Individuals with depression express more distorted thinking on social media

Abstract

Depression is a leading cause of disability worldwide, but is often underdiagnosed and undertreated. Cognitive behavioural therapy holds that individuals with depression exhibit distorted modes of thinking, that is, cognitive distortions, that can negatively affect their emotions and motivation. Here, we show that the language of individuals with a self-reported diagnosis of depression on social media is characterized by higher levels of distorted thinking compared with a random sample. This effect is specific to the distorted nature of the expression and cannot be explained by the presence of specific topics, sentiment or first-person pronouns. This study identifies online language patterns that are indicative of depression-related distorted thinking. We caution that any future applications of this research should carefully consider ethical and data privacy issues.

Declining well-being during the COVID-19 pandemic reveals US social inequities

“…Although considerable advances have been made in characterizing and diagnosing mental health changes from social media data [ 46 ] remains a fast-evolving challenge. This capability has recently been demonstrated in the literature by the use of supervised learning models [ 47 ] and natural language analytics [ 48 ], and may in future work be leveraged to perform stratified analysis of our cohort samples. We also acknowledge social media samples may skew younger, more educated, and less ethnically diverse than the general population [ 49 ].…”

Developing natural language processing instruments to study sociotechnical systems

“…Emojis are uniquely and interestingly complex entities. 16 People-centric emojis can be composed of skin-tone modifiers, hair-type modifiers, and family structures.…”

“…and monitor inflammatory discourse on social media, with vital relevance to national security [217]. Sentiment analysis has also been used in the public health domain [64,109,313], with recent studies analyzing discussions on social media regarding mental health [16,276], especially in light of the latest advances in NLP and emerging ethical concerns [63]. The growing number of applications of sentimentaware systems has prompted tremendous efforts among the NLP community in the past decade to develop end-to-end models to examine short-and medium-length text documents [92,304], particularly for social media [3,157,215].…”

Historical language records reveal a surge of cognitive distortions in recent decades

“…Language is closely intertwined with this dynamic. In fact, recent research shows that individu-als with internalizing disorders express significantly higher levels of cognitive distortions in their language (16,17) to the point that their prevalence may be used as an index of vulnerability for depression (18,19).…”