Mapping the backbone of the Humanities through the eyes of Wikipedia

The present study aims to establish a valid method by which to apply the theory of co-citations to Wikipedia article references and, subsequently, to map these relationships between scientific papers. This theory, originally applied to scientific literature, will be transferred to the digital environment of collective knowledge generation. To this end, a dataset containing Wikipedia references collected from Altmetric and Scopus’ Journal Metrics journals has been used. The articles have been categorized according to the disciplines and specialties established in the All Science Journal Classification (ASJC). They have also been grouped by journal of publication. A set of articles in the Humanities, comprising 25 555 Wikipedia articles with 41 655 references to 32 245 resources, has been selected. Finally, a descriptive statistical study has been conducted and co-citations have been mapped using networks and indicators of degree and betweenness centralit

Where is the science in Wikipedia? Identification and characterization of scientifically supported contents

This study illustrates the challenges of developing a broad Wikipedia thematic landscape. Particularly the limitations of Wikipedia categories in providing an overview of the thematic areas covered in Wikipedia are shown. The use of WikiProjects is presented as a viable although limited alternative, providing interesting classificatory possibilities. The classification proposed here can be useful for further research on Wikipedia as well as for other researchers who want to identify Wikipedia dynamics in a more aggregated and visual way

Science through Wikipedia: A novel representation of open knowledge through co-citation networks

We thank Altmetric.com for the transfer of the data that has allowed us to conduct this studyThis study provides an overview of science from the Wikipedia perspective. A methodology has been established for the analysis of how Wikipedia editors regard science through their references to scientific papers. The method of co-citation has been adapted to this context in order to generate Pathfinder networks (PFNET) that highlight the most relevant scientific journals and categories, and their interactions in order to find out how scientific literature is consumed through this open encyclopaedia. In addition to this, their obsolescence has been studied through Price index. A total of 1 433 457 references available at Altmetric.com have been initially taken into account. After pre-processing and linking them to the data from Elsevier's CiteScore Metrics the sample was reduced to 847 512 references made by 193 802 Wikipedia articles to 598 746 scientific articles belonging to 14 149 journals indexed in Scopus. As highlighted results we found a significative presence of “Medicine” and “Biochemistry, Genetics and Molecular Biology” papers and that the most important journals are multidisciplinary in nature, suggesting also that high-impact factor journals were more likely to be cited. Furthermore, only 13.44% of Wikipedia citations are to Open Access journals

Identifying and characterizing social media communities: a socio‑semantic network approach to altmetrics

Funding for open access charge: Universidad de Granada/CBUA. This work has funded by the Spanish Ministry of Science and Innovation grant number PID2019-109127RB-I00/SRA/10.13039/501100011033. Wenceslao Arroyo-Machado has an FPU Grant (FPU18/05835) from the Spanish Ministry of Universities. Daniel Torres-Salinas is supported by the Reincorporation Programme for Young Researchers from the University of Granada. Nicolas Robinson-Garcia is funded by a Ramon y Cajal grant from the Spanish Ministry of Science and Innovation (REF: RYC2019-027886-I).Altmetric indicators allow exploring and profiling individuals who discuss and share scientific literature in social media. But it is still a challenge to identify and characterize communities based on the research topics in which they are interested as social and geographic proximity also influence interactions. This paper proposes a new method which profiles social media users based on their interest on research topics using altmetric data. Social media users are clustered based on the topics related to the research publications they share in social media. This allows removing linkages which respond to social or personal proximity and identifying disconnected users who may have similar research interests. We test this method for users tweeting publications from the fields of Information Science & Library Science, and Microbiology. We conclude by discussing the potential application of this method and how it can assist information professionals, policy managers and academics to understand and identify the main actors discussing research literature in social media.Spanish Government PID2019-109127RB-I00/SRA/10.13039/501100011033Spanish Ministry of Universities FPU18/05835Ramon y Cajal grant from the Spanish Ministry of Science and Innovation REF: RYC2019-027886-IUniversity of GranadaUniversidad de Granada/CBU

Wikipedia Citations: A comprehensive dataset of citations with identifiers extracted from English Wikipedia

Wikipedia's contents are 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 dataset of citations extracted from Wikipedia. A total of 29.3M citations were extracted from 6.1M English Wikipedia articles as of May 2020, and classified as being to books, journal articles or Web contents. We were thus able to extract 4.0M citations to scholarly publications with known identifiers -- including DOI, PMC, PMID, and ISBN -- and further equip an extra 261K 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 dataset in the future

Quantifying Engagement with Citations on Wikipedia

Wikipedia, the free online encyclopedia that anyone can edit, is one of the most visited sites on the Web and a common source of information for many users. As an encyclopedia, Wikipedia is not a source of original information, but was conceived 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 very 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' interaction with citations on Wikipedia. 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 open the door to a deeper understanding of Wikipedia's role in a global information economy where reliability is ever less certain, and source attribution ever more vital.Comment: The Web Conference WWW 2020, 10 page