ArticlePloS one2025
Development and validation of an automated machine for self-injury assessment via young Koreans' natural writings.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Self-injury is common in all countries, and 20% of South Korean youths experience self-injury. One of the barriers to assessment and treatment planning is the tendency of young self-injurers to conceal their identities. Following a new stream of research that uses online text data to assess psychological symptoms as they are described in online posts, this study developed a computerized machine that can analyze South Korean self-injurers' writing in assessing their self-injury severity. Based on 16,645 online posts, Study 1 developed a machine called the Korean Self-Injurious Text Reviewer (K-SITR) using Latent Dirichlet Allocation topic modeling and machine learning. The K-SITR's text-assessment results were statistically indistinguishable from those of professional counselors. Study 2 confirmed the validity of the K-SITR through a survey of 47 young Koreans who had experienced self-injury. Results showed that the K-SITR scores converged with participants' self-injury frequency and duration and discriminated from other heterogenous factors. The K-SITR also had incremental validity over two popular self-injury questionnaires. This study provides a new measure that may reduce the tendency of young self-injurers to self-conceal compared to traditional direct-item questionnaires.
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