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Retrieving arXiv, SocArXiv, and SSRN metadata for initial review screening
Tsinghua Univ, Sch Software, Beijing, Peoples R China..
Tsinghua Univ, Sch Software, Beijing, Peoples R China..
Tsinghua Univ, Sch Software, Beijing, Peoples R China..
Tsinghua Univ, Sch Software, Beijing, Peoples R China..
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2023 (English)In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 161, article id 107251Article, review/survey (Refereed) Published
Abstract [en]

Context: Researchers around the globe invest a lot of time searching the literature for performing reviews (Systematic Literature Review (SLR), Multivocal Literature Review (MLR)). The steps to performing the review includes inclusion of the grey literature, preprints, and quality assessed non-peer reviewed literature (the purpose is to minimize the publication bias). The initial screening of the papers takes time and bibliographic information is only available online for the researcher(s). Objective: Objective of our study is to propose, design, and develop a method that will help the research community to download the basic information of the papers (title, abstract, author) for the searched query from arxiv, SSRN, and SocArxiv (Social Science ArXiv). Method: We used Web scraping to extract data from the servers and save it in excel file. To retrieve the desired query from the databases, a Python code is used. Two methods have been discussed in the study to download the metadata of the searched query. Results: We have used different queries (such as "grey literature", "testing software", and "python" etc.) to see the results of our proposed method. Furthermore, we cross-verified the results with the online search results of the databases. Conclusion: Initial results from the preliminary pilot evaluations show that it is a viable method to search, download, and shortlist the research articles information (title, abstract etc.) from arXiv,1 SSRN,2 and SocArXiv.3 For external validity more evaluations are needed.

Place, publisher, year, edition, pages
ELSEVIER , 2023. Vol. 161, article id 107251
Keywords [en]
Information retrieval, Software engineering, Metadata, Initial screening, Bibliographic
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-63794DOI: 10.1016/j.infsof.2023.107251ISI: 001013081000001Scopus ID: 2-s2.0-85162768520OAI: oai:DiVA.org:mdh-63794DiVA, id: diva2:1779912
Available from: 2023-07-05 Created: 2023-07-05 Last updated: 2023-07-12Bibliographically approved

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Afzal, Wasif

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