How can computational tools help to understand language patterns in mental suffering considering social diversity

Abstract

The complex interaction between biological and social factors challenges measuring human behavior. Language has been a crucial source of information that mirrors inner processes like thoughts. The development of a novel computational strategy that helps to understand language needs to consider social factors that could also impact human behavior. Ten years ago, I developed a computational approach based on graph theory to measure structural aspects of the narrative's mental organization expressed in spontaneous oral reports. It was possible to measure the decrease in narrative graph connectedness associated with the schizophrenia diagnosis and negative symptoms severity. However, I was worried that the psychiatric field neglected factors from diverse social realities (such as poor access to education). Formal education impacts language by mastering grammar and syntax. Changes in language structure could be related to symptoms and lack of exposure to formal education. Indeed, the same connectedness markers increase according to typical cognitive and academic development. In this paper, I describe the reasons and methods for investigating both factors (psychiatric symptoms and formal education) on language patterns. Further, I evaluate concerns and future challenges of using computational strategies that include social diversity in mental health conditions.