Examining the Role of Mood Patterns in Predicting Self-Reported Depressive symptoms
Table 5. Predicting depressive symptom with hidden states
['Criteria', 'P', 'R', 'f1']
The baseline model is formulated using a stratified dummy classifier that predicts based on the most frequent training labels. Precision increases as the criterion of x increase. Assigning participants with six high symptom states within 14 days to the high symptom class results in very low recall (10.8%) but high precision (71.2%). Assigning participants with one high symptom state within 14 days results in a more balanced recall (60.3%) and precision (58.1%) to high symptom class. Result from this classifier does not surpass the baseline in f1 score but when using a higher x as criteria, the precision rate is much higher than the baseline. Our result supports the claim that daily mood representations inferred from social media text is highly associated with depressive symptoms. When a social media user shows specific mood patterns, it is highly likely that the person developed high level of depressive symptoms. However, only using this approach to identify high symptom individuals would result in a lot of false negative cases. Mood is a time dependent variable, using time series approaches to model mood inferred from social media text provides us with better insight about mood and depressive symptoms. Participants in this study demonstrated significantly fewer mood fluctuations if they reported a low symptom score. This finding aligns with the well-established connection between emotionality and depression in the psychology literature. Our model suggests that an individual having one high symptom state in 14 days is highly likely to have high symptom level. It is worth to note that the criteria we used in here is different from the criteria in the CES-D scale, where individuals need to have experienced symptoms 1-2 days in the past 7 days to score on a criterion. However, we cannot assume that people will talk about their symptoms every time they experience them. This result suggests that individuals who show specific mood pattern in social media text are highly likely having high depressive symptoms, however, most of the individuals with high symptom do not display this mood pattern.