# A computational linguistic study of personal recovery in bipolar disorder

## TL;DR

This study explores how computational linguistics can analyze social media data to better understand personal recovery experiences in bipolar disorder across diverse populations.

## Contribution

It introduces a novel interdisciplinary approach combining computational linguistics with mental health research to analyze large-scale, multilingual social media data on bipolar disorder recovery.

## Key findings

- Potential to uncover new recovery insights from online language data
- Enables analysis of diverse, multilingual populations
- Provides a scalable method for mental health research

## Abstract

Mental health research can benefit increasingly fruitfully from computational linguistics methods, given the abundant availability of language data in the internet and advances of computational tools. This interdisciplinary project will collect and analyse social media data of individuals diagnosed with bipolar disorder with regard to their recovery experiences. Personal recovery - living a satisfying and contributing life along symptoms of severe mental health issues - so far has only been investigated qualitatively with structured interviews and quantitatively with standardised questionnaires with mainly English-speaking participants in Western countries. Complementary to this evidence, computational linguistic methods allow us to analyse first-person accounts shared online in large quantities, representing unstructured settings and a more heterogeneous, multilingual population, to draw a more complete picture of the aspects and mechanisms of personal recovery in bipolar disorder.