  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.
