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Using Topic Modeling to Detect and Describe Self‐Injurious and Related Content on a Large‐Scale Digital Platform
Author(s) -
Franz Peter J.,
Nook Erik C.,
Mair Patrick,
Nock Matthew K.
Publication year - 2020
Publication title -
suicide and life‐threatening behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.544
H-Index - 90
eISSN - 1943-278X
pISSN - 0363-0234
DOI - 10.1111/sltb.12569
Subject(s) - computer science , field (mathematics) , topic model , data science , software , the internet , scale (ratio) , digital content , world wide web , artificial intelligence , physics , mathematics , quantum mechanics , pure mathematics , programming language
Objective Self‐injurious thoughts and behaviors ( SITB s) are a complex and enduring public health concern. Increasingly, teenagers use digital platforms to communicate about a range of mental health topics. These discussions may provide valuable information that can lead to insights about complex issues like SITB s. However, the field of clinical psychology currently lacks an easy‐to‐implement toolkit that can quickly gather information about SITB s from online sources. In the present study, we applied topic modeling, a natural language processing technique, to identify SITB s and related themes online, and we validated this approach using human coders. Method We separately used topic modeling software and human coders to identify themes present in text from a popular online Internet support forum for teenagers. We then determined the degree to which results from the software's topic model aligned with themes identified by human coders. Results We found that topic modeling detected SITB s and related themes in online discussions in a way that accurately distinguishes between relevant and irrelevant human‐coded themes. Conclusions This approach has the potential to drastically increase our understanding of SITB s and related issues discussed on digital platforms, as well as our ability to identify those at risk for such outcomes.