Depression Symptoms Identification through Social Media Data: Applying Design Science Research to Develop a Classification Model

• Silas Lima Filho UFRJ
• Monica Ferreira da Silva UFRJ
• Jonice Oliveira UFRJ

DOI: https://doi.org/10.5753/webmedia_estendido.2024.244420

Resumo

The thesis addresses the prevalence of depression as a debilitating condition and underscores the importance of early identification of symptoms for timely interventions. Exploring user-generated content on social networks, the study proposes the use of machine learning models in detecting depressive symptoms. Following the DSR methodology, the research validates the effectiveness of these models compared to existing approaches, involving healthcare professionals and domain experts. The thesis introduces an innovative stacking model using LIWC metrics from social media posts, contributing to the understanding of machine learning-based solutions in identifying symptoms of depressive disorder.

Palavras-chave: transtorno depressivo, redes sociais, análise textual

Referências

C. Boscarioli, R. Araujo, and R. S. Maciel. 2017. I GranDSI-BR Grand Research Challenges in Information Systems in Brazil 2016-2026. SBC, Porto Alegre. DOI: 10.5753/sbc.2884.0

Isabel Cafezeiro, Jose Viterbo, Leonardo Costa, Luciana Salgado, Marcelo Rocha, and Rodrigo Monteiro. 2017. Strengthening of the Sociotechnical Approach in Information Systems Research. SBC, 133–147. DOI: 10.5753/sbc.2884.0.11

Munmun De Choudhury. 2014. Opportunities of Social Media in Health and Well-Being. XRDS 21, 2 (dec 2014), 23–27. DOI: 10.1145/2676570

Daniel M Low, Laurie Rumker, John Torous, Guillermo Cecchi, Satrajit S Ghosh, and Tanya Talkar. 2020. Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID19: Observational Study. Journal of medical Internet research 22, 10 (2020), e22635.

Silas P. Lima Filho, Mônica Ferreira da Silva, Jonice Oliveira, and Lívia Ruback. 2022. A Study about Gathering Features in Depression Detection’ Problem with Health Professionals Community. iSys - Brazilian Journal of Information Systems 15, 1 (Oct. 2022), 10:1–10:26. DOI: 10.5753/isys.2022.2285

Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. [link]

H. Topi and A. Tucker. 2014. Computing Handbook: Information Systems and Information Technology. CRC Press. [link]

Shou-Chun Yen, Kuo-Chung Chu, and Po-Yao Tsai. 2021. Prediction model of social network suicide ideation by small sample. In 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 385–389. DOI: 10.1109/IRI51335.2021.00060