{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T04:40:27Z","timestamp":1769575227646,"version":"3.49.0"},"reference-count":53,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["K"],"published-print":{"date-parts":[[2024,3,13]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Scholarly communications, particularly, questions and answers (Q&amp;A) present on digital scholarly platforms provide a new avenue to gain knowledge. However, several studies have raised a concern about the content anomalies in these Q&amp;A and suggested a proper validation before utilizing them in scholarly applications such as influence analysis and content-based recommendation systems. The content anomalies are referred as disinformation in this research. The purpose of this research is firstly, to assess scholarly communications in order to identify disinformation and secondly, to help scholarly platforms determine the scholars who probably disseminate such disinformation. These scholars are referred as the probable sources of disinformation.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>To identify disinformation, the proposed model deduces (1) content redundancy and contextual redundancy in questions (2) contextual nonrelevance in answers with respect to the questions and (3) quality of answers with respect to the expertise of the answering scholars. Then, the model determines the probable sources of disinformation using the statistical analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The model is evaluated on ResearchGate (RG) data. Results suggest that the model efficiently identifies disinformation from scholarly communications and accurately detects the probable sources of disinformation.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>Different platforms with communication portals can use this model as a regulatory mechanism to restrict the prorogation of disinformation. Scholarly platforms can use this model to generate an accurate influence assessment mechanism and also relevant recommendations for their scholars.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The existing studies majorly deal with validating the answers using statistical measures. The proposed model focuses on questions as well as answers and performs a contextual analysis using an advanced word embedding technique.<\/jats:p><\/jats:sec>","DOI":"10.1108\/k-07-2022-0998","type":"journal-article","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T00:44:53Z","timestamp":1673916293000},"page":"1434-1449","source":"Crossref","is-referenced-by-count":1,"title":["Contextual analysis of scholarly communications to identify the\u00a0source of disinformation on\u00a0digital scholarly platforms"],"prefix":"10.1108","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3264-6143","authenticated-orcid":false,"given":"Mitali","family":"Desai","sequence":"first","affiliation":[]},{"given":"Rupa G.","family":"Mehta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5058-1355","authenticated-orcid":false,"given":"Dipti P.","family":"Rana","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"key":"key2024031301263290300_ref001","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/IRI.2018.00016","article-title":"Recommending scientific collaboration based on topical, authors and venues similarities","year":"2018"},{"key":"key2024031301263290300_ref002","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1108\/LHT-05-2017-0090","article-title":"Ranking authors in academic social networks: a survey","volume":"36","year":"2018","journal-title":"Library Hi Tech"},{"key":"key2024031301263290300_ref003","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1007\/s10734-016-9995-x","article-title":"Measuring impact in research evaluations: a thorough discussion of methods for, effects of and problems with impact measurements","volume":"73","year":"2017","journal-title":"Higher Education"},{"key":"key2024031301263290300_ref004","first-page":"911","article-title":"A paper recommendation system based on user's research interests","year":"2018"},{"key":"key2024031301263290300_ref005","doi-asserted-by":"crossref","unstructured":"Bulut, B., G\u00fcndo\u011fan, E., Kaya, B., Alhajj, R. and Kaya, M. 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