What a sad tweet...

What a sad tweet...

Problem being addressed

Depression is a leading cause of disability worldwide​,​ but is often under-diagnosed and under-treated.


In an online sample of individuals, the researchers employed a theory-driven approach to measure linguistic markers that may indicate cognitive vulnerability to depression and study depressive language on Twitter. They defined a set of Cognitive Distortion Schemata that they grouped along 12 widely accepted types of distorted thinking and compared their prevalence between two cohorts of Twitter users: one of individuals who self-identified as having received a clinical diagnosis of depression and the other a similar random sample. The results point towards the detection, and possibly mitigation, of patterns of online language that are generally deemed depressogenic.

Advantages of this solution

An advantage of studying theory-driven differences between the language of depressed and non-depressed individuals, as opposed to a purely data-driven or machine learning approach, is that we can explicitly use the principles underpinning cognitive-behavioral theory to understand the cognitive and lexical components that may shape depression. The results of the suggested approach are robust to changes in the user sample and text sentiment.

Solution originally applied in these industries



Possible New Application of the Work


Healthcare Sector

The research confirms the recent observations that social media usage can have a negative impact on mental health, thus suggesting promising targets for psychotherapy. Timely detection of early depression based on analysis of written text (not only tweets but all sorts of posts, emails etc) can become significant part of preventive mental healthcare. HR departments can also benefit from this type of research to identify potential burnouts among their employees.


Telecommunications Industry

Future characterizations of the relations between depressogenic language and mood may aid in the development of automated interventions, e.g., chatbots.

Author of original research described in this blitzcard: Krishna C. Bathina, Marijn ten Thij, Lorenzo Lorenzo-Luaces, Lauren A. Rutter, Johan Bollen


Name of the author who conducted the original research that this blitzcard is based on.

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