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Recognition of Emotional Patterns of Depressive User Behaviors in Social Networks

Faculty Involved Jó Ueyama

Student Involved: Felipe Giuntini (PhD Project)

The rise and popularization of these online communities offer unprecedented opportunities to solve problems in a wide variety of fields through data mining techniques and information and knowledge extraction techniques (AGGARWAL; ABDELZAHER, 2011). In particular, psychology research is mostly based on questionnaires and interviews. However, many mental health professionals and researchers have attempted to analyze data from social networks from a psychological point of view. Undoubtedly, this interdisciplinary integration has injected new vigor into the area of ​​psychology. However, support computational are far from mental health. Mental disorders affect at least 20% of the world’s population at some point in life, being the second cause of emergency care. Besides, depression is the world’s most enormous public health problem (World Health Organization and others, 2017). However, experts in the field consider the utility network still insufficient. An example is the public health city of São Paulo, where less than 30% of municipal basic health units (UBS) offer psychological/psychiatric care, overloading the system (SANCHEZ, 2016).

 

The use of social networks as another lens in the process of identifying indicators of depressive mood disorders can enable faster and more accurate diagnosis and follow-up.  According to OMS, to be considered a major depressive disorder, the onset of symptoms lasts for at least two weeks. This project aims to recognize emotional behavior patterns of depressive users, as well as the evolution and temporal coherence of these behaviors, using the analysis of social media shared by these users.

A Social Life | Award Winning Short Film | Social Media Depression

Credits: Kerith Lemon Pictures

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