Extraction of Characteristics of Time in “Tree Hole” Data

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Abstract
Statistics show that 15 percent of depressed people died by suicide, and more than 50 percent of depressed people are thinking about suicide. Worldwide, depression has become the second leading cause of death among people aged 15–29. This paper focus on the “tree hole” message data on microblog, and conducts data visualization research from different granularity, such as quarter, month, and analyses activity of message during holiday based on the knowledge graph, so as to obtain the national time distribution characteristics of the potential risk of mental health for the reference of social institutions’ monitoring and rescue and government departments’ decision-making. According to the time distribution rule of “tree hole” data, the relatively high occurrence time and possible reasons for depression and suicide are found, so that manpower could be reasonably deployed for effective prevention and rescue.