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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.
Recent years have witnessed increased performance in many computational linguistics tasks such as syntactic and semantic parsing BIBREF0 , BIBREF1 , emotion classification BIBREF2 , and sentiment analysis BIBREF3 , BIBREF4 , BIBREF5 , especially concerning the applicability of such tools to noisy online data. Moreover, the field has made substantial progress in developing multilingual models and extending semantic annotation resources to languages beyond English BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 . Concurrently, it has been argued for mental health research that it would constitute a `valuable critical step' BIBREF10 to analyse first-hand accounts by individuals with lived experience of severe mental health issues in blog posts, tweets, and discussion forums. Several severe mental health difficulties, e.g., bipolar disorder (BD) and schizophrenia are considered as chronic and clinical recovery, defined as being relapse and symptom free for a sustained period of time BIBREF11 , is considered difficult to achieve BIBREF12 , BIBREF13 , BIBREF14 . Moreover, clinically recovered individuals often do not regain full social and educational/vocational functioning BIBREF15 , BIBREF16 . Therefore, research originating from initiatives by people with lived experience of mental health issues has been advocating emphasis on the individual's goals in recovery BIBREF17 , BIBREF18 . This movement gave rise to the concept of personal recovery BIBREF19 , BIBREF20 , loosely defined as a `way of living a satisfying, hopeful, and contributing life even with limitations caused by illness' BIBREF18 . The aspects of personal recovery have been conceptualised in various ways BIBREF21 , BIBREF22 , BIBREF23 . According to the frequently used CHIME model BIBREF24 , its main components are Connectedness, Hope and optimism, Identity, Meaning and purpose, and Empowerment. Here, we focus on BD, which is characterised by recurring episodes of depressed and elated (hypomanic or manic) mood BIBREF25 , BIBREF12 . Bipolar spectrum disorders were estimated to affect approximately 2% of the UK population BIBREF13 with rates ranging from 0.1%-4.4% across 11 other European, American and Asian countries BIBREF26 . Moreover, BD is associated with a high risk of suicide BIBREF27 , making its prevention and treatment important tasks for society. BD-specific personal recovery research is motivated by mainly two facts: First, the pole of positive/elevated mood and ongoing mood instability constitute core features of BD and pose special challenges compared to other mental health issues, such as unipolar depression BIBREF25 . Second, unlike for some other severe mental health difficulties, return to normal functioning is achievable given appropriate treatment BIBREF28 , BIBREF16 , BIBREF29 . A substantial body of qualitative and quantitative research has shown the importance of personal recovery for individuals diagnosed with BD BIBREF22 , BIBREF25 , BIBREF30 , BIBREF31 , BIBREF23 . Qualitative evidence mainly comes from (semi-)structured interviews and focus groups and has been criticised for small numbers of participants BIBREF10 , lacking complementary quantitative evidence from larger samples BIBREF32 . Some quantitative evidence stems from the standardised bipolar recovery questionnaire BIBREF30 and a randomised control trial for recovery-focused cognitive-behavioural therapy BIBREF31 . Critically, previous research has taken place only in structured settings. What is more, the recovery concept emerged from research primarily conducted in English-speaking countries, mainly involving researchers and participants of Western ethnicity. This might have led to a lack of non-Western notions of wellbeing in the concept, such as those found in indigenous peoples BIBREF32 , limiting its the applicability to a general population. Indeed, the variation in BD prevalence rates from 0.1% in India to 4.4% in the US is striking. It has been shown that culture is an important factor in the diagnosis of BD BIBREF33 , as well as on the causes attributed to mental health difficulties in general and treatments considered appropriate BIBREF34 , BIBREF35 . While approaches to mental health classification from texts have long ignored the cultural dimension BIBREF36 , first studies show that online language of individuals affected by depression or related mental health difficulties differs significantly across cultures BIBREF37 , BIBREF36 . Hence, it seems timely to take into account the wealth of accounts of mental health difficulties and recovery stories from individuals of diverse ethnic and cultural backgrounds that are available in a multitude of languages on the internet. Corpus and computational linguistic methods are explicitly designed for processing large amounts of linguistic data BIBREF38 , BIBREF39 , BIBREF40 , BIBREF41 , and as discussed above, recent advances have made it feasible to apply them to noisy user-generated texts from diverse domains, including mental health BIBREF42 , BIBREF43 . Computer-aided analysis of public social media data enables us to address several shortcomings in the scientific underpinning of personal recovery in BD by overcoming the small sample sizes of lab-collected data and including accounts from a more heterogeneous population. In sum, our research questions are as follows: (1) How is personal recovery discussed online by individuals meeting criteria for BD? (2) What new insights do we get about personal recovery and factors that facilitate or hinder it? We will investigate these questions in two parts, looking at English-language data by westerners and at multilingual data by individuals of diverse ethnicities.