprofit organization that coordinates the various Wikipedia projects,4 reported even a steeper gender gap: women account for just 9 percent of editors. The importance of Wikipedia editors’ diversity is relevant since Wikipedia is increasingly becoming one of the most accessed Web sources for information needs. Some 53 percent of American Internet users searched for information on Wikipedia as of May 2010; 88 percent of 2,318 university students used Wikipedia during a course-related research process, and finally, Wikipedia is the sixth most visited site on the entire Web.5 Thus, because so many people read the content of Wikipedia pages, it is important to become aware that these pages reflect the point of view of a pre- dominantly male population. -
Russian Political Astroturfing As an Ideological Manifestation of The
Rewriting knowledge: Russian Political Astroturfing as an Ideological Manifestation of the National Role Conceptions Elena Labzina Departments of Political Science and Mathematics Washington University in St. Louis December 6, 2017 PRELIMINARY DRAFT PLEASE DO NOT DISTRIBUTE Abstract As the most implicit part of online censorship, political astroturfing imposes con- siderable difficulties in terms of study because of identification challenges posed by its nature of concealment. Consequently, while being a prominent factor in today's online political discourse, it is still vastly under-investigated. In particular, one perspective completely overlooked so far is how authoritarian regimes may use it to manifest their ideology and national role conceptions. This paper studies the behavior of the most developed and institutionalized \political trolls" in the world - the Russian ones - on the largest collaborative knowledge network, Wikipedia. The paper employs a mixed- methods approach that combines Big Data and other qualitative methodology. First, making use of Wikipedia's unique knowledge-centered structure, which distinguishes it from other popular online social networks, this work utilizes a novel strategy to identify political astroturfing by including the available geographical information in the anal- ysis. Second, the conclusive findings of the following qualitative investigation affirm the existence of political astroturfing on the Russian Wikipedia and that its essential function stems from current Russian regime's self-perception. Third, the study also looks for similar contributions from \genuine" people who happen to share parallel views with the Russian regime but who are not formally associated with it. Substan- tively, this work concludes that \a peaceful regional integrator" is the national role conception that best corresponds to the content produced by both identified political trolls and their \genuine" accomplices. -
Wikimedia Foundation Board of Trustees Meeting July 2018
Wikimedia Foundation Board of Trustees meeting July 2018 ● Welcome ● Operations Agenda ● Movement strategy ● Look back/forward ● Chair’s year-in-review Day One ● Future of the Board ● Executive session Welcome 3 Operations 4 Revenue & Fundraising 5 $104 million raised in FY 17-18 Note: The Advancement Department releases a detailed fundraising report every September. Stay tuned . Revenue by $60.1m Quarter FY17-18 $22.4m $100.4m $10.3m $7.6m Q1 Q2 Q3 Q4 FY2018-19 Q1 revenue projections PROGRAM TARGET PROBABILITY PROJECTION Country Campaigns in Spain, South Africa, $5.3m 90% $4.8m Malaysia, and Japan Low-Level Multi-Country Campaigns $1.7m 90% $1.5m English Testing $2.5m 90% $2.2m Recurring Donations $2.2m 95% $2.1m Major Gifts Pipeline (that may come by September 30) $3m 25% $750,000 TOTAL: $11.35 m Financials 9 +$23.4M (+30%) revenue over plan -$0.4M (-1%) spending under budget Year-end +$9.4M (+10%) YoY growth in revenue +$10.6M (+16%) YoY growth in spending FY17-18 Maintained programmatic ratio at 74% overview ● Repurposed over $2.7M in underspend to Wikidata, Grants, combatting Wikipedia block in Turkey, trademark filings in countries with *Please note that all FY17-18 amount in this key emerging communities, strategic deck are preliminary pending completion of partnerships, Singapore data center, and other the full financial closing process and audit. programmatic investments FY17-18 $100.4M Revenue Actual $78.8M Budget $76.8M $78.4M vs. Target Actual Spending Revenue Spending Revenue exceeded spending by $22M Additional staffing, including $5.4M +16% YoY Increase CDPs Increasing grants to $2.3M +37% Drivers communities $78.4M Funds available for a specific $1.4M purpose (including Movement - FY17-18 Strategy) Building capacity in Technology $1.2M +36% and our data centers $67.6M Wikimania (which was not held $0.8M - FY16-17 in the prior fiscal year) Donation processing fees $0.7M +17% related to increased revenue Legal fees related to community $0.2M +17% defense, supporting privacy, & combating state censorship In FY16-17, Movement Strategy was $1.5M. -
Mobile Editing Why Mobile? Traffic Has Shifted
Technology for growth The future of mobile editing Why mobile? Traffic has shifted In December 2017, mobile pageviews overtook desktop pageviews Mobile editing is growing During 2018, of the top 20 wikis: Mobile web edits ● 10 had more than 10% of edits coming from mobile ● 2 have more than 20% visual 1.2m edits For some of these wikis, mobile editing has increased by more 826k edits than 20% since last year. 2017 2019 Source Preferences and access For a growing number of people, mobile is their preferred or only device for contributing. This is crucial to community growth: we need to meet contributors where they are and where they prefer to be Rawpixel [CC0] But… mobile not optimised for editing ● Important editing functionality not available or hidden ● Special pages were broken ● Core editing features were unusable ● Editing was not accessible to newcomers Key questions ● What needs to be considered ✏ Etherpad before mobile VE is the first editor newcomers see? w.wiki/7BG ● How do we build for contributors with different levels of experience? Two approaches 1) Make common editing workflows simpler on mobile 2) Expand the functionality to more closely resemble the desktop version of the site Two approaches 1) Make common editing workflows simpler on mobile 2) Expand the functionality to more closely resemble the desktop version of the site Why visual editing? “I find that VE has cut my Editathon training time in half: you don't have to explain, and reinforce the skills of adding things like references or bolding.” “As a volunteer in Ecuador, I only recommend using VE to the newbies that attend our events.