Wikimedia Foundation

Intrinsically motivated information seeking is an expression of curiosity believed to be central to human nature. However, most curiosity research relies on small, Western convenience samples. Here, we analyze a naturalistic population of 482,760 readers using Wikipedia’s mobile app in 14 languages from 50 countries or territories. By measuring the structure of knowledge networks constructed by readers weaving a thread through articles in Wikipedia, we replicate two styles of curiosity previously identified in laboratory studies: the nomadic “busybody” and the targeted “hunter.” Further, we find evidence for another style—the “dancer”—which was previously predicted by a historico-philosophical examination of texts over two millennia and is characterized by creative modes of knowledge production. We identify associations, globally, between the structure of knowledge networks and population-level indicators of spatial navigation, education, mood, well-being, and inequality. These results advance our understanding of Wikipedia’s global readership and demonstrate how cultural and geographical properties of the digital environment relate to different styles of curiosity.

With 60M articles in more than 300 language versions, Wikipedia is the largest platform for open and freely accessible knowledge. While the available content has been growing continuously at a rate of around 200K new articles each month, very little attention has been paid to the discoverability of the content. One crucial aspect of discoverability is the integration of hyperlinks into the network so the articles are visible to readers navigating Wikipedia. To understand this phenomenon, we conduct the first systematic study of orphan articles, which are articles without any incoming links from other Wikipedia articles, across 319 different language versions of Wikipedia. We find that a surprisingly large extent of content, roughly 15% (8.8M) of all articles, is de facto invisible to readers navigating Wikipedia, and thus, rightfully term orphan articles as the dark matter of Wikipedia. We also provide causal evidence through a quasi-experiment that adding new incoming links to orphans (de-orphanization) leads to a statistically significant increase in their visibility in terms of the number of pageviews. We further highlight the challenges faced by editors for de-orphanizing articles, demonstrate the need to support them in addressing this issue, and provide potential solutions for developing automated tools based on cross-lingual approaches. Overall, our work not only unravels a key limitation in the link structure of Wikipedia and quantitatively assesses its impact but also provides a new perspective on the challenges of maintenance associated with content creation at scale in Wikipedia.

Wikipedia, in its role as the world's largest encyclopedia, serves a broad range of information needs. Although previous studies have noted that Wikipedia users' information needs vary throughout the day, there is to date no large-scale, quantitative study of the underlying dynamics. The present paper fills this gap by investigating temporal regularities in daily consumption patterns in a large-scale analysis of billions of timezone-corrected page requests mined from English Wikipedia's server logs, with the goal of investigating how context and time relate to the kind of information consumed. First, we show that even after removing the global pattern of day-night alternation, the consumption habits of individual articles maintain strong diurnal regularities. Then, we characterize the prototypical shapes of consumption patterns, finding a particularly strong distinction between articles preferred during the evening/night and articles preferred during working hours. Finally, we investigate topical and contextual correlates of Wikipedia articles' access rhythms, finding that article topic, reader country, and access device (mobile vs. desktop) are all important predictors of daily attention patterns. These findings shed new light on how humans seek information on the Web by focusing on Wikipedia as one of the largest open platforms for knowledge and learning, emphasizing Wikipedia's role as a rich knowledge base that fulfills information needs spread throughout the day, with implications for understanding information seeking across the globe and for designing appropriate information systems.

With over 60M articles, Wikipedia has become the largest platform for open and freely accessible knowledge.While it has more than 15B monthly visits, its content is believed to be inaccessible to many readers due to the lack of readability of its text.However, previous investigations of the readability of Wikipedia have been restricted to English only, and there are currently no systems supporting the automatic readability assessment of the 300+ languages in Wikipedia.To bridge this gap, we develop a multilingual model to score the readability of Wikipedia articles.To train and evaluate this model, we create a novel multilingual dataset spanning 14 languages, by matching articles from Wikipedia to simplified Wikipedia and online children encyclopedias.We show that our model performs well in a zero-shot scenario, yielding a ranking accuracy of more than 80% across 14 languages and improving upon previous benchmarks.These results demonstrate the applicability of the model at scale for languages in which there is no ground-truth data available for model fine-tuning.Furthermore, we provide the first overview on the state of readability in Wikipedia beyond English.

Wikidata, now a decade old, is the largest public knowledge graph, with data on more than 100 million concepts contributed by over 560,000 editors. It is widely used in applications and research. At its launch in late 2012, however, it was little more than a hopeful new Wikimedia project, with no content, almost no community, and a severely restricted platform. Seven years earlier still, in 2005, it was merely a rough idea of a few PhD students, a conceptual nucleus that had yet to pick up many important infuences from others to turn into what is now called Wikidata. In this paper, we try to recount this remarkable journey, and we review what has been accomplished, what has been given up on, and what is yet left to do for the future.

Sections are the building blocks of Wikipedia articles. They are used by editors to create a structure for the content of articles, which in turn improves reading and editing workflows. Today, millions of carefully curated section titles exist in more than 160 actively edited Wikipedia languages as standalone components of a larger system. Understanding the connection and correspondence of section titles across languages presents various application opportunities such as article template recommendation, i.e., given a source language article, we can generate a skeleton of section titles for a target language. Inspired by this real-world data mining problem, the present paper introduces the problem of aligning section titles across Wikipedia languages and proposes a probabilistic method for identifying such correspondences. Instead of applying translation tools to section titles (which may generate out-of lexicon titles), we develop a supervised model that identifies cross-language mappings based on section content features. We collected a ground-truth dataset created for this purpose with the help of volunteers. In addition, we use Probabilistic Soft Logic to model the dependencies between multilingual section pairings. We show that our approach performs better than machine translation solutions in about 80% of the language pairs, including distant language mappings such as Arabic to Russian or French to Japanese and in many of the more closely related languages such as French to Spanish.

The success of Wikipedia and other user-generated content communities has been driven by the openness of recruiting volunteers globally, but this openness has also led to a persistent lack of trust in its content. Despite several attempts at developing trust indicators to help readers more quickly and accurately assess the quality of

La Viquipèdia en català és una exitosa plataforma de coneixement lliure amb una forta comunitat d'editors que ha contribuït significativament a normalitzar aquesta llengua minoritzada a Internet. El 2012 l'Associació Amical Wikimedia i el Servei de Biblioteques Públiques del Departament de Cultura de la Generalitat de Catalunya impulsà #Bibliowikis, una iniciativa que ha implicat diversos centenars de bibliotecaris i biblioteques públiques en la millora d'aquesta versió de l'enciclopèdia en línia. Aquest model únic i reeixit es va presentar a l'Organització de les Nacions Unides per a l'Educació, la Ciència i la Cultura (UNESCO) i ha estat destacat com un cas pràctic per la Federació Internacional d'Associacions i Institucions de Biblioteques (IFLA). El projecte consisteix en l'anomenat model Amical, en què la formació en cascada, l'autogestió a llarg termini i la xarxa de voluntaris viquipedistes distribuïts geogràficament constitueixen els tres pilars fonamentals de treball. L'èxit de #Bibliowikis ha experimentat diferents nivells d'aplicabilitat i regularitat als territoris de parla catalana –especialment al País Valencià i a Andorra– i corre perill per les dinàmiques en línia globals, la pèrdua dels principis de voluntariat i la priorització de recursos econòmics a l'entorn de canvi constant del moviment Wikimedia. Malgrat això, les característiques de #Bibliowikis estan totalment alineades amb les de l'accés obert, de manera que es combina fàcilment amb el món acadèmic i la transferència de coneixement públic a les universitats. El seu robust discurs ètic sobre contribució social, infraestructura negligible i protecció del patrimoni lingüístic ha permès implementar #Bibliowikis en el context creixent del teletreball i de la necessitat de digitalització social dels dipòsits bibliogràfics.

In this paper, we present a dataset of inter-language knowledge propagation in Wikipedia. Covering the entire 309 language editions and 33M articles, the dataset aims to track the full propagation history of Wikipedia concepts, and allow follow-up research on building predictive models of them. For this purpose, we align all the Wikipedia articles in a language-agnostic manner according to the concept they cover, which results in 13M propagation instances. To the best of our knowledge, this dataset is the first to explore the full inter-language propagation at a large scale. Together with the dataset, a holistic overview of the propagation and key insights about the underlying structural factors are provided to aid future research. For example, we find that although long cascades are unusual, the propagation tends to continue further once it reaches more than four language editions. We also find that the size of language editions is associated with the speed of propagation. We believe the dataset not only contributes to the prior literature on Wikipedia growth but also enables new use cases such as edit recommendation for addressing knowledge gaps, detection of disinformation, and cultural relationship analysis.