{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T05:30:51Z","timestamp":1764999051999,"version":"3.46.0"},"reference-count":33,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T00:00:00Z","timestamp":1764720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Depression represents a critical global mental health challenge, with social media offering unprecedented opportunities for early detection through computational analysis. We propose a novel BERT\u2013CNN\u2013BiLSTM architecture with attention mechanisms that systematically integrate emoji usage patterns\u2014fundamental 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 (\ud83d\ude14 13.9%, \ud83d\ude22 12.8%, \ud83d\udc94 6.7%) while controls prefer positive expressions (\ud83d\ude02 16.5%, \ud83d\ude0a 11.0%, \ud83d\ude0e 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.<\/jats:p>","DOI":"10.3390\/bdcc9120310","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T16:47:37Z","timestamp":1764780457000},"page":"310","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Attention-Based BERT\u2013CNN\u2013BiLSTM Model for Depression Detection from Emojis in Social Media Text"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4212-2183","authenticated-orcid":false,"given":"Joel Philip","family":"Thekkekara","sequence":"first","affiliation":[{"name":"School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology; Auckland 1010, New Zealand"}]},{"given":"Sira","family":"Yongchareon","sequence":"additional","affiliation":[{"name":"School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology; Auckland 1010, New Zealand"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,3]]},"reference":[{"key":"ref_1","unstructured":"Dattani, S., Rod\u00e9s-Guirao, L., Ritchie, H., and Roser, M. 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