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Testing a Citation and Text-Based Framework for Retrieving Publications for Literature Reviews

We propose a citationand text-based framework to conduct literature review searches. Given a small set of articles included in a literature review (i.e. seed articles), the first step of the framework retrieves articles that are connected to the seed articles in the citation network. The next step filters these retrieved articles using a hybrid citation and text-based criteria. In this paper, we evaluate a first implementation of this framework (code available at https://github.com/janinaj/ lit-review-search) by comparing it to the conventional search methods for retrieving the included studies of 6 published systematic reviews. Using different combinations of 3 seed articles, on average we retrieved 71.2% of the total included studies in the published reviews and 82.33% of the studies available in the search database (Scopus). Our best combinations retrieved 87% of the total included studies, which comprised 100% of the studies available in Scopus. In 5 of the 6 reviews, we reduced the number of results by 34–88%, which in practice would save reviewers significant time, since the overall number of search results that need to be manually screened is substantially reduced. These results suggest that our framework is a promising approach to improving the literature review search process.