Research On User Charateristics And Psychological Crisis Intervention In An Online Depression Community

Depression is a global public health management issues that should be addressed urgently,and it severely impairs human physical and mental health.At present,the prevalence of depression is 2.1% in China,but due to the shortage of mental health specialists and the patients’ stigma sense,the rate of consultation for depression is less than 20%.The online depression community has become an popular platform for depression patients to discuss health problems,make friends online,and seek medical help.A large number of users who are struggling with depression gather in the depression community,but many of them do not realize the severity of their illness and the need for professional psychological assistance.Undetected and untreated depression has become a serious public health issue,which will cause immeasurable personal and property losses,such as violence and suicide deaths.A full understanding of the characteristics of users in the community will help community managers and psychological crisis interventionists to develop better health management strategies and intervention mechanisms that focus on different types of users.Therefore,this topic uses a large number of posts and comment data in the online depression community as the main data source,and uses natural language processing technology,machine learning,and empirical research to analyze the user’s negative emotions,symptoms,and community participation behavior characteristics in the online depression community.Based on this analysis,an online psychological crisis intervention system for depression communities is constructed,and provide a reference for the application of big data on online psychological crisis intervention.The main research contents are as follows:Firstly,it analyzes the negative emotion characteristics and evolution patterns of depressed users in the community.Negative emotions are the main characteristic of users with depression.A full understanding of the changing patterns of negative emotions will help patients find,guide and intervene in time.This research uses the deep learning LSTM neural network model to build a text classifier,realizes the recognition of depressed users and their negative emotions,and quantitatively analyzes the subject content and time distribution of negative emotions.Using time series analysis,the evolutionary pattern of negative emotions of depressed users is described,and a method of how to monitor the changes of negative emotions of depressed users based on social media data is proposed.Secondly,an online automatic recognition model of depressive symptoms was established,and the study revealed the correlations between individual symptoms.Symptoms are an important basis for the early recognition and treatment of depression.In order to summarize what should be included in online depressive symptoms,we established annotation scheme.Based on the annotation scheme,the deep learning Attention-BLSTM method is used to construct a depressive symptom recognition model,which realizes the automatic and rapid identification of various depressive symptoms from a large amount of noisy community text data.Then,the method of network analysis is used to reveal the relationship between the various symptoms and their accompanying symptoms.Thirdly,the role of two participation behaviors,contribution and rumination,of depressed community users was investigated.First,based on the response style theory of depression,the participation behaviors of users in depression communities were classified into two types: online rumination and community contribution,and the two behaviors were extracted from the posted by using the BERT pre-training model.Based on this,the effects of users’ online rumination and community contribution behaviors on received of social support and co-rumination(i.e.,emotional contagion)were explored based on social support theory,and the moderating effect of illness severity was also explored.The results of the study may provide some academic foundation for community managers to improve the social services of their communities and motivate community members to practice healthy behaviors.Finally,based on the results of this study on the negative emotions,depressive symptoms and participation behavior characteristics of users in the depression community,an online psychological crisis intervention system for the depressed community was constructed.According to the process of crisis development,the online psychological crisis intervention system includes three functions: crisis prevention,crisis early warning,and crisis intervention.This article primary introduces the basic features of the psychological crisis intervention system,analyzes the relationship between the three functions,and then constructs a specific path to realize the three functions.This system can provide an effective method for the depression community to achieve the goal of online psychological crisis intervention for users with depression,and develop an academic foundation for the formulation of suicide intervention strategies.