A monthly overview of recent academic research about Wikipedia and other Wikimedia projects, edited jointly with the Wikimedia Research Committee and republished as the Wikimedia Research Newsletter.
Cohort of cross-language Wikipedia editors analyzed
Network graph of the cross-language Wikipedia edits analyzed in the study.
The same network, with the node for the English Wikipedia removed.
Analyzing edits to the then 46 largest Wikipedias between July 9 and August 8, 2013, a study identified a set of about 8,000 contributors (labeled multilingual) with a global user account who have edited more than one of these language versions (excluding Simple English, which was treated separately) in that time frame. It tested five hypotheses about cross-language editing and editors and looked, for instance, at the proportion of contributions that any of these Wikipedias receives from multilingual editors versus contributions from those only editing one language version. The research found that Esperanto and Malay stick out with a high proportion of contributions from multilinguals, and on the other end, that Japanese has few contributions from multilinguals. Overall, in terms of edits per user, multilingual users made more than twice the number of contributions to the study corpus than monolinguals did; they often work on the same topics across language; and in any given language, they are frequently editing articles not edited by monolinguals during the one-month period analyzed here. They thus serve a bridging function between languages.
Two existing write-ups are good starting points to putting the study in context.[supp 1][supp 2] In the long run, it would be interesting to extend the research to (a) cover a longer time span, (b) include contributions from non-registered users, despite technical difficulties, (c) include smaller Wikipedias, and (d) explore the effects of that bridging function in more detail, perhaps in search for ways to support its beneficial effects while minimizing the non-beneficial ones. It would also be interesting to focus on some aspects of those multilingual users (e.g. how do the languages they edit in match with the languages they display on their user pages) or their contributions (e.g. how do their contributions to text, illustrations, references, links, templates, categories or talk page discussions differ across languages, or how contributions from multilinguals differ across topics or between pages with high and low traffic – or to entertain ideas for a multilingual version of editing tools like User:SuggestBot. The paper is one of the first to make use of Wikidata; comparing such cross-lingual Wikipedia contributions with contributions to multi-lingual projects like Wikidata and Commons may also be a fruitful avenue for further research. (See also earlier coverage of a CSCW paper about a similar topic: "Activity of content translators on Wikipedia examined")
Attempt to use Wikipedia pageviews to predict election results in Iran, Germany and the UK
A new paper on arXiv asks the question "Can electoral popularity be predicted using socially generated big data?" Operating on the assumption that "sentiment data is implied in information seeking behaviour," the authors Yasseri and Bright compare Wikipedia page views and Google search trends to election outcomes in Iran, Germany and the UK. In Iran and the UK, where the researchers were able to use the articles of individual politicians, the page view and search trend data correctly pick the winners of the elections. In the UK, the data polled even correctly picks the orders of the runners-up, but the same is not true for Iran. In the German case, no correlation is found between search data and election results. Yasseri and Bright defer to the argument from previous studies on Twitter prediction that conclude that the sample data is too self-selecting. Overall, it is shown that "people do not simply search in the same proportions that they vote." Still the researchers note that these techniques react "quickly to the emergence of new 'insurgent' candidates."
Integrity of Wikipedia and Wikipedia research
A book titled Confidentiality and Integrity in Crowdsourcing Systems contains a chapter on the integrity of the English Wikipedia as a case study of integrity management in crowdsourcing systems. To test the integrity of Wikipedia, they first tried to start a new article with "invalid content" (it got deleted) and then turned to vandalizing pages systematically, both of which violates Wikipedia policies (cf. Wikipedia:Vandalism). They noted that simple cases were caught by automated counter-vandalism tools (ClueBot and XLinkBot, whose user pages – one of them with a typo – are the only references cited in the chapter), whereas more subtle cases ("incorrect information containing words related to the page’s topic" or adding external links present in related Wikipedia articles) were not. No indication was given as to whether these inappropriate edits had later been removed (by the authors themselves or by other users), nor what the affected pages were or what IP address(es) they had used to make those edits.
In a next step, the authors went through dumps of the English Wikipedia from 2001 to 2011 and analyzed revision histories for "100 good and featured articles" (which refers to Wikipedia:Good articles and Wikipedia:Featured articles – later, they call this set "high-quality articles") and "100 non-featured articles" (by which they mean neither good nor featured – later, they refer to this set as "low-quality articles"). In this sample (of which no further details are given), they observed that the number of contributions to high-quality articles is about one order of magnitude higher than that of low-quality articles and "that there is a highly active group of contributors involved from the creation of high quality articles until present", while most editors to low-quality articles never contributed to those pages again. They then looked at revert rates, at the overlap between sets of top contributors to a given article across years, and at the range of topics edited by top contributors to an article, observing that "the top contributors have become the owners of high quality articles and their engagement has increased" (which runs contrary to WP:OWN), "[T]his results in higher quality for a small portion of articles in Wikipedia" and "[T]op contributors of high quality articles are more like- minded than the top contributors of low quality articles", concluding "that the main difference between low quality and featured articles is the number of contributions."
From that, they venture into extrapolating to crowdsourcing systems more generally: "[w]e observe that to have higher integrity in crowdsourcing systems, we need to have a permanent set of contributors who are dedicated for maintaining the quality of the contributions to the articles. For systems with open access such as Wikipedia, this can be a huge burden for the permanent editors. Therefore, we need new mechanisms for coordinating the activities in a crowdsourcing information system." No discussion of these new mechanisms is offered.
The chapter has a few simple tables and plots but no link to the underlying data nor the code used for the analysis, nor links to relevant literature or Wikipedia policies, but it is paywalled behind a price tag of $29.95 / €24.95 / £19.95. Given that the experimental edits to Wikipedia actually damaged the project, it is hard to imagine that an ethical review panel involving Wikipedians might have approved the study in that form. In fact, such a panel does exist in the form of the Research Committee, which had not been contacted about the project. Considering further that the conclusions of the study are not new, their possibly interesting implications for crowdsourcing more generally are not discussed and neither the paper nor its materials are available to those concerned about the integrity of Wikipedia, it is hard to see any benefit of this study that would outweigh the damage it caused (cf. earlier coverage: "Link spam research with controversial genesis but useful results", "Traffic analysis report and research ethics").
"How we found a million style and grammar errors in the English Wikipedia": The abstract of a keynote talk with this title at the upcoming FOSDEM conference reports on the application of "Language Tool", an open source grammar-and-style error checker for multiple languages – including English, German and Polish – that has been in development since 2003, to Wikipedia articles (example). Its method is like regular expressions except it takes into account parts-of-speech. When it was applied to 20,000 English Wikipedia articles, 37,000 errors were found. However, the false positive rate was high, as only 29 of 200 manual inspections were human validated. This raises a question: does Wikipedia need to be grammar and style checked differently from other texts?
"Evaluation of gastroenterology and hepatology articles on Wikipedia" – In this paper, the author was concerned about "the tendency of medical students to use Wikipedia as their preferred learning resource", so he and his assistants assessed 39 articles on topics related to gastroenterology and hepatology for a number of quality indicators (e.g. length, depth, accuracy, readability, number of tables or illustrations, topicality of listed references, missing important references). He concludes that "Wikipedia is not suitable for medical students as a learning resource for gastroenterology and hepatology topics" and suggests they turn to the peer-reviewed literature instead. The study is hidden behind a paywall, which reflects current practice in the field and is one of the reasons why some of the articles in the study do not contain suitable illustrations. The article has been discussed at WikiProject Medicine but taking concrete action is difficult, as the details of the assessment are hidden further away in an appendix not available "ahead of print".
Overview of research on FLOSS and Wikipedia: A draft paper titled "Peer Production: A Modality of Collective Intelligence" co-authored by one of the pioneers of peer production/collaborative intelligence research, Yochai Benkler, discusses some of the developments in this field, using Wikipedia as one of several case studies, alongside free software (FLOSS). The paper, in essence, is a literature review of the fields of "organization, motivation, and quality" of peer production.
In battle over Walt Whitman's sexuality, Wikipedia policies "tamed the mass into producing a good encyclopedia entry": The revision history and talk page of the Wikipedia article on American poet Walt Whitman are examined closely in a paper by three scholars from Southern Illinois University Edwardsville (SIUE), two of whom have previously published research on Whitman themselves. The authors focus on how Wikipedians covered Whitman's sexual orientation, which "has been settled in a particularly ambiguous way" in the academic literature. The paper examines the talk page discussions on this aspect and how they were influenced by Wikipedia policies on e.g. original research, neutrality and verifiability, and concludes: "Significantly, and perhaps ironically, Wikipedia’s evolving editorial codes have resulted in a Whitman page that, while curtailing open accessibility, or at least free-form contributions [the article has been semi-protected since 2008], generally reflects the nuance and complexity of the scholarly consensus regarding Whitman's sexuality. In this regard, Wikipedia has instituted something like the professional standards to which all scholars are subject: peer review, citations, and at least an affected tone of neutrality. Contrary to its democratic origins, Wikipedia has, at least in this case, tamed the mass into producing a good encyclopedia entry".
Elinor Ostrom's theories applied to Wikipedia: A French-language paper in Management et Avenir (Management and Future) by two researchers from the University of Montpellier applies Nobelist Elinor Ostrom's analysis of self-organizing systems to the governance of Wikipedia.
^Meyer, Christian M. Wiktionary: "The Metalexicographic and the Natural Language Processing Perspective". Technische Universität Darmstadt, Darmstadt Ph.D. Thesis], (2013) http://tuprints.ulb.tu-darmstadt.de/3654/