Leveraging Textual Similarity to Predict Beck Depression Inventory Answers

Abstract

This work proposes an approach to predict potential answers to the Beck Depression Inventory-Second Edition (BDI-II), a 21-item self-report inventory measuring the severity of depression in adolescents and adults. Predictions are based on similarity measures between the textual productions of social media users and completed BDI-IIs. Two methods of establishing similarity are compared. The first one is using unsupervised extraction of topics, and the second one is based on authorship attribution through the use of neural encoders. Both approaches achieve interesting results, indicating that the authorship attribution task can induce a similarity measure useful for depression symptom detection. The issues that arise in predicting several aspects of depression are further discussed.