Abstract
Depression is a globally known disease with a great impact on the suicide rate. However, this can be an early diagnostic by observing the behavior of the patients through the time. In this research, we studied the linguistics and visual features of depressive mood during COVID-19 pre and post-pandemic based on Flickr posts. We implemented the significant advances in text-based sentiment analysis and image classification using Natural Language Processing (NLP), histograms and deep learning strategies to characterize some of the main patterns of depression. We demonstrate that user’s behavior in social media had a relevant impact during pandemics, since the main patterns change drastically between periods. For images, we found that in pre-pandemic, user posts were more uniform in color distribution and with medium to low levels of light intensity. Besides, the scenes were more outside activities like. For text, we found that the topics and general sentiment were always depressive and with negative connotation, however, during pre-pandemic they described attributes of the symptomatology of depression pathology, while in post-pandemic are more related to the product of isolation and fear.
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Fernández-Barrera, I., Bravo-Bustos, S., Vidal, M. (2024). Evaluating the Social Media Users’ Mental Health Status During COVID-19 Pandemic Using Deep Learning. In: Pino, E., Magjarević, R., de Carvalho, P. (eds) International Conference on Biomedical and Health Informatics 2022. ICBHI 2022. IFMBE Proceedings, vol 108. Springer, Cham. https://doi.org/10.1007/978-3-031-59216-4_7
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DOI: https://doi.org/10.1007/978-3-031-59216-4_7
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