A diachronic perspective on citation latency in Wikipedia articles on CRISPR/Cas-9: an exploratory case study

Abstract

This paper analyzes Wikipedia’s representation of the Nobel Prize winning CRISPR/Cas9 technology, a method for gene editing. We propose and evaluate different heuristics to match publications from several publication corpora against Wikipedia’s central article on CRISPR and against the complete Wikipedia revision history in order to retrieve further Wikipedia articles relevant to the topic and to analyze Wikipedia’s referencing patterns. We explore to what extent the selection of referenced literature of Wikipedia’s central article on CRISPR adheres to scientific standards and inner-scientific perspectives by assessing its overlap with (1) the Web of Science (WoS) database, (2) a WoS-based field-delineated corpus, (3) highly-cited publications within this corpus, and (4) publications referenced by field-specific reviews. We develop a diachronic perspective on citation latency and compare the delays with which publications are cited in relevant Wikipedia articles to the citation dynamics of these publications over time. Our results confirm that a combination of verbatim searches by title, DOI, and PMID is sufficient and cannot be improved significantly by more elaborate search heuristics. We show that Wikipedia references a substantial amount of publications that are recognized by experts and highly cited, but that Wikipedia also cites less visible literature, and, to a certain degree, even not strictly scientific literature. Delays in occurrence on Wikipedia compared to the publication years show (most pronounced in case of the central CRISPR article) a dependence on the dynamics of both the field and the editor’s reaction to it in terms of activity.

High Impact: Wikipedia sources and edit history document two decades of the climate change field

“…Together these comparative findings and the tools behind them show how researchers can use Wikipedia as a trove of open data and knowledge to enrich our understanding of the growth of knowledge and its representation online. Our findings are in line with previous works conducted on Wikipedia and climate change (17)(18)(19) as well as other scientific fields with similar methods (9,12). This work, which we associate as a form of "thick big data", helps underscore how computational and qualitative analyses are not just possible but lucrative, and help go beyond a divide that has long plagued research on Wikipedia.…”

“…From computer sciences (7) to the history of science (8,9), Wikipedia, its articles and the bibliometric data they contain, are increasingly becoming a lucrative and popular research topic. A growing body of research has tried to use Wikipedia for historical purposes, bringing mixed-methods from digital humanities (10), bibliometrics (11,12), textual analysis (13) and others from the disciplines of history and sociology of science and knowledge (14) to the online encyclopedia. These include studies on diverse topics such as the Egyptian revolution (15) and even attempts to map the entire history of human knowledge since the dawn of history (16).…”