210:(XXG)'s contributions: students who are white, economically affluent, male and Internet-experienced are more likely to edit than others. The strongest and statistically significant predictor variables, however, are Internet skills and gender, and regression models show that variables such as race, ethnicity, socioeconomic status, time availability, Internet experience, and confidence in editing Knowledge (XXG) are not significant. The authors find that the gender becomes more significant as one's digital literacy increases. At a low level of Internet skills, the likelihood of one's contribution to Knowledge (XXG) is low, regardless of gender. As one's skills increase, males became much more likely to contribute, but women fall behind. The authors find that women tend to have lower Internet skills than men, which helps explain a part of the Knowledge (XXG) gender gap: to contribute to Knowledge (XXG), one needs to have a certain level of digital literacy, and the digital gap is reducing the number of women who have the required level of skills. The authors crucially admit that "why women, on average, report lower level understanding of Internet-related terms remains a puzzle. Although studies with detailed data about actual skills based on performance tests suggest no gender differences in the observed skills, research that looks at self-rated know-how consistently finds gender variation with real consequences for online behavior". This suggests that while men and women have, in reality, similar skills, women are much less confident about them, which in turns makes them much less confident about contributing to (or trying to contribute to) Knowledge (XXG). This, however, is a hypothesis to be confirmed by future research. In the end, the authors do feel confident enough to conclude that "gender and Internet skills likely have a relatively mild interaction with each other, reinforcing the gender gap at the high end of the Internet skills spectrum." In conclusion,
418:"shows how natural language processing approaches can be used to assist information quality management on a massive scale" on Knowledge (XXG). As the first main contribution, the author highlights his definition of a "comprehensive article quality model that aims to consolidate both the quality of writing and the quality criteria defined in multiple Knowledge (XXG) guidelines and policies into a single model. The model comprises 23 dimensions segmented into the four layers of intrinsic quality, contextual quality, writing quality and organizational quality." Secondly, the dissertation presents methods for automatically detecting quality flaws (overlapping with previous publications co-authored by Ferschke), and evaluates them on a "novel corpus of Knowledge (XXG) articles with neutrality and style flaws". Thirdly, the dissertation presents "an approach for automatically segmenting and tagging the user contributions on article Talk pages to improve work coordination among Wikipedians. These unstructured discussion pages are not easy to navigate and information is likely to get lost over time in the discussion archives."
369:, in order to facilitate and prioritize digitization of their works. The following information from the authors' Knowledge (XXG) articles are used: "article length, article age in days, time elapsed since last revision, revision rate during article’s life, article text (200 topic weights derived from a topic model), category count, translation count, redirect count, estimated views per day, presence of translation for the 10 Wikipedias with the most translations, presence of bibliographic identifier (GND, ISNI, LCCN, VIAF), article quality classification ("Good Article" and "Featured Article"), presence of protected classification, indicator for decade of death for decades 1910–1950, and interactions between article age and all features." The proposed algorithm may be of interest to members of
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group in a rather confusing manner). Authors coded for references (3,029 total), information on editing history, and search ranking in Google, Bing and Yahoo! search engines. The study confirmed that
Knowledge (XXG) articles are highly ranked by all search engines, with Yahoo! actually being even more "Knowledge (XXG)-friendly" than Google. The author shows that (as expected) the articles improve in quality (or at least, number and density of references) over time. Crucially, the authors show that the overall percentage of mainstream news media references has decreased, while references to academic publications increased over that time. By the end of the study period, only the article on (or topic group of?)
432:(a user comment that responds to another) by analyzing the content, in particular detecting "lexical pairs" (giving the examples "(why, because)" and "(?, yes)"), validated against human annotation. As a side result, they observe that "Incorrect indentation (i.e., indentation that implies a reply-to relation with the wrong post) is quite common in longer discussions in the EWDC. In an analysis of 5 random threads longer than 10 turns each, shown in Table 1, we found that 29 of 74 total turns, or 39%±14pp of an average thread, had indentation that misidentified the turn to which they were a reply."
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between different language
Wikipedias, as different cuisines can be very differently described on different projects, thus reinforcing the theory that knowledge can be significantly influenced by one's culture. For Knowledge (XXG) editors, this is a reminder that all language editions suffer from significant biases, and that articles in different language editions can be and usually are significantly different.
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main reference type categories or... not be clearly determined". The authors conclude, commendably, that "Knowledge (XXG) needs to be high on the agenda for health communication researchers and practitioners" and that "communications professionals in the health field need to be much more actively involved in ensuring that the content on
Knowledge (XXG) is reliable and well-sourced with reliable references".
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395:'s theories to analyze cultural similarities and differences between 31 European countries, by looking at the differences between articles on various national cuisines across 27 different European-language Wikipedias. They find that the existence, quality and links of studied Knowledge (XXG) articles can be correlated with data from the
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the ones created by professional journalists on the same topics. Finally, we analyze the revision logs of news events over the past 7 years in order to characterize the WCEP crowd and their activities. The results show that WCEP has reached a stable state in terms of the volume of contributions as well as the size of its crowd..."
202:(XXG) is relatively low" but that "about one in eight students had been given an assignment in class at some point either to edit or create a new entry on Knowledge (XXG)" – which likely suggests that the (undisclosed by authors) university was one where at least one member of the faculty participated in the
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contained more references to news sources than to academic publications. The authors overall support the description of
Knowledge (XXG) as a source aiming for reliability, though they are hesitant to call it reliable, pointing out that for example 15% of analyzed references were coded as "outside the
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are relatively well-developed on numerous
Wikipedias, which could be explained by long-term and significant in size migration of Turkish people to various European countries, and the resulting interest in Turkish cuisine in those countries. The authors also find that significant differences do exist
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From the abstract: "Knowledge (XXG)'s
Current Events Portal (WCEP) is a special part of Knowledge (XXG) that focuses on daily summaries of news events. ...First, we provide descriptive analysis of the collected news events. Second, we compare between the news summaries created by the WCEP crowd and
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This article contributes to the discussion on gender inequalities on
Knowledge (XXG). The authors take a novel approach of looking for answers outside the Knowledge (XXG) community, thus also tying their research into the analysis of new editors recruitment, motivations, and barriers to contribute.
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Even trimodally though, "this strategy for identifying session thresholds is not universally suitable for all user-initiated events". For instance they show League of
Legends, which has modal peaks at 5 minutes and one day. As a reviewer this is easy to describe from a player's perspective. If you
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analyzed the development of the referencing of 45 articles over nine topic groups related to health and nutrition over a period of five years (2007–2011) (unfortunately, the authors are not very clear on which particular articles were analyzed, and tend to use the concepts of an article and topic
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sector. The book chapter is interesting as clearly placing itself in the relatively small body of literature that describe
Knowledge (XXG)/Wikimedia as a social movement. Unfortunately it is primarily a descriptive rather than an analytical piece, and does not provide any significant theoretical
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ask whether there is some way we can talk about contributions in terms of "sessions" rather than atomic operations, in all collaborative work online. The researchers would like to answer "yes," and that a "session" can be defined as the operations conducted until "a good rule-of-thumb inactivity
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Regarding the gender gap issues, women are much less likely to have contributed to
Knowledge (XXG) than men (21% to 38%), and that becomes even more divergent when controlling for student assignments (13% to 32%). The authors find an indication of gender gap affecting the likelihood of Knowledge
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in academia. The survey was carried out in 2009, with a follow-up wave in 2012. The students were asked about their socioeconomic and demographic background, as well as about their level of digital literacy skills. The authors report that "the average respondent's confidence in editing Knowledge
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which is fighting for equal access to free education. The bulk of subsequent subchapters consist of describing the European Wikimedia projects through tables listing whether they exist, estimated size in articles, members, etc., and briefly describing their activities such as involvement in the
206:. The vast majority (99%) of respondents reported having read an entry on Knowledge (XXG), and over a quarter (28%) have had some experience editing it (interestingly, even when controlling for students who were assigned to edit Knowledge (XXG), the former number is still as high as 20%).
282:, but now the idea is to cement that fact as a universal heuristic across many domains. Opposition to this idea has been that session length thresholds will always be arbitrary, or that a session deviates from completing a task that might extend beyond someone logging off for a night.
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route-getting requests, and Knowledge (XXG) viewing (from the desktop, mobile and apps) seem to fit bimodally. Together their the threshold is in the range of 29 to 115 minutes, but all would not be far off of an hour, say the authors. Yet when it comes to Knowledge (XXG) editing,
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Johannes Daxenberger and Iryna Gurevych: Automatically Detecting Corresponding Edit-Turn-Pairs in Knowledge (XXG) Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Short Papers), pages 187–192, Baltimore, Maryland, USA, June 23-25
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justification for calling the Wikimedia movement a social movement, a weakness amplified by the fact that this work fails to engage with the prior relevant body of Knowledge (XXG) research, and is only very loosely connected to the literature on social movements.
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Patryk Korzeniecki: Ruch Wikimediów w państwach europejskich jako przykład aktywności obywatelskiej (Wikimedia Movement in European countries as an example of civil participation). Chapter 6 in: Joachim Osiński, Joanna Zuzanna Popławska (eds.):
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Halfaker, Aaron; Oliver Keyes; Daniel Kluver; Jacob Thebault-Spieker; Tien Nguyen; Kenneth Shores; Anuradha Uduwage; Morten Warncke-Wang (2014-11-11). "User Session Identification Based on Strong Regularities in Inter-activity Time".
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with the bimodal models is not presented. This leaves outliers like Stack Overflow either able to be modeled but not compliant with the one hour rule, when they could just potentially not be describable using the proposed heuristic.
438:: Another paper co-authored by Gurevych, titled "Automatically Detecting Corresponding Edit-Turn-Pairs in Knowledge (XXG)" uses machine learning to automatically identify talk page comments about a particular article edit.
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Regarding "Crucially, the authors show that the overall percentage of mainstream news media references has decreased, while references to academic publications increased over that time", that's not surprising given that
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finds this study to be a highly valuable one, both for the literature on gender gap and online communities, and for the Knowledge (XXG) community and WMF efforts to reduce this gap in our environment.
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To bolster their argument, the authors use empirical data collected from seven datasets to test the hypothesis. The method employed is to take the log-normal time between user events, and then fit a
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341:: This Polish-language book chapter (with an English abstract) looks at the Wikimedia community as a social movement. In the first subchapter, it argues that the Wikimedia movement is a type of
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editing, and MovieLens reviewing and searching, a bimodal 1-hour fit is good, but can be further explained by a trimodal model. In the case of the first two activities the third category is the
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In a recent preprint titled "User Session Identification Based on Strong Regularities in Inter-activity Time", Halfaker and team from the Wikimedia Foundation's Analytics department and the
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play 5 games in a row, which takes 5 minutes queueing between games, and then repeat it daily, you get the histogram seen where the 5 minute peak is about 5 times as tall as the day peak.
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is related to their chance of becoming a Knowledge (XXG) editor, by surveying 547 young adults (aged 21–22) – students at a (presumably American) university, the most used
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Laufer, Paul; Claudia Wagner; Fabian Flöck; Markus Strohmaier (2014-11-17). "Mining cross-cultural relations from Knowledge (XXG) - A study of 31 European food cultures".
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on cross-cultural ties between European countries. In addition to expected findings (all cultures are interested in their own cuisine first, then in famous ones such as
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to the histogram. Once we have a two-humped histogram, we simply find the point which makes half the data "within" session and the other half "between" session.
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does not easily fit into their model at all with a threshold of 335 minutes. The authors claim this is from the high quality edits expected at Stack Overflow.
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665:"The Quality of Content in Open Online Collaboration Platforms: Approaches to NLP-supported Information Quality Management in Knowledge (XXG)"
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555:"Influencing public opinion from corn syrup to obesity: A longitudinal analysis of the references for nutritional entries on Knowledge (XXG)"
824:. WWW Companion '14. Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee. pp. 511--516.
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Gender gap and skills gap; academic citations on the rise; European food cultures: The monthly roundup of research related to Wikimedia.
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What a good idea it was to analyze the foods that Wikipedian editors eat to determine why they contribute what they contribute.
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threshold of about 1 hour" is reached, regardless if you're editing Knowledge (XXG), viewing Knowledge (XXG), rating movies,
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and in those of their neighbours), the article does present some interesting data, for example noting that the articles on
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458:"Does the Administrator Community of Polish Knowledge (XXG) Shut out New Candidates Because of the Acquaintance Relation?"
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data, for the purpose of identifying the most notable (important, popular, read) authors whose work is about to enter the
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A monthly overview of recent academic research about Knowledge (XXG) and other Wikimedia projects, also published as the
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Riddell, Allen B. (2014-11-08). "Public Domain Rank: Identifying Notable Individuals with the Wisdom of the Crowd".
510:"Mind the skills gap: the role of Internet know-how and gender in differentiated contributions to Knowledge (XXG)"
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184:"Mind the skills gap: the role of Internet know-how and gender in differentiated contributions to Knowledge (XXG)"
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Emily K. Jamison, Iryna Gurevych: Adjacency Pair Recognition in Knowledge (XXG) Discussions using Lexical Pairs.
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Overall the authors conclude that one hour seems to suffice as a rule of thumb. But does it? The issue is that a
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next month. In the paper, his doctoral adviser Irina Gurevych and another author construct an method to detect
424:: Ferschke's "English Knowledge (XXG) Discussions Corpus" ("EWDC") is used in a paper, to be presented at the
476:"Development of a semantic data collection tool. : The Wikidata Project as a step towards the semantic web."
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Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion
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Development of a semantic data collection tool. : The Wikidata Project as a step towards the semantic web
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815:"Indexing and Analyzing Knowledge (XXG)'s Current Events Portal, the Daily News Summaries by the Crowd"
487:"Indexing and Analyzing Knowledge (XXG)'s Current Events Portal, the Daily News Summaries by the Crowd"
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Messner, Marcus; Marcia W. DiStaso; Yan Jin; Shana Meganck; Scott Sherman; Sally Norton (2014-10-29).
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Interesting piece at the top. I wish Maximilianklein would translate the 3rd one into English.
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A list of other recent publications that could not be covered in time for this issue –
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In nutritional articles, academic citations rise while news media citations decrease
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Comparison of time between user interactions on Knowledge (XXG), AOL and Cyclopath
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Gender gap and skills gap; academic citations on the rise; European food cultures
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among certain groups. The authors study whether the level of one's skills in
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mainstream news media from being used for references in medical articles.--
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What it takes to become an admin: Insights from the Polish Knowledge (XXG)
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Knowledge (XXG) user session timing compared with other online activities
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460:(cf. earlier coverage of related publications by the same authors: "
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28th Pacific Asia Conference on Language, Information and Computing
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Spychała, Justyna; Mateusz Adamczyk; Piotr Turek (2014-06-30).
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361:: This article proposes a way to combine Knowledge (XXG) and
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Predicting admin elections based on social network analysis
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Decline of adminship candidatures on Polish Knowledge (XXG)
770:"To Use or Not to Use? The Credibility of Knowledge (XXG)"
482:"To Use or Not to Use? The Credibility of Knowledge (XXG)"
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International Journal On Advances in Intelligent Systems
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Ranking public domain authors using Knowledge (XXG) data
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for reviewing or summarizing newly published research.
414:: A recent PhD dissertation by Oliver Ferschke at the
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and selecting underdeveloped articles for development.
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958:If your comment has not appeared here, you can try
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39% of talk page threads contain wrong indentations
611:. WARSAW SCHOOL OF ECONOMICS PRESS, WARSAW 2014
667:. Darmstadt: Technische Universität Darmstadt.
436:Which talk page comment refers to which edit?
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508:Hargittai, Eszter; Aaron Shaw (2014-11-04).
412:Dissertation on automatic quality assessment
813:Tran, Giang Binh; Mohammad Alrifai (2014).
1093:Knowledge (XXG) Signpost archives 2014-11
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663:Ferschke, Oliver (2014-07-15).
348:Knowledge (XXG) Loves Monuments
954:add the page to your watchlist
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177:Wikimedia Research Newsletter
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751:Ubah, Ifeanyichukwu (2013).
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768:Hilles, Stefanie (2014).
443:Other recent publications
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869:"Recent research" →
343:new social movement
938:Discuss this story
918:WikiProject report
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391:: The authors use
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199:convenience sample
45:← Back to Contents
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962:purging the cache
861:"Recent research"
838:978-1-4503-2745-9
478:(bachelor thesis)
383:importance rating
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363:Online Books Page
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