{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:35:04Z","timestamp":1770741304283,"version":"3.49.0"},"reference-count":56,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,5,20]],"date-time":"2020-05-20T00:00:00Z","timestamp":1589932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Twitter has been one of the most popular social network sites for academic research; the main objective of this study was to update the current knowledge boundary surrounding Twitter-related investigations and, further, identify the major research topics and analyze their evolution across time. A bibliometric analysis has been applied in this article: we retrieved 19,205 Twitter-related academic articles from Web of Science after several steps of data cleaning and preparation. The R package \u201cBibliometrix\u201d was mainly used in analyzing this content. Our study has two sections, and performance analysis contains 5 categories (Annual Scientific Production, Most Relevant Sources, Most Productive Authors, Most Cited Publications, Most Relevant Keywords.). The science mapping included country collaboration analysis and thematic analysis. We highlight our thematic analysis by splitting the whole bibliographic dataset into three temporal periods, thus a thematic evolution across time has been presented. This study is one of the most comprehensive bibliometric overview in analyzing Twitter-related studies by far. We proceed to explain how the results will benefit the understanding of current academic research interests on the social media giant.<\/jats:p>","DOI":"10.3390\/fi12050091","type":"journal-article","created":{"date-parts":[[2020,5,20]],"date-time":"2020-05-20T10:37:38Z","timestamp":1589971058000},"page":"91","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["A Bibliometric Overview of Twitter-Related Studies Indexed in Web of Science"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9400-4859","authenticated-orcid":false,"given":"Jingyuan","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Social Psychology, Universitat Aut\u00f2noma de Barcelona, 08193 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7782-9306","authenticated-orcid":false,"given":"Juan","family":"Mu\u00f1oz-Justicia","sequence":"additional","affiliation":[{"name":"Department of Social Psychology, Universitat Aut\u00f2noma de Barcelona, 08193 Barcelona, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,20]]},"reference":[{"key":"ref_1","unstructured":"Twitter (2018). 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