JMIR Publications

ABSTRACT

Background:

Mental illness affects a significant portion of the worldwide population. Online mental health forums can provide a supportive environment for those afflicted and also generate a large amount of data which can be mined to predict mental health states using machine learning methods.

Objective:

We benchmark multiple methods of text feature representation for social media posts and compare their downstream use with automated machine learning (AutoML) tools. We test on datasets that contain posts labelled for perceived suicide risk or moderator attention in the context of self-harm. Specifically, we assess the ability of the methods to prioritize posts that a moderator would identify for immediate response.

Methods:

We used 1588 labeled posts from the CLPsych 2017 shared task collected from the Reachout.com forum (Milne et al., 2019). Posts were represented using lexicon based tools including VADER, Empath, LIWC and also used pre-trained artificial neural network models including DeepMoji, Universal Sentence Encoder, and GPT-1. We used TPOT and Auto-sklearn as AutoML tools to generate classifiers to triage the posts.

Results:

The top-performing system used features derived from the GPT-1 model, which was fine-tuned on over 150,000 unlabeled posts from Reachout.com. Our top system had a macro averaged F1 score of 0.572, providing a new state-of-the-art result on the CLPsych 2017 task. This was achieved without additional information from meta-data or preceding posts. Error analyses revealed that this top system often misses expressions of hopelessness. We additionally present visualizations that aid understanding of the learned classifiers.

Conclusions:

We show that transfer learning is an effective strategy for predicting risk with relatively little labeled data. We note that fine-tuning of pre-trained language models provides further gains when large amounts of unlabeled text is available.