A computational linguistic study of personal recovery in bipolar disorder
This research addresses the need for a more comprehensive view of personal recovery in bipolar disorder, particularly for a diverse, global population, though it is incremental as it builds on existing methods.
The study tackled the problem of understanding personal recovery in bipolar disorder by analyzing social media data with computational linguistics, aiming to complement existing qualitative and quantitative evidence with large-scale, unstructured, and multilingual first-person accounts.
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.