Thekkekara, Joel PhilipYongchareon, Sira2026-01-262026-01-262025-12-03Big Data and Cognitive Computing, ISSN: 2504-2289 (Print); 2504-2289 (Online), MDPI AG, 9(12), 310-310. doi: 10.3390/bdcc91203102504-22892504-2289http://hdl.handle.net/10292/20540Depression represents a critical global mental health challenge, with social media offering unprecedented opportunities for early detection through computational analysis. We propose a novel BERT–CNN–BiLSTM architecture with attention mechanisms that systematically integrate emoji usage patterns—fundamental components of digital emotional expression overlooked by existing approaches. Evaluated on the SuicidEmoji dataset, our model achieves 97.12% accuracy, 94.56% precision, 93.44% F1-score, 85.67% MCC, and 91.23% AUC-ROC. Analysis reveals distinct emoji patterns: depressed users favour negative emojis (😔 13.9%, 😢 12.8%, 💔 6.7%) while controls prefer positive expressions (😂 16.5%, 😊 11.0%, 😎 10.2%). The attention mechanism identifies key linguistic markers, including emotional indicators, personal pronouns, and emoji features, providing interpretable insights into depression-related language. Our findings suggest that the integration of emojis substantially improves optimal social media-based mental health detection systems.© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).46 Information and Computing Sciences4608 Human-Centred ComputingDepressionMental IllnessMental HealthBrain DisordersBehavioral and Social Science3 Good Health and Well Being46 Information and computing sciencesdepression detectionnatural language processingdeep learningemoji analysisJournal ArticleOpenAccess10.3390/bdcc9120310