Conventional approaches to identify depression are not scalable, and the public has limited awareness of mental health, especially in developing countries. As evident by recent studies, social media has the potential to complement mental health screening on a greater scale. The vast amount of first-person narrative posts in chronological order can provide insights into one's thoughts, feelings, behavior, or mood for some time, enabling a better understanding of depression symptoms reflected in the online space. In this paper, we propose SERCNN, which improves the user representation by (1) stacking two pretrained embeddings from different domains and (2) reintroducing the embedding context to the MLP classifier. Our SERCNN shows great performance over state-of-the-art and other baselines, achieving 93.7% accuracy in a 5-fold cross-validation setting. Since not all users share the same level of online activity, we introduced the concept of a fixed observation window that quantifies the observation period in a predefined number of posts. With as minimal as 10 posts per user, SERCNN performed exceptionally well with an 87% accuracy, which is on par with the BERT model, while having 98% less in the number of parameters. Our findings open up a promising direction for detecting depression on social media with a smaller number of posts for inference, towards creating solutions for a cost-effective and timely intervention. We hope that our work can bring this research area closer to real-world adoption in existing clinical practice.
翻译:确定抑郁症的常规方法无法伸缩,公众对心理健康的认识有限,特别是在发展中国家。最近的研究表明,社交媒体有可能更大规模地补充心理健康筛查。大量按时间顺序排列的第一人叙述性文章能够深入了解一个人的想法、感觉、行为或情绪一段时间,从而更好地了解在线空间中反映的抑郁症症状。在本文中,我们建议SERCNN(SERCNN)改善用户代表性,办法是:(1) 堆叠来自不同领域的两个预先训练的嵌入器,(2) 将嵌入环境重新引入MLP分类器。我们的SECNN(SERCN)展示出在最新和其他基线上的巨大业绩,在5倍交叉校验设置中实现了93.7%的准确性。由于并非所有用户都分享了相同的在线活动水平,我们引入了固定的观察窗口概念,将观察期分成一个预先界定的数。SERCNN(S)通过每个用户最少的10个职位,以87%的准确性为特别表现,这与BERT(S)基线和其他基线相比,在5倍的交叉校准环境中实现了93.7%的准确性,在交叉校准模型中实现准确性研究,同时将98 %,同时在我们现有的研究领域为更接近于更接近于更接近于更接近于更接近于更接近于更接近于更接近的深度的深度的研究,在现实的深度的深度的深度的深度的深度的深度的深度的深度的深度的模型,我们的模型中,我们为我们为探索。我们所测测测测测测测测测测。