Tracing the Emotional Roadmap of Depressive Users on Social Media Through Sequential Pattern Mining

Author(s)

Giuntini, Felipe Taliar ^{[1, 2]} ; De Moraes, Kaue L. P. ^[1] ; Cazzolato, Mirela T. ^[1] ; Kirchner, Luziane de Fatima ^[3] ; Dos Reis, Maria de Jesus D. ^[4] ; Traina, Agma J. M. ^[1] ; Campbell, Andrew T. ^[5] ; Ueyama, Jo ^[1]

Affiliation

^[1] Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Carlos, SP - Brazil

^[2] Sidia Inst Sci & Technol, BR-69055035 Manaus, Amazonas - Brazil

^[3] Univ Catolica Dom Bosco, Dept Psychol, BR-79117900 Campo Grande, MS - Brazil

^[4] Univ Fed Sao Carlos, Dept Psychol, BR-13565905 Sao Carlos, SP - Brazil

^[5] Dartmouth Coll, Dept Comp Sci, Hanover, NH 03755 - USA

• Document type: Journal article
• Source: IEEE ACCESS; v. 9, p. 97621-97635, 2021.
• Web of Science Citations: 0

Abstract

Depression is one of the most growing health disorders, generating social and economic problems. The affective computing models focus on analyzing unique user posts, not observing temporal behavior patterns, which are essential to track changes and the evolution of emotional behavior and user context, that involves the persistent analysis of feelings and characteristics over time. This article proposes the TROAD framework for longitudinal recognition of sequential patterns from depressive users on social media. The framework identifies the best interval to analyze every user activity, extracts emotional and contextual features from user data, and models the features into time windows to recognize sequential patterns from depressive user behavior. The main characteristics of the users found in the top-10 rules are negative emotions: violence, pain, shame, depression, sadness, and silence. We obtained strong sequence patterns with a minimum of 70% of support, 81% of confidence, and 69% regarding sequential confidence, considering periods of silence between users' posts. Without considering silent periods, the rules showed 70%, 86%, and 38% of support, confidence, and sequential confidence. TROAD computational approach is a promising tool for clinical specialists in human behavior. (AU)

• FAPESP's process: 18/24414-2 - A framework for integration of feature extraction techniques and complex databases for MIVisBD
• Grantee: Mirela Teixeira Cazzolato
• Support Opportunities: Scholarships in Brazil - Technical Training Program - Technical Training
• FAPESP's process: 20/07200-9 - Analyzing complex data from COVID-19 to support decision making and prognosis
• Grantee: Agma Juci Machado Traina
• Support Opportunities: Regular Research Grants
• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD)
• Grantee: Agma Juci Machado Traina
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 20/11258-2 - Interoperability and similarity queries on medical databases
• Grantee: Mirela Teixeira Cazzolato
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 18/17335-9 - Exploring DLTs and Computational Intelligence in IoT
• Grantee: Jó Ueyama
• Support Opportunities: Regular Research Grants