Tense and tendencies: a linguistic analysis of verbal data from people with low mood

My psychologically grounded linguistic analysis of verbal data investigates the link between language use and low mood. The focus of this study is low mood such as that experienced as part of mood disorders including depression. I address research question one: What linguistic markers are prevalent in the verbal language of people experiencing low mood? And research question two: Do people with low mood focus on the past?
Existing studies into language and mood disorders frequently rely on written language without demographic metadata. This obfuscates the potential for intersectionality, whilst popular analytical software LIWC is methodologically obtuse.
In this thesis, I cover the distinctive process of data generation including informant recruitment, interviews and transcription alongside categorisation. The focus of this study is the researcher elicited verbal data from informants with low mood, which is compared to the data from the remaining interviews as a reference.
I analysed the language data for patterns potentially associated with low mood, including stylistic, thematic, syntactic and temporal preponderances. I primarily used corpus linguistic methodologies including keyword and part of speech analysis. I also combined the psychological framework of attribution theory with linguistic appraisal theory.
The findings for research question one include an increased focus on the self and a reduced sense of agency for people experiencing low mood when compared to the reference group.
Further to this, low mood informants use a less varied vocabulary when compared to the reference group. The low mood informants are specific about negative things and talk generally about positive things, whilst the reference informants are the opposite. In response to research question two, low mood informants consider their current experience: important events, people and effects to be constant and unchanging. These findings have applications for linguistic approaches to mood data and for health professionals’ usage in outreach and amelioration.