Depressed individuals express more distorted thinking on social media
This research addresses the challenge of under-diagnosis and under-treatment of depression by potentially enabling detection and mitigation of depressogenic language patterns online, though it is incremental as it builds on known cognitive-behavioral therapy principles.
The study tackled the problem of detecting depression through social media language by showing that individuals with self-reported depression express higher levels of distorted thinking, with some types like Personalizing and Emotional Reasoning being more than twice as prevalent in the depressed cohort.
Depression is a leading cause of disability worldwide, but is often under-diagnosed and under-treated. One of the tenets of cognitive-behavioral therapy (CBT) is that individuals who are depressed exhibit distorted modes of thinking, so-called cognitive distortions, which can negatively affect their emotions and motivation. Here, we show that individuals with a self-reported diagnosis of depression on social media express higher levels of distorted thinking than a random sample. Some types of distorted thinking were found to be more than twice as prevalent in our depressed cohort, in particular Personalizing and Emotional Reasoning. This effect is specific to the distorted content of the expression and can not be explained by the presence of specific topics, sentiment, or first-person pronouns. Our results point towards the detection, and possibly mitigation, of patterns of online language that are generally deemed depressogenic. They may also provide insight into recent observations that social media usage can have a negative impact on mental health.