The purpose of this scoping review is to highlight the machine learning tools used in research to address and prevent violent extremism. To achieve this goal, the following objectives guide this study: (1) describe outcomes that have been studied; (2) summarize the data sources used; and (3) determine whether the reporting of machine learning predictive models aligns with the established reporting guidelines for reporting of prediction models. ProQuest, Compendex, IEEE, JStor and PubMed were searched from June to July 2022. Based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, databases were searched for articles related to machine learning models applied to the address and prevention of violent extremism. Following standards established by reporting guidelines, findings were extracted from published articles, including general study characteristics, aspects of model development, and reporting of results. Of 53 unique articles identified by the search, 18 were included in the review. Most articles were published between 2016 and 2022 (n = 16, 88.8%). Studies focused on violent extremism worldwide, with the majority of studies not specifically focused on a distinct region (n = 11, 61.1%). The most frequently used machine learning algorithms were support vector machines (n = 9, 50%), followed by random forests (n = 5, 27.7%), natural language processing (n = 4, 22.2%), and deep learning (n = 4, 22.2%). The number of features used varied greatly, ranging from 17 to 7556. Many studies did not report an epistemological or theoretical framework which guided their machine learning approaches or interpretation of findings (n = 8, 44.4%). Many studies did not incorporate the TRIPOD or any other recommended guidelines for the reporting of predictive models. Future research in this field should prioritize evaluating the impact of prediction models on decisions for addressing and preventing violent extremism.
