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LeadWise’s bots do is build a 'supportive audience' with experts. ... Once a supportive audience with over fifteen members, the bots follow the same behavioural rules to request and guide participation: They publicly ask for the names of women who should be added to
Knowledge (XXG). ... We primarily focused on Spanish speaking experts in gender equality. ... We considered that experts were individuals who tweeted heavily about gender equality. Both bots looked for users mentioning related Spanish keywords, such as 'equidad de genero,' and who had already published a large number of related tweets (over 50). ... In total, 22 new women were added to the list of Knowledge (XXG) articles to cover."
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hoaxes that were discovered shortly after being created. We find characteristic differences in terms of article structure and content, embeddedness into the rest of
Knowledge (XXG), and features of the editor who created the hoax. Third, we successfully apply our findings to address a series of classification tasks, most notably to determine whether a given article is a hoax. And finally, we describe and evaluate a task involving humans distinguishing hoaxes from non-hoaxes. We find that humans are not good at solving this task and that our automated classifier outperforms them by a big margin."
243:"YearInSpaceflight", "Asteroid", "BaseballSeason", "MotorsportSeason", and "FormulaOneTeam". He observes sports as recurring theme "throughout all significant 'male categories'. Besides sports other recurring subjects are transport and politics." On the other hand, "the categories with a female overrepresentation show somewhat less obvious recurring themes. Many of these categories are more or less culture related however." The five categories with the most female overrepresentation are "FigureSkater", "Skater", "Garden", "GaelicGamesPlayer", and "Mollusca".
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female-identified collaborators present, and where feedback from the pseudonymous collaborators was neutral (vs. constructive). Interestingly, female participants also tended to assume that one of the non-gendered pseudonyms ("AnonymousOne") was male, and also evaluate feedback from that editor as more critical than male participants who received the same feedback. Based on these findings, the researchers suggest that increasing the visibility of female editors and encouraging constructive feedback may encourage more women to edit
Knowledge (XXG).
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pseudonyms were obviously gendered ("Ms
Trouble", "Mr Football"), and two were gender neutral ("Cheerios4Life", "AnonymousOne"). Since most people are not familiar with the mechanics of wiki editing, the researchers used a Microsoft Word document with "track changes" enabled as a platform for the editing task, to simulate the versioning and commenting capabilities of MediaWiki pages. The researchers also surveyed the students to gather relevant demographic and psychometric data, and compared their survey responses with their editing behaviors.
1430:, which was not about labeling "someone a woman writer”" but about the fact that articles about women were being deliberately "orphaned" by placing them in categories that contained only women and removing articles about them from mainstream categories. It became a Knowledge (XXG) scandal when it was revealed by Filipacchi that experienced editors were behind this massive dislocation of articles about women. It was a scandal because the editors responsible were knowingly doing this in contravention of Knowledge (XXG) guidelines.
1565:, but it is long established that lists and categories are different, complementary search tools. Still, I agree with its core, but for a different reason: "ghettoisation" into minuscule subcategories makes search in categories extremely inconvenient. But this is not restricted to race and gender. For example, try to find an American city I forgot its name, ony remember it starts with "Ap". But this is the problem of WMF, which wastes its money on various weird projects instead of improving the wikimedia engine.
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292:). A paper at the International Conference on Information and Software Technologies studies titled "Quality and Importance of Knowledge (XXG) Articles in Different Languages" studies the connection between importance and quality. The paper's three research questions look at whether importance affects quality, what parameters are useful for applying machine learning to automatically assess importance, and if there are differences between how language editions model importance.
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239:'s ontology, rather than Knowledge (XXG)'s inbuilt category system). The thesis first establishes the distribution of editors by gender (roughly 85% males and 15% females). The number of edits by each group is statistically compared to that baseline distribution. For each category, if it varies from the baseline, it is considered to represent a gender gap, i.e. that editors from that gender are overrepresented in that category.
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elections in the UK, France, Germany, Spain and Italy. The researchers gathered additional data points such as number of views to the political party's
Knowledge (XXG) page the week before the election, the final percentage of vote share each party received, whether a party was new, whether a party was incumbent, and the number of times each party was mentioned in print media during the week before the election.
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added as a parameter, indicating that importance affects quality. The same dataset and model is then trained to predict article importance, finding that about two-thirds of top- and low-importance articles can be correctly identified. Lastly the paper compares the importance of model features between different language editions, finding many differences, although these are not described in more detail.
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most page views does not win the election, rather, it may be a smaller party which interested swing voters. Figure 1(a) shows a high correlation between print media mentions and overall voter share for parties. Figure 1(b) shows
Knowledge (XXG) page views may predict a new party's success, while news outlet mentions are better at predicting an established party's success.
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the previously modeled data, along with two
Knowledge (XXG)-related parameters. The models show that Knowledge (XXG) can be considered a predictor of voter outcome, but it only marginally improves upon the baseline models. Knowledge (XXG)'s predictive power lies in predicting the amount a party's vote share may increase or decrease from the previous election cycle.
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computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from
Knowledge (XXG). Statements independently known to be true consistently receive higher support via our method than do false ones."
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matching a 12 h period, a harmonic from the main frequency. ... The highest activity peak can switch between mornings and evenings, depending on the day. In the process of WP editing, the change of activity patterns on week-ends is clear. ... Along the year the intensity of activity seems conditioned by holidays."
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Research on aspects of article quality across different language editions is an area that has not received a lot of attention, making this paper a welcome addition to the literature. It is also great to see article importance being studied. At the same time, this paper could have made a much stronger
502:
From the abstract: "we propose LeadWise, a system that uses social media bots to recruit and guide contributions from experts to assist non-profits in reaching their goals. ... We focus in particular on experts who can help
Knowledge (XXG) in its objective of reducing the gender gap by covering more
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From the abstract: "This paper analyzes the relationship between search engine hit counts and
Knowledge (XXG) article views by evaluating the cross correlation between them. We observe the hit count estimates of three popular search engines over a month and compare them with the Knowledge (XXG) page
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Researchers have attempted to quantify Knowledge (XXG)'s gender gap and its impact on content type and quality, and to understand the reasons for the gender gap. A new journal article attempts to experimentally evaluate several hypotheses for why women tend to edit Knowledge (XXG) less than men do.
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From the abstract: "We find that, while most hoaxes are detected quickly and have little impact on Knowledge (XXG), a small number of hoaxes survive long and are well cited across the Web. Second, we characterize the nature of successful hoaxes by comparing them to legitimate articles and to failed
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As noted by the researchers, one limitation of this article is that the data is at an aggregated level, while all theories are at the micro level. Also, it is unclear what number of Knowledge (XXG) page views reflect voters versus other groups, such as journalists or those those affiliated with the
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regression models. The first model is a baseline of past voting results. The second model is also a baseline model which includes past voting results, along with all other non-Knowledge (XXG) related data collected. These baseline models serve as a comparison to the third model, which includes all
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Comparing the relative change in page views to the EU Parliament elections article and total voter turnout in the 2009 and 2014 elections indicates that interest in election events is proportional to levels of readership on Knowledge (XXG). This research suggests that often the party garnering the
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The English edition offers the most data on article importance, and the paper therefore uses a dataset of English articles to test if importance affects quality. Using a random forest classifier and a model with 85 parameters, a modest increase in classifier performance is found when importance is
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And how about marginalizing those who support more inclusiveness of women. It is not unheard of to characterize such editors who are perceived to be men as ‘’creepy’’. It creates an environment where any editor who is the target of attacks (many/most editors here) must think twice before showing
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The first dataset used in this research is Knowledge (XXG) page views data on the general election page in 14 different languages (those which are the primary languages of the voting countries). The second dataset includes political parties which had at least 5% vote share in the 2009 and/or 2014
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Yep, it is amazing to what height of idiotism political correctness may be elevated. Last thing I learned some Chinese people find it insulting when being referred to as "that Chinese guy" At first "Oriental" became derogatory, next "Asian" became an insult. And now "Chinese". It comes both from
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From the rest of the paper: "Our data sample is a database of WP edits, of pages written in English in the period of about 10 years ending in January 2010 ... In general, editors have the main power peak at ∼ 1.157 × 10 Hz corresponding to a period of 24 h and a second peak at ∼ 2.315 × 10 Hz,
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The researchers asked 192 male and female college students to contribute a draft essay about school bullying. The version of the draft that participants were asked to work on had already been edited by four other users (secretly, the researchers themselves), identified by pseudonyms. Two of the
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I admit I have never seen this guideline. Unfortunately our rulebook grew to become extremely unwieldy, so I am pretty much sure that it was not violated "deliberately". After seeing the guideline, I disagree with many of its guidance. In particular, the advice "do not create a subcategory for
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From the abstract: "we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient
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From the abstract: "We provide evidence that data on how often a company’s Knowledge (XXG) page is being viewed is linked to its subsequent performance in the stock market. We then develop a portfolio in line with the Knowledge (XXG) usages and demonstrate that our investment strategy based on
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Findings from this study include that while women edited more than men overall (contributed more words to the draft), they were less likely to edit under the conditions designed to approximate the social environment of Knowledge (XXG). Specifically, women edited less where there were few or no
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The results show that despite the huge imbalance in the two groups, pages in some categories receive more edits from users belonging to one gender, while other categories are dominated by the other one. As the "Top five categories where male editors are most overrepresented", the author lists
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From the abstract: "... the Wikimedia Foundation (WMF) has devoted a fair amount of time and resources to tackling ‘gender gap.’ While we acknowledge the good intentions of the WMF and volunteer efforts to improve conditions for women editors on Knowledge (XXG), we argue that borrowing from
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From the rest of the article: "We created 'CauseBots' which are bots that present themselves as a social cause (hiding that the accounts are an automated agent). We also created 'AgentBots' which are bots that present themselves as bots supporting a social cause. ... the first thing all of
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From the abstract: "... the optional Knowledge (XXG) Task provides a test collection for retrieval of individual mathematical formula from Knowledge (XXG) based on search topics that contain exactly one formula pattern. We developed a framework for automatic query generation and immediate
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This research aims to explore the relationship between Knowledge (XXG) page view statistics and electoral results during the 2009 and 2014 European Parliament elections in regards to overall voter turnout and individual party results. The article suggests two reasons why voters might seek
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contribution through comparisons against a sensible baseline (this reviewer notes that the paper cites an in-press paper by the same authors, although that paper's results do not appear to be available in English) because the classifier performance appears to be similar to for instance
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This bachelor thesis looks for gender imbalance among editors for specific categories in the English Knowledge (XXG). The analysis is based on the edits of users who publicly disclosed their gender (about 176 thousand) to more than 3.7 million articles in 470 categories (derived from
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analyses revealed that confidence in expertise and discomfort with editing partially mediated the gender difference in number of articles edited, the standard measure for contribution to Knowledge (XXG)." (See also our 2012 coverage of a related paper by the same authors:
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the text says "perceived minimization of female novelists", i.e., labeling women as women is "minimalization", right? Pray tell me which guideline was violated. We routinely create subcategory by defining characteristics. Now, what shall you do with the ""racist""
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and who reported being occasional contributors. ... Women reported less confidence in their expertise, expressed greater discomfort with editing (which typically involves conflict) and reported more negative responses to critical feedback compared to men.
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although ORES uses a model with a lot fewer parameters. A deeper investigation into article importance would also be worthwhile, for example because importance differs between topic areas, as exemplified by the article on waffle described earlier.
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From the abstract: "We ... show in this work that Knowledge (XXG) editing presents well defined periodic patterns with respect to daily, weekly and monthly activity. In addition, we also show the periodic nature of the number of inter-event in
1561:"African-American poets" is highly dubious, because there are tons of books of literary criticism specifically about AA poets. It is a well-defined and extremely useful category. Of course, some may argue that its function may be served by
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While highlighting some information on such unbalanced distribution, the underlying hypothesis could be further explored by using the quantity of text changed in each edit and other patterns mentioned by the author.
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information: to research new parties which are beyond the voter's familiarity, and to research alternative party options if a voter is unhappy with the party they previously supported (thus becoming swing voters).
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women in its articles. Results from our first pilot show that LeadWise was able to obtain a noteworthy number of expert participants in a two week period with limited requests to targeted specialists."
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I never thought of molluscs as having some kind of female connotations. Or asteroids as male - they're just big rocks in space, what could be less gendered? Maybe there is something I am missing.--
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While much is known about the quality of Knowledge (XXG) articles, less is known about how the different language editions assess article importance. The English Knowledge (XXG)'s article about
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1214:. This has been brought up time and again, but is still dismissed as silliness by many, including editors who themselves belong to a marginalized real-life group such as Gay (for example).
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corporatized diversity initiatives more effectively supports organizational growth rather than addresses the underlying reasons behind women’s low representation and participation."
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Why women edit less, and where they are overrepresented; article importance and quality; predicting elections from Knowledge (XXG): A recap of recent research in our realm
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to describe female humans there are plenty of RSes (just google it) that find it to be derogatory. This is what I found on the top of the google search for
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Yes I remember more than one scandal in the media that involved the depiction of women on Knowledge (XXG). I believe the one you are referring to is the
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Asteroids are among the categories with the most overrepresentation of male editors, and figure skating among those with most female overrepresentation
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the guideline which was deliberately violated by experienced editor(s) who removed hundreds of articles about women writers from sub/categories of
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1301:" derogatory, which is no less weird that the same some find the word "woman" derogatory as well, since it is etymologically inferior to "man".
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The research tests the theory that an increase in Knowledge (XXG) page views may suggest an increase to votes for a party using three linear
757:. WWW '16 Companion. Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee. pp. 591–594.
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today. It is news to me that some people find the word "woman" derogaroy, but I am learning new things every day on Knowledge (XXG).
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Ciampaglia, Giovanni Luca; Shiralkar, Prashant; Rocha, Luis M.; Bollen, Johan; Menczer, Filippo; Flammini, Alessandro (2015-06-17).
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699:"Where are the women in Knowledge (XXG)? Understanding the different psychological experiences of men and women in Knowledge (XXG)"
429:"Where are the women in Knowledge (XXG)? Understanding the different psychological experiences of men and women in Knowledge (XXG)"
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From the abstract: "We analyzed data from a sample of 1,598 individuals in the United States who completed the English version of
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Why women edit less, and where they are overrepresented; article importance and quality; predicting elections from Knowledge (XXG)
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inferiority complex and social stigma/stereotyping. I know it firsthand: when an American calls me "a Pole" I bet my ancestor's
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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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748:"Knowledge (XXG) and stock return: Knowledge (XXG) usage pattern helps to predict the individual stock movement"
554:. Communications in Computer and Information Science. Vol. 639. Springer International Publishing. pp. 613–624.
449:"Knowledge (XXG) and stock return: Knowledge (XXG) usage pattern helps to predict the individual stock movement"
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912:
Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval
584:"Examining potential mechanisms underlying the Knowledge (XXG) gender gap through a collaborative editing task"
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Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion
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Other recent publications that could not be covered in time for this issue include the items listed below.
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News media mentions compared with (a) Knowledge (XXG) page views and (b) absolute level of vote share
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Could you please elaborate: I don't understand the point you are trying to make. Thanks in advance,
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804:. Studies in Computational Intelligence. Vol. 644. Springer International Publishing. pp. 293–300.
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1381:". Do you remember a scandal in the media about wikipedia when some militant feminist noticed the
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951:"Quantifying the relationship between hit count estimates and Knowledge (XXG) article traffic"
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622:"Knowledge (XXG) traffic data and electoral prediction: towards theoretically informed models"
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493:"Quantifying the relationship between hit count estimates and Knowledge (XXG) article traffic"
336:"Knowledge (XXG) traffic data and electoral prediction: towards theoretically informed models"
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660:"Disinformation on the Web: impact, characteristics, and detection of Knowledge (XXG) hoaxes"
422:"Disinformation on the Web: impact, characteristics, and detection of Knowledge (XXG) hoaxes"
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908:"Challenges of mathematical information retrieval in the NTCIR-11 Math Knowledge (XXG) Task"
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Gandica, Y.; Lambiotte, R.; Carletti, T.; Aidos, F. Sampaio dos; Carvalho, J. (2016-03-06).
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486:"Challenges of mathematical information retrieval in the NTCIR-11 Math Knowledge (XXG) Task"
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284:, the highest category possible, but at the same time labelled "high importance" by
1199:, yet none have studied what is obvious to many of the foot-soldiers edting here.
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views. The strongest cross correlations are recorded with their delays in days."
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800:. In Hocine Cherifi; Bruno Gonçalves; Ronaldo Menezes; Roberta Sinatra (eds.).
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220:
1385:? To label someone a "woman writer" was fussed as an extremely gross insult.
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an international survey of Knowledge (XXG) users and readers conducted in 2008
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Analiza porównawcza modeli jakości informacji w narodowych wersjach Wikipedii
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Lewoniewski, Włodzimierz; Węcel, Krzysztof; Abramowicz, Witold (2016-10-13).
382:(See also our 2014 coverage of some related blog posts by the same authors: "
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Proceedings of the 25th International Conference Companion on World Wide Web
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548:"Quality and Importance of Knowledge (XXG) Articles in Different Languages"
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Gender gap connected to conflict aversion and lower confidence among women
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Schubotz, Moritz; Youssef, Abdou; Markl, Volker; Cohl, Howard S. (2015).
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Knowledge (XXG) views is profitable both financially and statistically."
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Lewoniewski, Włodzimierz; Węcel, Krzysztof; Abramowicz, Witold (2015).
779:"Editing diversity in: reading diversity discourses on Knowledge (XXG)"
456:"Editing diversity in: reading diversity discourses on Knowledge (XXG)"
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are you saying that the gender of wikipedia editors is not important?
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982:"LeadWise: using online bots to recruite and guide expert volunteers"
500:"LeadWise: using online bots to recruite and guide expert volunteers"
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for upcoming conferences and events, including submission deadlines.
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Flores-Saviaga, Claudia; Savage, Saiph; Taraborelli, Dario (2016).
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International Journal of Advanced Computer Science and Applications
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Shane-Simpson, Christina; Gillespie-Lynch, Kristen (2016-10-06).
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Knowledge (XXG) use driven by news media or replacing news media?
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Proceedings of the 25th International World Wide Web Conference
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Sure thing, colleague. And other male pigs derogate innocent
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34:
988:. CSCW '16 Companion. New York, NY, USA: ACM. pp. 257–260.
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New tool analyzes article contributors' gender and location
195:"Gender gap on Knowledge (XXG): visible in all categories?"
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Gender gap on Knowledge (XXG): visible in all categories?
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for reviewing or summarizing newly published research.
841:"Computational fact checking from knowledge networks"
550:. In Giedre Dregvaite; Robertas Damasevicius (eds.).
479:"Computational fact checking from knowledge networks"
262:
Quality and importance in different language editions
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Kumar, Srijan; West, Robert; Leskovec, Jure (2016).
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Category:American women novelists of Indian descent
1171:If your comment has not appeared here, you can try
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Ada: A Journal of Gender, New Media, and Technology
914:. SIGIR '15. New York, NY, USA: ACM. pp. 951–954.
473:Wikipedians' weekends in international comparison
777:MacAulay, Maggie; Visser, Rebecca (2016-05-01).
697:Bear, Julia B.; Collier, Benjamin (2016-01-04).
471:See also our 2011 coverage of a related paper: "
620:Yasseri, Taha; Bright, Jonathan (2016-06-18).
1187:Study after study by researchers, sitting in
798:"Circadian Patterns on Knowledge (XXG) Edits"
463:"Circadian patterns on Knowledge (XXG) edits"
280:is for instance labelled "top-importance" by
8:
309:Why women edit less: a controlled experiment
1701:Knowledge (XXG) Signpost archives 2016-11
1297:I find it weird that some find the word "
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18:Knowledge (XXG):Knowledge (XXG) Signpost
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288:(you can find both of these labels on
1210:on Knowledge (XXG) referring to them
552:Information and Software Technologies
29:
7:
949:Tian, Tina; Agrawal, Ankur (2015).
1463:? Some curry-muncher hater barred
1202:For example: How about the use of
534:(Thesis). University of Amsterdam.
56:
28:
1647:is written by editors like you –
1469:Category:American women novelists
1156:These comments are automatically
401:research events page on Meta-wiki
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746:Wei, Pengyu; Wang, Ning (2016).
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414:contributions are always welcome
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1471:. Must be Donald Trump's hand.
1334:hmmmm…As far as using the word
638:10.1140/epjds/s13688-016-0083-3
1563:List of African-American poets
1167:add the page to your watchlist
528:Schrijver, Paul (2016-05-25).
1:
1628:19:50, 24 November 2016 (UTC)
1606:06:40, 11 November 2016 (UTC)
1575:18:00, 14 November 2016 (UTC)
1542:16:19, 14 November 2016 (UTC)
1481:18:18, 11 November 2016 (UTC)
1440:17:17, 11 November 2016 (UTC)
1428:Amanda Filipacchi controversy
1395:03:25, 10 November 2016 (UTC)
186:Wikimedia Research Newsletter
1352:16:08, 9 November 2016 (UTC)
1311:01:51, 9 November 2016 (UTC)
1279:01:12, 9 November 2016 (UTC)
1258:00:45, 8 November 2016 (UTC)
1228:18:43, 5 November 2016 (UTC)
876:10.1371/journal.pone.0128193
810:10.1007/978-3-319-30569-1_22
560:10.1007/978-3-319-46254-7_50
1528:. This all happened around
1206:when it comes to depicting
967:10.14569/IJACSA.2015.060504
588:Computers in Human Behavior
1717:
1218:support for these issues.
1191:, about the causes of the
286:WikiProject Food and Drink
1023:Supplementary references:
715:10.1007/s11199-015-0573-y
600:10.1016/j.chb.2016.09.043
594:(January 2017): 312–328.
407:Other recent publications
227:
994:10.1145/2818052.2869106
920:10.1145/2766462.2767787
736:(free account required)
675:10.1145/2872427.2883085
1526:Knowledge (XXG):GHETTO
1383:category:Women writers
1164:. To follow comments,
1074:
395:Conferences and events
372:ordinary least squares
367:
39:
1073:
365:
282:WikiProject Breakfast
254:coverage from 2011: "
38:
1160:from this article's
802:Complex Networks VII
1204:derogatory language
1183:Why women edit less
1062:"Recent research" →
867:2015PLoSO..1028193C
270:Morten Warncke-Wang
163:Morten Warncke-Wang
1244:referring to them
1151:Discuss this story
1131:Arbitration report
1075:
368:
290:waffle’s talk page
45:← Back to Contents
40:
1522:Category: Writers
1340:"woman vs female"
1175:purging the cache
1116:Technology report
1111:Discussion report
1054:"Recent research"
1002:978-1-4503-3950-6
928:978-1-4503-3621-5
683:978-1-4503-4143-1
536:(bachelor thesis)
511:
232:
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50:View Latest Issue
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