Mental Health Safety and Depression Detection in Social Media Text Data: A Classification Approach Based on a Deep Learning Model
Social media expansion enables real-time mental health monitoring particularly for depression detection through its platforms. Traditional depression detection techniques, however, struggle to handle the informal, often ambiguous nature of language used in social media, including emojis, slang, and...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10960428/ |
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| author | Shiwen Zhou Masnizah Mohd |
| author_facet | Shiwen Zhou Masnizah Mohd |
| author_sort | Shiwen Zhou |
| collection | DOAJ |
| description | Social media expansion enables real-time mental health monitoring particularly for depression detection through its platforms. Traditional depression detection techniques, however, struggle to handle the informal, often ambiguous nature of language used in social media, including emojis, slang, and context-dependent expressions. To address this limitation, this research proposes a novel framework for depression detection that integrates advanced text preprocessing with a deep learning model. The preprocessing pipeline contains two key components where emojis are converted into text by emoji normalization then slang expressions are replaced through a customized dictionary. Emotion scores serve as additional components to identify depression-related emotional cues within the text. The model combines BERT’s contextual embeddings with BiLSTM’s sequential processing power to effectively represent emotional content in social media posts. The proposed approach demonstrates superior performance compared to traditional baseline models by achieving better results in accuracy, precision, recall, F1-score, AUC and RGA. This approach shows exceptional performance in detecting depression through informal texts which serves as a new benchmark for depression detection in dynamic social media environments. |
| format | Article |
| id | doaj-art-b0c2d0a58bc749369358587647afef38 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b0c2d0a58bc749369358587647afef382025-08-20T02:26:27ZengIEEEIEEE Access2169-35362025-01-0113632846329710.1109/ACCESS.2025.355917010960428Mental Health Safety and Depression Detection in Social Media Text Data: A Classification Approach Based on a Deep Learning ModelShiwen Zhou0https://orcid.org/0009-0000-1967-6199Masnizah Mohd1https://orcid.org/0000-0001-8908-8755Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, MalaysiaFaculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, MalaysiaSocial media expansion enables real-time mental health monitoring particularly for depression detection through its platforms. Traditional depression detection techniques, however, struggle to handle the informal, often ambiguous nature of language used in social media, including emojis, slang, and context-dependent expressions. To address this limitation, this research proposes a novel framework for depression detection that integrates advanced text preprocessing with a deep learning model. The preprocessing pipeline contains two key components where emojis are converted into text by emoji normalization then slang expressions are replaced through a customized dictionary. Emotion scores serve as additional components to identify depression-related emotional cues within the text. The model combines BERT’s contextual embeddings with BiLSTM’s sequential processing power to effectively represent emotional content in social media posts. The proposed approach demonstrates superior performance compared to traditional baseline models by achieving better results in accuracy, precision, recall, F1-score, AUC and RGA. This approach shows exceptional performance in detecting depression through informal texts which serves as a new benchmark for depression detection in dynamic social media environments.https://ieeexplore.ieee.org/document/10960428/BERT-BiLSTMdepression detectionsocial mediamental healthnatural language processing (NLP) |
| spellingShingle | Shiwen Zhou Masnizah Mohd Mental Health Safety and Depression Detection in Social Media Text Data: A Classification Approach Based on a Deep Learning Model IEEE Access BERT-BiLSTM depression detection social media mental health natural language processing (NLP) |
| title | Mental Health Safety and Depression Detection in Social Media Text Data: A Classification Approach Based on a Deep Learning Model |
| title_full | Mental Health Safety and Depression Detection in Social Media Text Data: A Classification Approach Based on a Deep Learning Model |
| title_fullStr | Mental Health Safety and Depression Detection in Social Media Text Data: A Classification Approach Based on a Deep Learning Model |
| title_full_unstemmed | Mental Health Safety and Depression Detection in Social Media Text Data: A Classification Approach Based on a Deep Learning Model |
| title_short | Mental Health Safety and Depression Detection in Social Media Text Data: A Classification Approach Based on a Deep Learning Model |
| title_sort | mental health safety and depression detection in social media text data a classification approach based on a deep learning model |
| topic | BERT-BiLSTM depression detection social media mental health natural language processing (NLP) |
| url | https://ieeexplore.ieee.org/document/10960428/ |
| work_keys_str_mv | AT shiwenzhou mentalhealthsafetyanddepressiondetectioninsocialmediatextdataaclassificationapproachbasedonadeeplearningmodel AT masnizahmohd mentalhealthsafetyanddepressiondetectioninsocialmediatextdataaclassificationapproachbasedonadeeplearningmodel |