A Computational Linguistic Study of Personal Recovery in Bipolar Disorder

Glorianna Jagfeld


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 inWestern 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.
Anthology ID:
P19-2003
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
17–26
Language:
URL:
https://aclanthology.org/P19-2003
DOI:
10.18653/v1/P19-2003
Bibkey:
Cite (ACL):
Glorianna Jagfeld. 2019. A Computational Linguistic Study of Personal Recovery in Bipolar Disorder. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 17–26, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
A Computational Linguistic Study of Personal Recovery in Bipolar Disorder (Jagfeld, ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-2003.pdf