  In cognitive psychology, automatic and self-reinforcing irrational thought
patterns are known as cognitive distortions. Left unchecked, patients
exhibiting these types of thoughts can become stuck in negative feedback loops
of unhealthy thinking, leading to inaccurate perceptions of reality commonly
associated with anxiety and depression. In this paper, we present a machine
learning framework for the automatic detection and classification of 15 common
cognitive distortions in two novel mental health free text datasets collected
from both crowdsourcing and a real-world online therapy program. When
differentiating between distorted and non-distorted passages, our model
achieved a weighted F1 score of 0.88. For classifying distorted passages into
one of 15 distortion categories, our model yielded weighted F1 scores of 0.68
in the larger crowdsourced dataset and 0.45 in the smaller online counseling
dataset, both of which outperformed random baseline metrics by a large margin.
For both tasks, we also identified the most discriminative words and phrases
between classes to highlight common thematic elements for improving targeted
and therapist-guided mental health treatment. Furthermore, we performed an
exploratory analysis using unsupervised content-based clustering and topic
modeling algorithms as first efforts towards a data-driven perspective on the
thematic relationship between similar cognitive distortions traditionally
deemed unique. Finally, we highlight the difficulties in applying mental
health-based machine learning in a real-world setting and comment on the
implications and benefits of our framework for improving automated delivery of
therapeutic treatment in conjunction with traditional cognitive-behavioral
therapy.
