A review on recognizing depression in social networks: challenges and opportunities

Autor(es)

Giuntini, Felipe T. ^[1] ; Cazzolato, Mirela T. ^[1] ; dos Reis, Maria de Jesus Dutra ^[2] ; Campbell, Andrew T. ^[3] ; Traina, Agma J. M. ^[1] ; Ueyama, Jo ^[1]

Afiliação do(s) autor(es)

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

^[2] Univ Fed Sao Carlos, Dept Psychol, Sao Carlos, SP - Brazil

^[3] Dartmouth Coll, Dept Comp Sci, Hanover, NH 03755 - USA

• Tipo de documento: Artigo de Revisão
• Fonte: JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING; v. 11, n. 11, SI, p. 4713-4729, NOV 2020.
• Citações Web of Science: 7

Resumo

Social networks have become another resource for supporting mental health specialists in making inferences and finding indications of mental disorders, such as depression. This paper addresses the state-of-the-art regarding studies on recognition of depressive mood disorders in social networks through approaches and techniques of sentiment and emotion analysis. The systematic research conducted focused on social networks, social media, and the most employed techniques, feelings, and emotions were analyzed to find predecessors of a depressive disorder. Discussions on the research gaps identified aimed at improving the effectiveness of the analysis process, bringing the analysis close to the user's reality. Twitter, Facebook, Blogs and Forums, Reddit, Live Journal, and Instagram are the most employed social networks regarding the identification of depressive mood disorders, and the most used information wastext, followed by emoticons, user log information, and images. The selected studies usually employ classic off-the-shelf classifiers for the analysis of the available information, combined with lexicons such as NRC Word-Emoticon Association Lexicon, WordNet-Affect, Anew, and LIWC tool. The challenges include the analysis of temporal information and a combination of different types of information. (AU)

• Processo FAPESP: 16/17078-0 - Mineração, indexação e visualização de Big Data no contexto de sistemas de apoio à decisão clínica (MIVisBD)
• Beneficiário: Agma Juci Machado Traina
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 18/17335-9 - Explorando DLTs e a inteligência computacional em IoT
• Beneficiário: Jó Ueyama
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 18/24414-2 - Ambiente para integração de técnicas para a extração de características e bases de dados complexos para o projeto MIVisBD
• Beneficiário: Mirela Teixeira Cazzolato
• Modalidade de apoio: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico