Abstract

Literature reviews are essential before conducting research on any topic. A thorough literature review provides an overview of current knowledge, allowing researchers to identify relevant theories, methods, and gaps in the existing research. Yet a thorough literature review is always tedious and time-consuming, especially when a large portion of the time and effort is wasted on citation screening—identifying the dozens of relevant papers from hundreds to thousands of non-relevant search results. In this tutorial, we will demonstrate how FASTREAD, a machine learning tool (https://github.com/fastread/src), helps reduce the time and effort of citation screening by incrementally learning what the user is looking for and thus predicting which papers are more likely to be relevant. In the instructors’ own experience of using this tool, it reduced 50 man-hours (94%) of the citation screening effort and included 90% of the relevant papers.

This tutorial features three sections each presenting

• the theory behind the FASTREAD tool
• a demonstration of how to set up and use the tool for citation screening
• an interactive section for the audiences to set up and use the tool themselves