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SYMBALS: A Systematic Review Methodology Blending Active Learning and Snowballing
Max van Haastrecht1, Injy Sarhan1,2, Bilge Yigit Ozkan1
1Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands.
Frontiers in Research Metrics and Analytics
|June 14, 2021
Summary
We developed SYMBALS, a novel systematic review method combining backward snowballing and active learning. This approach significantly accelerates research screening and outperforms existing methods.
Area of Science:
- Information Science
- Computer Science
- Bibliometrics
Background:
- Rapid growth in research output complicates comprehensive literature analysis.
- Systematic reviews are crucial for clarity but are time-consuming and expertise-intensive.
- Existing methods struggle to keep pace with the dynamic research landscape.
Purpose of the Study:
- To introduce and validate SYMBALS, an innovative methodology for efficient systematic reviews.
- To address the challenges of protracted review processes and the need for expertise.
- To demonstrate SYMBALS's capability in achieving broad research coverage swiftly.
Main Methods:
- SYMBALS integrates traditional backward snowballing with machine learning-based active learning.
- The methodology was applied in a case study for initial demonstration.
- Validity was confirmed through a replication study and benchmarking experiments.
Main Results:
- SYMBALS demonstrated swift achievement of broad research coverage in a case study.
- A replication study showed SYMBALS accelerates title and abstract screening by a factor of 6.
- Benchmarking confirmed SYMBALS outperforms the state-of-the-art FAST² systematic review methodology.
Conclusions:
- SYMBALS offers a significant advancement in systematic review efficiency and scope.
- The methodology effectively accelerates literature screening and enhances research coverage.
- SYMBALS presents a viable solution for managing the increasing volume of scientific literature.
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