  Seven out of ten people with bipolar disorder are initially misdiagnosed and
thirty percent of individuals with bipolar disorder will commit suicide.
Identifying the early phases of the disorder is one of the key components for
reducing the full development of the disorder. In this study, we aim at
leveraging the data from social media to design predictive models, which
utilize the psychological and phonological features, to determine the onset
period of bipolar disorder and provide insights on its prodrome. This study
makes these discoveries possible by employing a novel data collection process,
coined as Time-specific Subconscious Crowdsourcing, which helps collect a
reliable dataset that supplements diagnosis information from people suffering
from bipolar disorder. Our experimental results demonstrate that the proposed
models could greatly contribute to the regular assessments of people with
bipolar disorder, which is important in the primary care setting.
