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

TL;DR

This paper reviews NLP methods for modeling depression in social media, emphasizing the impact of COVID-19 on mental health and discussing recent datasets, approaches, and ethical considerations in this research area.

Contribution

It provides a post-COVID-19 outlook on NLP approaches to depression detection, highlighting new datasets, methods, and ethical issues in the context of the pandemic.

Findings

Significant increase in depression rates post-COVID-19

Use of new datasets and state-of-the-art NLP methods

Discussion on ethical issues in mental health data processing

Abstract

Computational approaches to predicting mental health conditions in social media have been substantially explored in the past years. Multiple reviews 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 review on natural language processing (NLP) approaches to modeling depression in social media, providing the reader with a post-COVID-19 outlook. This review 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.