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

Detection of the Prodromal Phase of Bipolar Disorder from Psychological and Phonological Aspects in Social Media