Academic literature on the topic 'Wikipedia citations'

Abstract Wikipedia’s content is based on reliable and published sources. To this date, relatively little is known about what sources Wikipedia relies on, in part because extracting citations and identifying cited sources is challenging. To close this gap, we release Wikipedia Citations, a comprehensive data set of citations extracted from Wikipedia. We extracted29.3 million citations from 6.1 million English Wikipedia articles as of May 2020, and classified as being books, journal articles, or Web content. We were thus able to extract 4.0 million citations to scholarly publications with known identifiers—including DOI, PMC, PMID, and ISBN—and further equip an extra 261 thousand citations with DOIs from Crossref. As a result, we find that 6.7% of Wikipedia articles cite at least one journal article with an associated DOI, and that Wikipedia cites just 2% of all articles with a DOI currently indexed in the Web of Science. We release our code to allow the community to extend upon our work and update the data set in the future.

Wikipedia is one of the most visited sites on the Web and a common source of information for many users. As an encyclopedia, Wikipedia was not conceived as a source of original information, but as a gateway to secondary sources: according to Wikipedia’s guidelines, facts must be backed up by reliable sources that reflect the full spectrum of views on the topic. Although citations lie at the heart of Wikipedia, little is known about how users interact with them. To close this gap, we built client-side instrumentation for logging all interactions with links leading from English Wikipedia articles to cited references during one month, and conducted the first analysis of readers’ interactions with citations. We find that overall engagement with citations is low: about one in 300 page views results in a reference click (0,29% overall; 0,56% on desktop; 0,13% on mobile). Matched observational studies of the factors associated with reference clicking reveal that clicks occur more frequently on shorter pages and on pages of lower quality, suggesting that references are consulted more commonly when Wikipedia itself does not contain the information sought by the user. Moreover, we observe that recent content, open access sources, and references about life events (births, deaths, marriages, etc.) are particularly popular. Taken together, our findings deepen our understanding of Wikipedia’s role in a global information economy where reliability is ever less certain, and source attribution ever more vital.

Wikipedia is one of the main sources of free knowledge on the Web. During the first few months of the pandemic, over 5,200 new Wikipedia pages on COVID-19 were created, accumulating over 400 million page views by mid-June 2020. 1 At the same time, an unprecedented amount of scientific articles on COVID-19 and the ongoing pandemic have been published online. Wikipedia’s content is based on reliable sources, such as scientific literature. Given its public function, it is crucial for Wikipedia to rely on representative and reliable scientific results, especially in a time of crisis. We assess the coverage of COVID-19-related research in Wikipedia via citations to a corpus of over 160,000 articles. We find that Wikipedia editors are integrating new research at a fast pace, and have cited close to 2% of the COVID-19 literature under consideration. While doing so, they are able to provide a representative coverage of COVID-19-related research. We show that all the main topics discussed in this literature are proportionally represented from Wikipedia, after accounting for article-level effects. We further use regression analyses to model citations from Wikipedia and show that Wikipedia editors on average rely on literature that is highly cited, widely shared on social media, and peer-reviewed.

Purpose/Thesis: The paper aims to describe the types and structure of references to different sources as cited by the selected Polish Wikipedia articles from the category of people related to the Austrian Partition and all the categories below. Approach/Methods: The research data consisted of references from 50 randomly selected articles from Polish Wikipedia, including 1007 citations and 758 references. The references have been gathered, processed, and analyzed mainly employing R language. They have been categorized, and then the descriptive statistics for the chosen elements have been provided and analyzed. Results and conclusions: The study shows that the majority of sources used in the research sample were of primary nature. Consequently, it demonstrates that the analyzed articles about historical persons can be regarded more as a product of research than simple imitative work to a certain extent. Polish Wikipedians mainly utilized government directories and newspaper or magazine articles, often from digital libraries. Secondary sources, on the other hand, chiefly consisted of books, webpages, and book sections. The structure of references was diverse, and bibliographic descriptions sometimes lacked important elements. The findings confirm difficulties in analyzing sources in Wikipedia. Moreover, they support the need for researching different editions and subject areas of the largest online encyclopedia. Research limitations: Due to the exploratory character of research, which focuses on references from selected articles about historical persons from Poland, one should not readily extrapolate its results to other parts of Polish Wikipedia. The research sample only comprised citations and references, which were collected at one specific point of time. Additionally, the categorization of references has been done by a single researcher, and intercoder reliability has not been checked. Originality/Value: Most of the studies into sources used in Wikipedia articles have been limited to its English edition so far. Moreover, articles about historical persons in this encyclopedia have not been analyzed from the perspective of utilized sources, their types, and reference patterns. The paper broadens the understanding of sources usage in Wikipedia by focusing on the Polish edition of the encyclopedia.

Википедия является одним из самых посещаемых сайтов в интернете и распространённым источником информации для многих пользователей. В качестве энциклопедии Википедия задумывалась не как источник оригинальной (окончательной) научной информации, а, скорее, как ворота к более глубоким и точным источникам. В соответствии с базовыми принципами Википедии факты должны быть подкреплены надёжными источниками, которые отражают полный спектр всех мнений по данной теме. Хотя цитаты лежат в основе функционирования Википедии, пока мало что известно о том, как пользователи работают с ними. Чтобы закрыть этот пробел, мы создали клиентские (пользовательские) инструменты для ведения записей (журналов) всех взаимодействий со ссылками, идущими из англоязычных статей Википедии на цитируемые ссылки в течение одного месяца, и провели первый анализ взаимодействия читателей с цитатами.Результаты показывают, что в целом вовлечённость в цитаты низкая. Около 300 просмотров страниц приводят к входу на одну ссылку – это составляет всего 0,29%, в том числе 0,56% при работе с настольным компьютером (на рабочем столе) и 0,13% при работе на мобильных устройствах. Сопоставление факторов, связанных с переходами по ссылке, показывает, что переходы происходят чаще на более коротких страницах и на страницах относительно низкого качества. Исходя из этого можно предположить, что ссылки чаще всего требуются, когда Википедия не содержит информацию, которую ищет пользователь. Кроме того, мы обратили внимание, что источники открытого доступа и ссылки о жизненных событиях (рождения, смерти, браки и т.д.) особенно популярны.Собранные воедино, наши выводы углубляют понимание роли Википедии в глобальной информационной экономике, где надёжность становится всё менее определённой, а значение источников становится всё более важным.Справочный формат ACM для ссылок: Тициано Пиккарди, Мириам Реди, Джованни Колавицца и Роберт Вест. 2020.Количественная оценка взаимодействия с цитатами в Википедии. В трудах: Веб-конференция 2020 (WWW’20), 20–24 апр. 2020 г., Тайбэй, Тай-вань. ACM, Нью-Йорк, штат Нью-Йорк, США. 12 стр. https://doi.org/10.1145/3366423.3380300.

Objective: Wikipedia is commonly used to acquire information about various medical conditions such as chronic pain. Ideally, better online pain management content could reduce the burden of opioid use disorders. Our goal was to improve the quality of the content available on Wikipedia to make it more accurate and applicable to medical students and the general public while training medical students to practice evidence-based medicine and critically assess their sources of information. Methods: An elective class in Neuroscience, Pain, and Opioids composed of 10 medical students met biweekly to discuss landmark and practice-changing research articles in the fields of acute pain, chronic pain, and opioid management. The professor chose Wikipedia articles relevant to this course. Three independent viewers analyzed the quality of citations, anecdotal medical content, and content value for both patients and medical professionals. As part of their coursework, students then edited the Wikipedia articles. Results: Although some of the Wikipedia pain topic content (6.7% ± 2.0) was anecdotal, financially biased, or inconsistent with Western Medical Practice content, overall articles included primarily high-quality citations (85.6% ± 3.1). On a 0-5 Likert scale, students felt content would be moderately helpful for both medical students/professionals (3.4 ± 0.2) and laypersons (3.5 ± 0.2). Editing and adding citations was feasible, but novel material was often reverted. Conclusion: A significant amount of pain medicine content was relevant and amenable to student editing. Therefore, future use of this tactic could provide a unique opportunity to integrate evidence-based medicine into the medical curriculum and have a direct impact on the widely available medical information. Future refinement in the editorial process may also further improve online information.

Wikipedia supplements library resources with timely, useful information; acts as a tool for librarians answering reference questions; provides a pathway to primary and secondary sources and citations. Wikipedia helps library clients perform research; and provides a vital platform for recording local knowledge. Public libraries can expose both their staff and patrons to more complex and sophisticated strategies for dealing with the changing digital landscape. Wikipedia has the potential to point clients to libraries, where resources in their areas of interest can be found. Wikipedia-library engagement connects Wikipedia with the important role libraries play in bridging inequalities in access to information. Active collaborations between Wikipedia editors and academic and research libraries to advance the free flow of information which supporting sustainable development around the world. These collaborative opportunities offer a range of outcomes that could benefit academic and research libraries including crowdsourcing, community engagement, rapid models for online publishing, student writing assignments, etc. For the Russian National Public Library for Science and Technology both projects are equally of interest. (http://2016.ifla.org/wp-content/uploads/sites/2/2016/08/112-IFLAWikipediaAcademicandResearchLibrariesDiscussioDRAFT.pdf)

Several libraries and Wikipedia agreed on developing cooperation. This initiative has been translated into two Public Projects presented at Session 112. Library engagement and Wikipedia during IFLA General Conference (August 13-19, Columbus, USA). Wikipedia supplements library resources with timely, useful information; acts as a tool for librarians answering reference questions; provides a pathway to primary and secondary sources and citations. Wikipedia helps library clients perform research; and provides a vital platform for recording local knowledge. Public libraries can expose both their staff and patrons to more complex and sophisticated strategies for dealing with the changing digital landscape. Wikipedia has the potential to point clients to libraries, where resources in their areas of interest can be found. Wikipedia-library engagement connects Wikipedia with the important role libraries play in bridging inequalities in access to information. Active collaborations between Wikipedia editors and academic and research libraries to advance the free flow of information which supporting sustainable development around the world. These collaborative opportunities offer a range of outcomes that could benefit academic and research libraries including crowdsourcing, community engagement, rapid models for online publishing, student writing assignments, etc. For the Russian National Public Library for Science and Technology both projects are equally of interest.

Although citation counts are widely used to support research evaluation, they can only reflect academic impacts, whereas research can also be useful outside academia. There is therefore a need for alternative indicators and empirical studies to evaluate them. Whilst many previous studies have investigated alternative indicators for journal articles and books, this thesis explores the importance and suitability of four web indicators for conference papers. These are readership counts from the online reference manager Mendeley and citation counts from Google Patents, Wikipedia and Google Books. To help evaluate these indicators for conference papers, correlations with Scopus citations were evaluated for each alternative indicator and compared with corresponding correlations between alternative indicators and citation counts for journal articles. Four subject areas that value conferences were chosen for the analysis: Computer Science Applications; Computer Software Engineering; Building & Construction Engineering; and Industrial & Manufacturing Engineering. There were moderate correlations between Mendeley readership counts and Scopus citation counts for both journal articles and conference papers in Computer Science Applications and Computer Software. For conference papers in Building & Construction Engineering and Industrial & Manufacturing Engineering, the correlations between Mendeley readers and citation counts are much lower than for journal articles. Thus, in fields where conferences are important, Mendeley readership counts are reasonable impact indicators for conference papers although they are better impact indicators for journal articles. Google Patent citations had low positive correlations with citation counts for both conference papers and journal articles in Software Engineering and Computer Science Applications. There were negative correlations for both conference papers and journal articles in Industrial and Manufacturing Engineering. However, conference papers in Building and Construction Engineering attracted no Google Patent citations. This suggests that there are disciplinary differences but little overall value for Google Patent citations as impact indicators in engineering fields valuing conferences. Wikipedia citations had correlations with Scopus citations that were statistically significantly positive only in Computer Science Applications, whereas the correlations were not statistically significantly different from zero in Building & Construction Engineering, Industrial & Manufacturing Engineering and Software Engineering. Conference papers were less likely to be cited in Wikipedia than journal articles were in all fields, although the difference was minor in Software Engineering. Thus, Wikipedia citations seem to have little value in engineering fields valuing conferences. Google Books citations had positive significant correlations with Scopus-indexed citations for conference papers in all fields except Building & Construction Engineering, where the correlations were not statistically significantly different from zero. Google Books citations seemed to be most valuable impact indicators in Computer Science Applications and Software Engineering, where the correlations were moderate, than in Industrial & Manufacturing Engineering, where the correlations were low. This means that Google Book citations are valuable indicators for conference papers in engineering fields valuing conferences. Although evidence from correlation tests alone is insufficient to judge the value of alternative indicators, the results suggest that Mendeley readers and Google Books citations may be useful for both journal articles and conference papers in engineering fields that value conferences, but not Wikipedia citations or Google Patent citations.

Altmetrics is a movement that aims to capture new and previously invisible types of impact of scholarly publications on social web platforms such as news sites, Wikipedia, blogs, microblogs, social bookmarking tools and online reference managers. This thesis aimtries to explore the suitability and reliability of two altmetrics resources: Mendeley, a social reference manager website, and Wikipedia, a free online encyclopedia, written collaboratively by the people who use it. At present, Wikipedia is the largest online encyclopedia. The main objectives of this thesis are to explore the use of reference management software and Wikipedia to estimate the readership of academic literature and to compare the evolution in the coverage of LIS literature by altmetric sources and by citation indexes. To reach these objectives, three different studies were designed. The general method to achive toobtain the initial data set was to search is that we used the Social Sciences Citation Index to retrieve articles and reviews published and indexed in the category “Information Science & Library Science”. Each record was then searched in Mendeley to obtain the number of bookmarks of the paper and the academic status of the users. Additonally, we performed a fifteen-month longitudinal study of the evolution of bookmarks in Mendeley. Afterwards, we searched for each of these articles in Wikipedia, and retrieved all the entries in which they were cited. The Results results of these studies show that the correlation between bookmarks and citations was moderate and the overlap between the most frequently bookmarked and the most frequently cited papers increased over time. A significant share of the bookmarks were made by students and professionals, although the shares of bookmarks made by different categories of users changed as time went by. Reviews were bookmarked more frequently than articles, and papers in English had more bookmarks than papers in any other language. Also results reveal severe limitations in the use of Wikipedia citations for research evaluation. Lack of standardization and incompleteness of Wikipedia references make it difficult to retrieve them. The number of Wikipedia citations is very low, A significant number of references are cited in biographical entries about the authors of the articles, resulting in a phenomenon of accumulated advantage. Nearly one-third of the Wikipedia citations link to an open access source, although this result is probably an underestimate of open access availability, given the incompleteness of Wikipedia citations.
Altmetrics es un movimiento que tiene como objetivo capturar nuevos tipos de impacto, antes invisibles, de las publicaciones académicas en las plataformas de la web social, tales como sitios de noticias, Wikiped ia, blogs, microblogs, herramientas de marcadores sociales y gestores de referencias en línea. Esta tesis pretende explorar la pertinencia y fiabilidad de dos recursos altmétricos: Mendeley, un sitio web de gestión de referencia social, y Wikipedia, una enciclopedia online gratuita escrita en colaboración por las personas que la utilizan. Actualmente, Wikipedia es la mayor enciclopedia en línea. Los principales objetivos de esta tesis son investigar el uso del software de gestión de referencia y de Wikipedia para estimar el número de lectores de literatura académica y comparar la evolución en la cobertura de la literatura de Ciencias de la Información y Biblioteconomía (Library and Information Science, por sus siglas en inglés), según fuentes altmétricas y por índices de citas. Para alcanzar estos objetivos, se diseñaron tres estudios diferentes. El método general para acceder al conjunto de datos consistía en utilizar el Social Sciences Citation Index para recuperar revisiones y artículos publicados e indexados en la categoría «Information Science & Library Science». Se buscó cada registro en Mendeley para obtener el número de marcadores del documento y el estatus académico de los usuarios. De manera adicional, realizamos un estudio longitudinal durante quince meses para estudiar la evoluci ón en el número de marcadores. Posteriormente, buscamos cada uno de estos artículos en Wikipedia y recuperamos todas las entradas en las que fueron citados. Los resultados de estos estudios muestran que la correlación entre los marcadores y las citas era moderada y la coincidencia entre los documentos guardados en marcadores con más frecuencia y los más citados aumentó con el tiempo. Una parte significativa de los marcadores los habían hecho estudiantes y profesionales, aunque el número de marcadores hechos por diferentes categorías de usuarios cambiaron con el paso del tiempo. Las revisiones se guardaban en marcadores con más frecuencia que los artículos, y los documentos en inglés tenían más marcadores que los documentos en cualquier otro idioma. Los resultados también revelan severas limitaciones en el uso de citas de Wikipedia para la evaluación de investigación. La falta de estandarización y el carácter incompleto de las referencias de Wikipedia dificultan su recuperación. El número de citas de Wikipedia es muy bajo, y un número significativo de referencias se citan en las entradas biográficas dedicadas a los autores de los artículos, lo que supone una ventaja acumulada. Cerca de un tercio de las citas de Wikipedia se vinculan a una fuente de acceso abierto, aunque probablemente este resultado sea una subestimación de la disponibilidad de acceso a estas fuentes, dado que las citas de Wikipedia están incompletas.

This thesis is called “Citation needed – the perceived credibility of Wikipedia among higheducation students”. The purpose of this thesis is to discover the opinions about the credibilityof Wikipedia among high education students. The assumption is that students are aware ofbeing source criticizing and do not directly cite Wikipedia in academic works. The main research question is: what are the views of the perceived credibility of theinformation on Wikipedia among high education students? The theoretical framework is made out of previous research specific related to Wikipediabut also theories around memory creation, assessment of sources and credibility. The method used is qualitative, with semi-structured interviews. The material consists ofinterviews with nine participants. Their answers are transcribed and presented in themes andthen analyzed. The main results are that the perceived credibility of the information on Wikipedia amongthe students is that it is fairly credible and their views of the encyclopedia are that it is fastupdated and neutral. They also believe that Wikipedia isn’t for academic usage so in allaspects the students do not make a thoroughly assessment of the encyclopedia’s credibility. Further implications are that Wikipedia would be ruined if anyone tried to make itacademic. Wikipedia fulfills an educational tool for the public even though it is not alwayscorrect. It is important to be reminded that Wikipedia contains errors but no other source iscompletely flawless either. In that sense it would be smarter to start a new encyclopedia, ifthere is such a need, perhaps open for professors only and would in the end only containvetted information, however mostly/only in academic fields. The length of the thesis is 56 pages in total and approximately 21 000 words. Keywords: Wikipedia, encyclopedias, source criticism, credibility, Nationalencyclopedin,students, in-depth interviews.

As a public and open-source resource, Wikipedia is used by many in the public as a quick reference; academic researchers have tapped Wikipedia for human- and machine-based insights. The MediaWiki understructure enables a wide range of transparency, enabling users to very easily search and access template-structured text and image contents, source citations, contributors, page histories, and others. Transparency has been hard-wired into the platform technology. Developers have been building tools to extend the transparency of data built on a MediaWiki understructure. Network Overview, Discovery and Exploration for Excel (NodeXL) features a third-party graph data importer that enables the extraction of MediaWiki article (and user) networks, which include all of the languages of Wikipedias. This chapter highlights the uses of graphs from two main types of Wikipedia pages for increased knowledge transparency: (1) topical article pages and (2) contributor user pages (whether human or robot).

Although academic skepticism of Wikipedia’s value as an information resource is widespread, the collaboratively created online encyclopedia is in fact one of the most frequently used health information resources in the world, including among students and professionals. As a result, the U.S.-based Network of the National Library of Medicine (NNLM) has, since 2018, organized biannual “#CiteNLM” edit-a-thons aimed at strengthening Wikipedia’s health pages by adding content and citations to trusted sources of information. The first #CiteNLM edit-a-thon was a one-day virtual event in April 2018; since then NNLM’s edit-a-thons have evolved into month-long campaigns engaging primarily academic libraries with in-person edit-a-thons as well as virtual events. Hundreds of students, faculty, and library staff across the country (many of whom were new to Wikipedia editing) have collaborated in NNLM’s efforts to support universal access to high-quality health information. To date, over 600 health articles have been edited by over 400 editors. The current #CiteNLM campaign structure makes it easy for either individuals or groups to contribute or host affiliated events, which can include classroom exercises, citizen science projects, or library engagement efforts.

Since 2007, the University Libraries at the University of Nevada, Las Vegas, has had a student employee peer learning program composed of six to seven undergraduate students. The Mason Undergraduate Peer Research Coaches, known as peer coaches, work within the instruction and outreach department co-teaching library instruction sessions and connecting with students through cocurricular outreach activities. When three librarians decided to plan their first Wikipedia edit-a-thon in 2017, the peer coaches became their collaborators. Since then, the peer coaches have developed lists of resources, identified notable individuals, evaluated Wikipedia pages, and worked with students during the event at orientation, citation, information, creation, and translation stations. They have also engaged in extra projects like creating playlists, designing swag, developing a trivia game, and pop-up tabling. Because of the collaboration with the peer coaches, the edit-a-thons have developed and grown far beyond initial expectations. In this chapter, we will share the background and institutional context for our university and Wikipedia program; detail the collaborative efforts of library faculty, staff, and peer coaches at each stage; and share reflections and recommendations from the peer coaches themselves.

We propose a scalable neural language model that leverages the links between documents to learn the deep context of documents. Our model, Deep Context Vector, takes advantage of distributed representations to exploit the word order in document sentences, as well as the semantic connections among linked documents in a document network. We evaluate our model on large-scale data collections that include Wikipedia pages, and scientific and legal citations networks. We demonstrate its effectiveness and efficiency on document classification and link prediction tasks.