On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook

• URL: http://arxiv.org/abs/2410.08793v1
• Date: Fri, 11 Oct 2024 13:20:54 GMT
• Title: On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook
• Authors: Ana-Maria Bucur, Andreea-Codrina Moldovan, Krutika Parvatikar, Marcos Zampieri, Ashiqur R. KhudaBukhsh, Liviu P. Dinu,
• Abstract summary: Depression is the most widely studied mental health condition. The COVID-19 global pandemic has had a great impact on mental health worldwide. We present a survey on natural language processing (NLP) approaches to modeling depression in social media.
• Score: 21.978924582262263
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Computational approaches to predicting mental health conditions in social media have been substantially explored in the past years. Multiple surveys have been published on this topic, providing the community with comprehensive accounts of the research in this area. Among all mental health conditions, depression is the most widely studied due to its worldwide prevalence. The COVID-19 global pandemic, starting in early 2020, has had a great impact on mental health worldwide. Harsh measures employed by governments to slow the spread of the virus (e.g., lockdowns) and the subsequent economic downturn experienced in many countries have significantly impacted people's lives and mental health. Studies have shown a substantial increase of above 50% in the rate of depression in the population. In this context, we present a survey on natural language processing (NLP) approaches to modeling depression in social media, providing the reader with a post-COVID-19 outlook. This survey contributes to the understanding of the impacts of the pandemic on modeling depression in social media. We outline how state-of-the-art approaches and new datasets have been used in the context of the COVID-19 pandemic. Finally, we also discuss ethical issues in collecting and processing mental health data, considering fairness, accountability, and ethics.

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