Wikimedia Foundation | EPFL Graph Search

The Wikimedia Foundation, Inc. (WMF) is an American 501(c)(3) nonprofit organization headquartered in San Francisco, California, and registered as a charitable foundation under local laws. Best known as the hosting platform for Wikipedia, a crowdsourced online encyclopedia, it also hosts other related projects and MediaWiki, a wiki software. The Wikimedia Foundation was established in 2003 in St. Petersburg, Florida, by Jimmy Wales as a nonprofit way to fund Wikipedia, Wiktionary, and other crowdsourced wiki projects that had until then been hosted by Bomis, Wales's for-profit company. The Foundation finances itself mainly through millions of small donations from Wikipedia readers, collected through email campaigns and annual fundraising banners placed on Wikipedia and its sister projects. These are complemented by grants from philanthropic organizations and tech companies, and starting in 2022, by services income from Wikimedia Enterpris

Overview of the MOSAiC expedition—Atmosphere

Hélène Paule Angot, Ivo Fabio Beck, Thomas Krumpen, Julia Schmale

With the Arctic rapidly changing, the needs to observe, understand, and model the changes are essential. To support these needs, an annual cycle of observations of atmospheric properties, processes, and interactions were made while drifting with the sea ice across the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. An international team designed and implemented the comprehensive program to document and characterize all aspects of the Arctic atmospheric system in unprecedented detail, using a variety of approaches, and across multiple scales. These measurements were coordinated with other observational teams to explore cross-cutting and coupled interactions with the Arctic Ocean, sea ice, and ecosystem through a variety of physical and biogeochemical processes. This overview outlines the breadth and complexity of the atmospheric research program, which was organized into 4 subgroups: atmospheric state, clouds and precipitation, gases and aerosols, and energy budgets. Atmospheric variability over the annual cycle revealed important influences from a persistent large-scale winter circulation pattern, leading to some storms with pressure and winds that were outside the interquartile range of past conditions suggested by long-term reanalysis. Similarly, the MOSAiC location was warmer and wetter in summer than the reanalysis climatology, in part due to its close proximity to the sea ice edge. The comprehensiveness of the observational program for characterizing and analyzing atmospheric phenomena is demonstrated via a winter case study examining air mass transitions and a summer case study examining vertical atmospheric evolution. Overall, the MOSAiC atmospheric program successfully met its objectives and was the most comprehensive atmospheric measurement program to date conducted over the Arctic sea ice. The obtained data will support a broad range of coupled-system scientific research and provide an important foundation for advancing multiscale modeling capabilities in the Arctic.

2022

Wikipedia Reader Navigation: When Synthetic Data Is Enough

Akhil Arora, Alberto Garcia Duran, Tiziano Piccardi, Robert West

Every day millions of people read Wikipedia. When navigating the vast space of available topics using hyperlinks, readers describe trajectories on the article network. Understanding these navigation patterns is crucial to better serve readers' needs and address structural biases and knowledge gaps. However, systematic studies of navigation on Wikipedia are hindered by a lack of publicly available data due to the commitment to protect readers' privacy by not storing or sharing potentially sensitive data. In this paper, we ask: How well can Wikipedia readers' navigation be approximated by using publicly available resources, most notably the Wikipedia clickstream data? We systematically quantify the differences between real navigation sequences and synthetic sequences generated from the clickstream data, in 6 analyses across 8 Wikipedia language versions. Overall, we find that the differences between real and synthetic sequences are statistically significant, but with small effect sizes, often well below 10%. This constitutes quantitative evidence for the utility of the Wikipedia clickstream data as a public resource: clickstream data can closely capture reader navigation on Wikipedia and provides a sufficient approximation for most practical downstream applications relying on reader data. More broadly, this study provides an example for how clickstream-like data can generally enable research on user navigation on online platforms while protecting users' privacy.

ASSOC COMPUTING MACHINERY2022

How We Use Wikipedia: Studying Readers' Behavior with Navigation Traces

Tiziano Piccardi

In the information age, the Web and the growing global connectivity drastically simplified our access to information. Learning and fact-checking from online resources is nowadays part of our daily routine. Studying the dynamic associated with online content consumption is critical to understanding human behavior and informing future platforms' design. In this thesis, we provide a comprehensive overview of online knowledge-seeking, a specific instance of information-seeking, by describing the behavioral pattern of Wikipedia readers. Despite the importance and pervasiveness of Wikipedia as one of the largest platforms for open knowledge, surprisingly little is known about how people navigate and interact with its content. This thesis is organized around two major contributions. We start with a large-scale characterization of the navigation patterns on Wikipedia in English, and then we introduce the tools we developed to conduct our analyses.In the first part, we shed light on the navigation patterns with three large-scale studies based on passively collected digital traces. Using billions of requests collected in Wikipedia's logs, we measure how readers reach articles, transition between pages, and leave the platform. We provide a complete overview of the readers' behavior by characterizing the frequent navigation dynamics and the level of engagement with different types of external links on the page. Then, given the observed role of Wikipedia as a gateway to the Web, we quantify the hypothetical economic value of the traffic received by external websites.In the second part, we present the tools that we developed to make our analysis possible and support future work in this field. First, we introduce WikiPDA, a cross-lingual topic modeling method able to generate a shared topics space for all editions of Wikipedia. Then, we present WikiHist.html, an effort to make publicly available the full Wikipedia history in HTML format. We conclude by discussing the implications of our findings and presenting future research opportunities enabled by our contributions.