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Wikipedia serves as a globally accessible knowledge source with content in over 300 languages. Despite covering the same topics, the different versions of Wikipedia are written and updated independently. This leads to factual inconsistencies that can impact the neutrality and reliability of the encyclopedia and AI systems, which often rely on Wikipedia as a main training source. This study investigates cross-lingual inconsistencies in Wikipedia's structured content, with a focus on tabular data. We developed a methodology to collect, align, and analyze tables from Wikipedia multilingual articles, defining categories of inconsistency. We apply various quantitative and qualitative metrics to assess multilingual alignment using a sample dataset. These insights have implications for factual verification, multilingual knowledge interaction, and design for reliable AI systems leveraging Wikipedia content.

Wikipedia is an essential component of the open science ecosystem, yet it is poorly integrated with academic open science initiatives. Wikipedia Citations is a project that focuses on extracting and releasing comprehensive datasets of citations from Wikipedia. A total of 29.3 million citations were extracted from English Wikipedia in May 2020. Following this one-off research project, we designed a reproducible pipeline that can process any given Wikipedia dump in the cloud-based settings. To demonstrate its usability, we extracted 40.6 million citations in February 2023 and 44.7 million citations in February 2024. Furthermore, we equipped the pipeline with an adapted Wikipedia citation template translation module to process multilingual Wikipedia articles in 15 European languages so that they are parsed and mapped into a generic structured citation template. This paper presents our open-source software pipeline to retrieve, classify, and disambiguate citations on demand from a given Wikipedia dump.

Writing Wikipedia with a neutral point of view is one of the five pillars of Wikipedia. Although the topic is core to Wikipedia, it is relatively understudied considering hundreds of research studies are published annually about the project. We hypothesize that part of the reason for the low research activity on the topic is that Wikipedia's definition of neutrality and its importance are not well understood within the research community. Neutrality is also an inherently challenging and contested concept. Our aim with this paper is to accelerate high quality research in this space that can help Wikipedia communities continue to improve their work in writing the encyclopedia. We do this by helping researchers to learn what Neutral Point of View means in the context of Wikipedia, identifying some common challenges with studying NPOV and how to navigate them, and offering guidance on how researchers can communicate the results of their work for increased impact on the ground for the benefit of Wikipedia.

In this paper, we present a comprehensive analysis and monitoring framework for the impact of Large Language Models (LLMs) on Wikipedia, examining the evolution of Wikipedia through existing data and using simulations to explore potential risks. We begin by analyzing article content and page views to study the recent changes in Wikipedia and assess the impact of LLMs. Subsequently, we evaluate how LLMs affect various Natural Language Processing (NLP) tasks related to Wikipedia, including machine translation and retrieval-augmented generation (RAG). Our findings and simulation results reveal that Wikipedia articles have been affected by LLMs, with an impact of approximately 1% in certain categories. If the machine translation benchmark based on Wikipedia is influenced by LLMs, the scores of the models may become inflated, and the comparative results among models could shift. Moreover, the effectiveness of RAG might decrease if the knowledge has been contaminated by LLMs. While LLMs have not yet fully changed Wikipedia's language and knowledge structures, we believe that our empirical findings signal the need for careful consideration of potential future risks in NLP research. We rel

The Wikipedia editors' community has been actively pursuing the intent of achieving gender equality. To that end, it is important to explore the historical evolution of underlying gender disparities in Wikipedia articles. This paper presents the Wikipedia Gender Dashboard (WGD), a tool designed to enable the interaction with gender distribution data, including the average age in every subclass of individuals (i.e. Astronauts, Politicians, etc.) over the years. Wikipedia APIs, DBpedia, and Wikidata endpoints were used to query the data to ensure persistent data collection. The WGD was then created with Microsoft Power BI before being embedded on a public website. The analysis of the data available in the WGD found that female articles only represent around 17% of English Wikipedia, but it has been growing steadily over the last 20 years. Meanwhile, the average age across genders decreased over time. WGD also shows that most subclasses of `Person' are male-dominated. Wikipedia editors can make use of WGD to locate areas with marginalized genders in Wikipedia, and increase their efforts to produce more content providing coverage for those genders to achieve better gender equality in W

Wikipedia, a widely successful encyclopedia recognized in academic circles and used by both students and professors alike, has led educators to question whether it can be cited as an information source, given its widespread use for this very purpose. The dilemma quickly emerged: if Wikipedia has become the go-to information source for so many, why can't it be cited? If consulting and using Wikipedia as a source of information is permitted, why does it become controversial the moment one attempts to cite it? This manuscript examines the systematic rejection of Wikipedia in academic settings, not to argue for its legitimacy as a source, but to demonstrate that its reliability is often underestimated while traditional academic sources enjoy disproportionate credibility, despite their increasingly apparent shortcomings. The central thesis posits that Wikipedia's rejection stems from an outdated epistemological bias that overlooks both the project's verification mechanisms and the structural crises affecting scientific publishing.

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.

The use of Wikipedia citations in scholarly research has been the topic of much inquiry over the past decade. A cross-publisher study (Taylor & Francis and University of Michigan Press) convened by Digital Science was established in late 2022 to explore author sentiment towards Wikipedia as a trusted source of information. A short survey was designed to poll published authors about views and uses of Wikipedia and explore how the increased addition of research citations in Wikipedia might help combat misinformation in the context of increasing public engagement with and access to validated research sources. With 21,854 surveys sent, targeting 40,402 papers mentioned in Wikipedia, a total of 750 complete surveys from 60 countries were included in this analysis. In general, responses revealed a positive sentiment towards research citation in Wikipedia and the researcher engagement practices. However, our sub analysis revealed statistically significant differences when comparison articles vs books and across disciplines, but not open vs closed access. This study will open the door to further research and deepen our understanding of authors perceived trustworthiness of the represent

Recent research has shown how strongly Wikipedia and other web services or platforms are connected. For example, search engines rely heavily on surfacing Wikipedia links to satisfy their users' information needs and volunteer-created Wikipedia content frequently gets re-used on other social media platforms like Reddit. However, publicly accessible datasets that enable researchers to study the interrelationship between Wikipedia and other platforms are sparse. In addition to that, most studies only focus on certain points in time and don't consider the historical perspective. To begin solving these problems we developed TWikiL, the Twitter Wikipedia Link Dataset, which contains all Wikipedia links posted on Twitter in the period 2006 to January 2021. We extract Wikipedia links from Tweets and enrich the referenced articles with their respective Wikidata identifiers and Wikipedia topic categories, which will make this dataset immediately useful for a large range of scholarly use cases. In this paper, we describe the data collection process, perform an initial exploratory analysis and present a comprehensive overview of how this dataset can be useful for the research community.

Gender imbalance in Wikipedia content is a known challenge which the editor community is actively addressing. The aim of this paper is to provide the Wikipedia community with instruments to estimate the magnitude of the problem for different entity types (also known as classes) in Wikipedia. To this end, we apply class completeness estimation methods based on the gender attribute. Our results show not only which gender for different sub-classes of Person is more prevalent in Wikipedia, but also an idea of how complete the coverage is for difference genders and sub-classes of Person.

We study text reuse related to Wikipedia at scale by compiling the first corpus of text reuse cases within Wikipedia as well as without (i.e., reuse of Wikipedia text in a sample of the Common Crawl). To discover reuse beyond verbatim copy and paste, we employ state-of-the-art text reuse detection technology, scaling it for the first time to process the entire Wikipedia as part of a distributed retrieval pipeline. We further report on a pilot analysis of the 100 million reuse cases inside, and the 1.6 million reuse cases outside Wikipedia that we discovered. Text reuse inside Wikipedia gives rise to new tasks such as article template induction, fixing quality flaws due to inconsistencies arising from asynchronous editing of reused passages, or complementing Wikipedia's ontology. Text reuse outside Wikipedia yields a tangible metric for the emerging field of quantifying Wikipedia's influence on the web. To foster future research into these tasks, and for reproducibility's sake, the Wikipedia text reuse corpus and the retrieval pipeline are made freely available.

This paper presents an automated adversarial mechanism called WikipediaBot. WikipediaBot allows an adversary to create and control a bot infrastructure for the purpose of adversarial edits of Wikipedia articles. The WikipediaBot is a self-contained mechanism with modules for generating credentials for Wikipedia editors, bypassing login protections, and a production of contextually-relevant adversarial edits for target Wikipedia articles that evade conventional detection. The contextually-relevant adversarial edits are generated using an adversarial Markov chain that incorporates a linguistic manipulation attack known as MIM or malware-induced misperceptions. Because the nefarious use of WikipediaBot could result in harmful damages to the integrity of wide range of Wikipedia articles, we provide an elaborate discussion about the implications, detection, and defenses Wikipedia could employ to address the threat of automated adversarial manipulations and acts of Wikipedia vandalism.

Wikipedia is the largest online encyclopedia: its open contribution policy allows everyone to edit and share their knowledge. A challenge of radical openness is that it facilitates introducing biased contents or perspectives in Wikipedia. Wikipedia relies on numerous external sources such as journal articles, books, news media, and more. News media sources, in particular, take up nearly third of all citations from Wikipedia. However, despite their importance for providing up-to-date and factual contents, there is still a limited understanding on which news media sources are cited from Wikipedia. Relying on a large-scale open dataset of nearly 30M citations from English Wikipedia, we find a moderate yet systematic liberal polarization in the selection of news media sources. We also show that this effect is not mitigated by controlling for news media factual reliability. Our results contribute to Wikipedia's knowledge integrity agenda in suggesting that a systematic effort would help to better map potential biases in Wikipedia and find means to strengthen its neutral point of view policy.

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 accessibility of the content. One crucial aspect of accessibility is the integration of hyperlinks into the network so the articles are visible to readers navigating Wikipedia. In order 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 of their visibility in terms of the number of pageviews. We further highlight the challenges faced by editors for de-orphanizing

Over the past 20 years, Wikipedia has gone from a rather outlandish idea to a major reference work, with more than 60 million articles across all languages, including nearly 7 million in English [Wiki01]. Around 27,000 of these articles concern mathematics [b], and Wikipedia is the first place that many of us go to learn about a new mathematical idea. In this overview, we will discuss how to go about creating or editing an article on a mathematical subject. (Most of this applies equally to topics from other technical fields.) We will also discuss biographies of mathematicians, articles on mathematical books, and the social dynamics of the Wikipedia editor community.

Wikipedia is one of the most popular sites on the Web, with millions of users relying on it to satisfy a broad range of information needs every day. Although it is crucial to understand what exactly these needs are in order to be able to meet them, little is currently known about why users visit Wikipedia. The goal of this paper is to fill this gap by combining a survey of Wikipedia readers with a log-based analysis of user activity. Based on an initial series of user surveys, we build a taxonomy of Wikipedia use cases along several dimensions, capturing users' motivations to visit Wikipedia, the depth of knowledge they are seeking, and their knowledge of the topic of interest prior to visiting Wikipedia. Then, we quantify the prevalence of these use cases via a large-scale user survey conducted on live Wikipedia with almost 30,000 responses. Our analyses highlight the variety of factors driving users to Wikipedia, such as current events, media coverage of a topic, personal curiosity, work or school assignments, or boredom. Finally, we match survey responses to the respondents' digital traces in Wikipedia's server logs, enabling the discovery of behavioral patterns associated with

In recent decades, the rapid growth of Internet adoption is offering opportunities for convenient and inexpensive access to scientific information. Wikipedia, one of the largest encyclopedias worldwide, has become a reference in this respect, and has attracted widespread attention from scholars. However, a clear understanding of the scientific sources underpinning Wikipedia's contents remains elusive. In this work, we rely on an open dataset of citations from Wikipedia to map the relationship between Wikipedia articles and scientific journal articles. We find that most journal articles cited from Wikipedia belong to STEM fields, in particular biology and medicine ($47.6$\% of citations; $46.1$\% of cited articles). Furthermore, Wikipedia's biographies play an important role in connecting STEM fields with the humanities, especially history. These results contribute to our understanding of Wikipedia's reliance on scientific sources, and its role as knowledge broker to the public.

Verifiability is a core content policy of Wikipedia: claims that are likely to be challenged need to be backed by citations. There are millions of articles available online and thousands of new articles are released each month. For this reason, finding relevant sources is a difficult task: many claims do not have any references that support them. Furthermore, even existing citations might not support a given claim or become obsolete once the original source is updated or deleted. Hence, maintaining and improving the quality of Wikipedia references is an important challenge and there is a pressing need for better tools to assist humans in this effort. Here, we show that the process of improving references can be tackled with the help of artificial intelligence (AI). We develop a neural network based system, called Side, to identify Wikipedia citations that are unlikely to support their claims, and subsequently recommend better ones from the web. We train this model on existing Wikipedia references, therefore learning from the contributions and combined wisdom of thousands of Wikipedia editors. Using crowd-sourcing, we observe that for the top 10% most likely citations to be tagged a

Wikipedia, rich in entities and events, is an invaluable resource for various knowledge harvesting, extraction and mining tasks. Numerous resources like DBpedia, YAGO and other knowledge bases are based on extracting entity and event based knowledge from it. Online news, on the other hand, is an authoritative and rich source for emerging entities, events and facts relating to existing entities. In this work, we study the creation of entities in Wikipedia with respect to news by studying how entity and event based information flows from news to Wikipedia. We analyze the lag of Wikipedia (based on the revision history of the English Wikipedia) with 20 years of \emph{The New York Times} dataset (NYT). We model and analyze the lag of entities and events, namely their first appearance in Wikipedia and in NYT, respectively. In our extensive experimental analysis, we find that almost 20\% of the external references in entity pages are news articles encoding the importance of news to Wikipedia. Second, we observe that the entity-based lag follows a normal distribution with a high standard deviation, whereas the lag for news-based events is typically very low. Finally, we find that events a