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The extensive Wikipedia literature has largely considered Wikipedia in isolation, outside of the context of its broader Internet ecosystem. Very recent research has demonstrated the significance of this limitation, identifying critical relationships between Google and Wikipedia that are highly relevant to many areas of Wikipedia-based research and practice. This paper extends this recent research beyond search engines to examine Wikipedia's relationships with large-scale online communities, Stack Overflow and Reddit in particular. We find evidence of consequential, albeit unidirectional relationships. Wikipedia provides substantial value to both communities, with Wikipedia content increasing visitation, engagement, and revenue, but we find little evidence that these websites contribute to Wikipedia in return. Overall, these findings highlight important connections between Wikipedia and its broader ecosystem that should be considered by researchers studying Wikipedia. Critically, our results also emphasize the key role that volunteer-created Wikipedia content plays in improving other websites, even contributing to revenue generation.

During the first era of the internet — from the 1980s through the early 2000s — internet services were built on open protocols that were controlled by the internet community. This meant that people or organizations could grow their internet presence knowing the rules of the game wouldn’t change later on. Huge web properties were started during this era including Yahoo, Google, Amazon, Facebook, LinkedIn, and YouTube. In the process, the importance of centralized platforms like AOL greatly diminished.

During the second era of the internet, from the mid 2000s to the present, for-profit tech companies — most notably Google, Apple, Facebook, and Amazon (GAFA) — built software and services that rapidly outpaced the capabilities of open protocols. The explosive growth of smartphones accelerated this trend as mobile apps became the majority of internet use. Eventually users migrated from open services to these more sophisticated, centralized services. Even when users still accessed open protocols like the web, they would typically do so mediated by GAFA software and services.

The good news is that billions of people got access to amazing technologies, many of which were free to use. The bad news is that it became much harder for startups, creators, and other groups to grow their internet presence without worrying about centralized platforms changing the rules on them, taking away their audiences and profits. This in turn stifled innovation, making the internet less interesting and dynamic. Centralization has also created broader societal tensions, which we see in the debates over subjects like fake news, state sponsored bots, “no platforming” of users, EU privacy laws, and algorithmic biases. These debates will only intensify in the coming years.

As people continue to adopt mobile devices and turn away from traditional text and toward creating and sharing video, audio, and visual multimedia content, pressure is growing on technology organizations to evolve.

The technology behind Wikimedia projects has advanced in some important ways, including mobile access to the sites, language translation, and visual editing. But in many other ways, Wikimedia projects have been slower to change than their mainstream counterparts. The number of editors on Wikipedia has declined, though it has recently stabilized. Younger users who already spend many hours on commercial social media platforms are not joining Wikipedia communities in large numbers to demand that Wikimedia projects keep up with their changing technological needs. Based on a once-revolutionary and disruptive concept, Wikipedia and most of its sister projects are now facing challenges familiar to other legacy media institutions: how to adapt to new user habits and expectations and take advantage of emerging technology.

These pressures are only set to increase over the next 15 years as new platforms and content types mature—especially in regions where awareness of Wikipedia and related projects is high. But how to decide where to focus? The sheer volume of claims about new information technologies is daunting, and analyses range from the near-term,[1] to the far-term,[2] to the patently speculative.[3]

The Gartner Hype Cycle for Emerging Technologies provides one convenient snapshot of a wide range of technologies currently being developed and discussed by technology and business leaders—and how viable they currently seem:[4]

Wikipedia is the largest existing knowledge repository that is growing on a genuine crowdsourcing support. While the English Wikipedia is the most extensive and the most researched one with over 5 million articles, comparatively little is known about the behaviour and growth of the remaining 283 smaller Wikipedias, the smallest of which, Afar, has only one article. Here, we use a subset of these data, consisting of 14 962 different articles, each of which exists in 26 different languages, from Arabic to Ukrainian. We study the growth of Wikipedias in these languages over a time span of 15 years. We show that, while an average article follows a random path from one language to another, there exist six well-defined clusters of Wikipedias that share common growth patterns. The make-up of these clusters is remarkably robust against the method used for their determination, as we verify via four different clustering methods. Interestingly, the identified Wikipedia clusters have little correlation with language families and groups. Rather, the growth of Wikipedia across different languages is governed by different factors, ranging from similarities in culture to information literacy.

In this paper, we study the network of global interconnections between language communities, based on shared co-editing interests of Wikipedia editors, and show that although English is discussed as a potential lingua franca of the digital space, its domination disappears in the network of co-editing similarities, and instead local connections come to the forefront. Out of the hypotheses we explored, bilingualism, linguistic similarity of languages, and shared religion provide the best explanations for the similarity of interests between cultural communities. Population attraction and geographical proximity are also significant, but much weaker factors bringing communities together. In addition, we present an approach that allows for extracting significant cultural borders from editing activity of Wikipedia users, and comparing a set of hypotheses about the social mechanisms generating these borders. Our study sheds light on how culture is reflected in the collective process of archiving knowledge on Wikipedia, and demonstrates that cross-lingual interconnections on Wikipedia are not dominated by one powerful language. Our findings also raise some important policy questions for the Wikimedia Foundation.

We perform an in-depth analysis on the inequality in 863 Wikimedia projects. We take the complete editing history of 267,304,095 Wikimedia items until 2016, which not only covers every language edition of Wikipedia, but also embraces the complete versions of Wiktionary, Wikisource, Wikivoyage, etc. Our findings of common growth pattern described by the interrelations between four characteristic growth yardsticks suggest a universal law of communal data formation. In this encyclopaedic data set, we observe the interplay between the number of edits and the degree of inequality. In particular, the rapid increase in the Gini coefficient suggests that this entrenched inequality stems from the nature of such open-editing communal data sets, namely the abiogenesis of the supereditors' oligopoly. We show that these supereditor groups were created at the early stages of these open-editing media and are still active. Furthermore, our model considers both short-term and long-term memories to successfully elucidate the underlying mechanism of the establishment of oligarchy in Wikipedia. Our results anticipate a noticeable prospect of such communal databases in the future: the disparity will not be resolved spontaneously.

Crowdsourcing is a process of accumulating the ideas, thoughts or information from many independent participants, with aim to find the best solution for a given challenge. Modern information technologies allow for massive number of subjects to be involved in a more or less spontaneous way. Still, the full potentials of crowdsourcing are yet to be reached. We introduce a modeling framework through which we study the effectiveness of crowdsourcing in relation to the level of collectivism in facing the problem. Our findings reveal an intricate relationship between the number of participants and the difficulty of the problem, indicating the optimal size of the crowdsourced group. We discuss our results in the context of modern utilization of crowdsourcing.

While Wikipedia is a subject of great interest in the computing literature, very little work has considered Wikipedia’s important relationships with other information technologies like search engines. In this paper, we report the results of two deception studies whose goal was to better understand the critical relationship between Wikipedia and Google. These studies silently removed Wikipedia content from Google search results and examined the effect of doing so on participants’ interactions with both websites. Our findings demonstrate and characterize an extensive interdependence between Wikipedia and Google. Google becomes a worse search engine for many queries when it cannot surface Wikipedia content (for example, click-through rates on results pages drop significantly) and the importance of Wikipedia content is likely greater than many improvements to search algorithms. Our results also highlight Google’s critical role in providing readership to Wikipedia. However, we also found evidence that this mutually beneficial relationship is in jeopardy: changes Google has made to its search results that involve directly surfacing Wikipedia content are significantly reducing traffic to Wikipedia. Overall, our findings argue that researchers and practitioners should give deeper consideration to the interdependence between peer production communities and the information technologies that use and surface their content.

Wikipedia is a free Internet encyclopedia with an enormous amount of content. This encyclopedia is written by volunteers with various backgrounds in a collective fashion; anyone can access and edit most of the articles. This open-editing nature may give us prejudice that Wikipedia is an unstable and unreliable source; yet many studies suggest that Wikipedia is even more accurate and self-consistent than traditional encyclopedias. Scholars have attempted to understand such extraordinary credibility, but usually used the number of edits as the unit of time, without consideration of real time. In this work, we probe the formation of such collective intelligence through a systematic analysis using the entire history of 34,534,110 English Wikipedia articles, between 2001 and 2014. From this massive data set, we observe the universality of both timewise and lengthwise editing scales, which suggests that it is essential to consider the real-time dynamics. By considering real time, we find the existence of distinct growth patterns that are unobserved by utilizing the number of edits as the unit of time. To account for these results, we present a mechanistic model that adopts the article editing dynamics based on both editor-editor and editor-article interactions. The model successfully generates the key properties of real Wikipedia articles such as distinct types of articles for the editing patterns characterized by the interrelationship between the numbers of edits and editors, and the article size. In addition, the model indicates that infrequently referred articles tend to grow faster than frequently referred ones, and articles attracting a high motivation to edit counterintuitively reduce the number of participants. We suggest that this decay of participants eventually brings inequality among the editors, which will become more severe with time.

Testing Coleman’s Social-Norm Enforcement Mechanism: Evidence from Wikipedia1

Effects of the News‐Finds‐Me Perception in Communication: Social Media Use Implications for News Seeking and Learning About Politics

Travel

vital article set

VITAL ARTICLE SET

This page contains each Wikipedia language edition Cultural Context Content divided in its territories according to the language territories mapping. Articles are assigned to territories according to the different strategies that have been used to include them into CCC. The label Not Assigned is for those articles which were not possible to classify.

https://eg.wmflabs.org/wiki/index.php/Toledo,_Spain

Main Page of Audiences

The following is a list of user groups defined on Wikipedia, with the rights associated with each.

Design researchers at Wikimedia Foundation seek to understand the needs, motivations, goals and challenges of people who consume and/or contribute to free knowledge. We work to understand how individuals use technology to interact with Wikimedia projects, and other sites. Our work contributes to the work of Wikimedia communities as well as teams across the Wikimedia Foundation. Our goal is creating meaningful positive experiences for the millions of readers and contributors who use Wikipedia and its sister projects every day.

Teamwork is an increasingly important aspect of knowledge production. In particular, factors influencing team formation relative to the composition of expertise are crucial for both organizational performance and for informing policy. In this paper, I draw attention to technology access as a highly influential factor impacting expertise in team formation. I examine the hack of Microsoft Kinect as an exogenous event that suddenly reduced motion-sensing technology costs. I show that great reductions in technology costs substitute for ex ante optimal involvement of area specialists and facilitate involvement of outside-area specialists through collaboration with researchers with broader knowledge—generalists. In other words, technology costs influence the composition of expertise in teamwork, with sufficiently large reductions leading to knowledge creation that combines more broadly across knowledge areas. These findings have important implications for organizations and policy makers in crafting incentives for more diverse knowledge creation through strategic investments that lower technology costs and influence team formation.

An open access archive for works by members of the PKP community. Click on Alperin, Garnett, MacGregor, Smecher, Stranack, Willinsky for a more complete list of their publications on Google Scholar.

repository of open source books, essays related to world economic trends.

Primarily, the goal of TICTeC is to promote and share rigorous and meaningful research into online technologies and digital democracy around the world. The conference facilitates discussion and networking amongst individuals and groups to find real-world solutions through sharing evidence of impact, and (importantly) evidence of what doesn’t work.

TICTeC 2019 will bring together individuals from academic and applied backgrounds as well as businesses, public authorities, NGOs, funders and education institutions to discuss ideas, present research and build a network of individuals interested in the civic technology landscape.

The Five Questions Playing to Win

What are our broad aspirations for our organization & the concrete goals against which we can measure our progress?
Across the potential field available to us, where will we choose to play and not play?
In our chosen place to play, how will we choose to win against the competitors there?
What capabilities are necessary to build and maintain to win in our chosen manner?
What management systems are necessary to operate to build and maintain the key capabilities?

Characterizing Wikipedia Reader Behaviour/Human development index and Wikipedia use cases

This document provides an overview of recent research, policy, process, methods, and critique related to addressing bias in machine learning applications, focusing on recommender systems.

In the past few years, researchers, designers, developers and legal scholars have begun to develop new methods for preventing, identifying, and addressing bias in recommender systems and other machine learning applications, and to formulate best practices for organizations that engage in machine learning development.

Below is an outline of considerations, methods, best practices, and external resources that inform the development of the Wikimedia Foundation's recommender systems.

Archive of past Research showcase presentations

References Preview

Knowledge cloud explorer

What makes customization so appealing? Is it because the content is tailored or because the user feels greater agency? Study 1 tested these propositions with a news-aggregator Website that was either personalized (system-tailored), customized (user-tailored), or neither. Power users rated content quality higher when it had a customizable interface, whereas nonpower users preferred personalized content. In Study 2, half the participants were told that their browsing information may be used for providing requested services while the other half was told that it would not be used. The interaction found in Study 1 was observed only under conditions of low privacy, with the pattern being reversed under high privacy. Significant three-way interactions were found for sense of control and perceived convenience.

danah boyd

Out of Office message generator

Mikhail's clustering

Literacy by country

Possible resource for research partners in other countries: Welcome to the Institute for Money, Technology & Financial Inclusion (IMTFI). Established in 2008, the Institute is housed in the School of Social Sciences at the University of California, Irvine. Its mission is to support research on money & technology among the world's poorest people. We create a community of practice & inquiry into the everyday uses & meanings of money, as well as the technological infrastructures being developed as carriers of mainstream & alternative currencies worldwide. Browse and sort database of projects with advanced search. CLICK HERE

102218 WO x WIKI Final Brand Strategy Recommendation (SHARED) - Google Slides

How Silicon Valley helps spread the same sterile aesthetic across the world