MHA: a multimodal hierarchical attention model for depression detection in social media

Li, Zepeng; An, Zhengyi; Cheng, Wenchuan; Fang, Zheng; Hu, Bin

doi:10.1007/s13755-022-00197-5

Cited by 19 publications

(10 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors obtained a comprehensive and well-rounded annotation strategy to guide the categorization of social media users into individuals with an explicit diagnosis of EDs, healthcare professionals, communicators (i.e., those who communicate, exchange, and distribute information to the public), and non-ED individuals. The approaches above attempted to address the possibility of researchers overlooking implicit indicators of specific MH disorders or lacking sufficient clinical knowledge to make accurate inferences based on several posts created by each individual [ 135 , 189 ]. However, these efforts may not suffice, given that public content posted by individuals might be adapted with considerations of self-presentation factors.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches

Khoo,

Lim,

Chong

et al. 2024

Sensors

View full text Add to dashboard Cite

As mental health (MH) disorders become increasingly prevalent, their multifaceted symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing a risk of underdiagnosis. While machine learning (ML) has been explored to mitigate these challenges, we hypothesized that multiple data modalities support more comprehensive detection and that non-intrusive collection approaches better capture natural behaviors. To understand the current trends, we systematically reviewed 184 studies to assess feature extraction, feature fusion, and ML methodologies applied to detect MH disorders from passively sensed multimodal data, including audio and video recordings, social media, smartphones, and wearable devices. Our findings revealed varying correlations of modality-specific features in individualized contexts, potentially influenced by demographics and personalities. We also observed the growing adoption of neural network architectures for model-level fusion and as ML algorithms, which have demonstrated promising efficacy in handling high-dimensional features while modeling within and cross-modality relationships. This work provides future researchers with a clear taxonomy of methodological approaches to multimodal detection of MH disorders to inspire future methodological advancements. The comprehensive analysis also guides and supports future researchers in making informed decisions to select an optimal data source that aligns with specific use cases based on the MH disorder of interest.

show abstract

Section: Resultsmentioning

confidence: 99%

Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches

Khoo,

Lim,

Chong

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Furthermore, the studies presented in the table emphasize the importance of considering multimodal data, user characteristics, and sentiment analysis for a comprehensive understanding of depression 80 , 81 . They also propose the use of lexicon features and emotional information capture to improve depression detection 82 , 83 .…”

Section: Resultsmentioning

confidence: 99%

A systematic review on automated clinical depression diagnosis

Mao,

Wu,

Chen

2023

npj Mental Health Res

View full text Add to dashboard Cite

Assessing mental health disorders and determining treatment can be difficult for a number of reasons, including access to healthcare providers. Assessments and treatments may not be continuous and can be limited by the unpredictable nature of psychiatric symptoms. Machine-learning models using data collected in a clinical setting can improve diagnosis and treatment. Studies have used speech, text, and facial expression analysis to identify depression. Still, more research is needed to address challenges such as the need for multimodality machine-learning models for clinical use. We conducted a review of studies from the past decade that utilized speech, text, and facial expression analysis to detect depression, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline. We provide information on the number of participants, techniques used to assess clinical outcomes, speech-eliciting tasks, machine-learning algorithms, metrics, and other important discoveries for each study. A total of 544 studies were examined, 264 of which satisfied the inclusion criteria. A database has been created containing the query results and a summary of how different features are used to detect depression. While machine learning shows its potential to enhance mental health disorder evaluations, some obstacles must be overcome, especially the requirement for more transparent machine-learning models for clinical purposes. Considering the variety of datasets, feature extraction techniques, and metrics used in this field, guidelines have been provided to collect data and train machine-learning models to guarantee reproducibility and generalizability across different contexts.

show abstract

“…The authors proposed a feature section method for analyzing depression symptoms using Multivariate time series approach, but the authors ended up with only correlating the disease to some of its symptoms. Li et al [26] built and deployed a multimodal attention mechanism for classifying social media users to depressed or normal users. The authors confirmed that analyzing the text data with picture added to it can lead to better results in depression detection, but this is not always the case on social media, beside some other drawbacks of their prosed solution such as the complexity and time consumed performing classification task.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection and prediction of Future Mental disorder from Social Media Data using Machine Learning, Ensemble Learning, and Large Language Models.

Abdullah,

Negied

2024

IEEE Access

View full text Add to dashboard Cite

Social media platforms are used widely by all people to express their feelings, opinions, and emotional states. Billions of people worldwide use them daily to share what they think and feel in their posts. Amongst all social media available platforms, Facebook only contains around three billion personal accounts. In this work Reddit dataset is used to automatically detect mental illness from social media posts. This study is not only limited to early detection of already existing mental illness or disorder like depression and anxiety from social posts, but also and most importantly the study is extended to predict successfully potential mental illness that would happen in future. This study deploys Nineteen different models to study the capability of them in detecting and predicting mental disorders from social media posts. Some of the deployed models are classical machine learning classifiers, some are ensemble learning models, and the rest are large language models (LLMs). Six machine learning classifiers were used in this work for the automatic detection and prediction of mental illness and logistic regression proved to be the best amongst other classifiers in this task. Nine Ensemble methods were also used and examined. Amongst the Nine ensemble learning models VC2, Light GBM, Bagging estimator, and XGBoost proved to be superior in this task. Four large language models were also used and examined for the same task. RoBERTa and OpenAI GPT proved to outperform the rest of models in this task. All those models were built, trained, tested, and compared with previous work in literature to get the best possible results. The study covers the main four mental disorders which are ADHD, Anxiety, Bipolar, and Depression. The work proposed in this paper succeeded in outperforming the results in literature in terms of number of addressed mental disorders, number of models used and tested, and dataset size used to validate results. The proposed work also outperformed the only attempt in literature that addressed all mental disorders in results of detection and prediction noticeably. This work achieved the detection of already existing mental disorders F1-score of 0.80 from clinical data and of 0.52 from non-clinical data, and it achieved a prediction of future mental disorder F1-score of 0.43 from non-clinical data.

show abstract

MHA: a multimodal hierarchical attention model for depression detection in social media

Cited by 19 publications

References 38 publications

Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches

Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches

A systematic review on automated clinical depression diagnosis

Detection and prediction of Future Mental disorder from Social Media Data using Machine Learning, Ensemble Learning, and Large Language Models.

Contact Info

Product

Resources

About