  Due to its popularity and availability, social media data may present a new
way to identify individuals who are experiencing mental illness. By analysing
blog content, this study aimed to investigate the associations between
linguistic features and symptoms of depression, generalised anxiety, and
suicidal ideation. This study utilised a longitudinal study design. Individuals
who blogged were invited to participate in a study in which they completed
fortnightly mental health questionnaires including the PHQ9 and GAD7 for a
period of 36 weeks. Linguistic features were extracted from blog data using the
LIWC tool. Bivariate and multivariate analyses were performed to investigate
the correlations between the linguistic features and mental health scores
between subjects. We then used the multivariate regression model to predict
longitudinal changes in mood within subjects. A total of 153 participants
consented to taking part, with 38 participants completing the required number
of questionnaires and blog posts during the study period. Between-subject
analysis revealed that several linguistic features, including tentativeness and
non-fluencies, were significantly associated with depression and anxiety
symptoms, but not suicidal thoughts. Within-subject analysis showed no robust
correlations between linguistic features and changes in mental health score.
This study provides further support for the relationship between linguistic
features within social media data and symptoms of depression and anxiety. The
lack of robust within-subject correlations indicate that the relationship
observed at the group level may not generalise to individual changes over time.
