Detecting Cognitive Distortions Through Machine Learning Text Analytics

Abstract

Machine learning and text analytics have proven increasingly useful in a number of health-related applications, particularly in the context of analyzing online data for disease epidemics and warning signs of a variety of mental health issues. We follow in this tradition here, but focus our attention on cognitive distortion, a precursor and symptom of disruptive psychological disorders such as anxiety, anorexia and depression. We collected a number of personal blogs from the Tumblr API, and labeled them based on whether they exhibited distorted thought patterns. We then used LIWC to extract textual features and applied machine learning to the resulting vectors. Our findings show that it is possible to detect cognitive distortions automatically from personal blogs with relatively good accuracy (73.0%) and false negative rate (30.4%).