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Conventional approaches to identify depression are not scalable, and thepublic has limited awareness of mental health, especially in developingcountries. As evident by recent studies, social media has the potential tocomplement mental health screening on a greater scale. The vast amount offirst-person narrative posts in chronological order can provide insights intoone's thoughts, feelings, behavior, or mood for some time, enabling a betterunderstanding of depression symptoms reflected in the online space. In thispaper, we propose SERCNN, which improves the user representation by (1)stacking two pretrained embeddings from different domains and (2) reintroducingthe embedding context to the MLP classifier. Our SERCNN shows great performanceover state-of-the-art and other baselines, achieving 93.7% accuracy in a 5-foldcross-validation setting. Since not all users share the same level of onlineactivity, we introduced the concept of a fixed observation window thatquantifies the observation period in a predefined number of posts. With asminimal 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 thenumber of parameters. Our findings open up a promising direction for detectingdepression on social media with a smaller number of posts for inference,towards creating solutions for a cost-effective and timely intervention. Wehope that our work can bring this research area closer to real-world adoptionin existing clinical practice.

SERCNN: Stacked Embedding Recurrent Convolutional Neural Network in Detecting Depression on Twitter