梳理疫情后社交媒体抑郁建模的NLP进展与挑战
On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook
- 综述疫情后抑郁症社交媒体建模的NLP方法演进
- 指出疫情使抑郁率上升超50%，推动研究新发展
- 强调数据伦理、公平性与可问责性的重要性
近年来，计算方法在社交媒体心理健康预测方面已得到广泛研究。已有多个综述系统总结了该领域的进展。其中，抑郁症因全球高发而最受关注。自2020年初开始的新冠疫情对全球心理健康造成重大影响，各国采取的封锁等措施及随之而来的经济衰退显著改变了人们的生活状态与心理状况。研究表明，人群中抑郁症发病率上升超过50%。在此背景下，本文对自然语言处理（NLP）在社交媒体抑郁症建模中的应用进行综述，提供疫情后的研究展望。文章梳理了疫情背景下最新技术与新数据集的应用情况，并讨论了心理健康数据收集与处理中的伦理问题，重点关注公平性、可问责性与伦理规范。
原文摘要 · Abstract (English)
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.
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