MHSM 2025 : Mental Health Disorder Detection on Social Media
The MHSM workshop is a half-day on-site workshop that aims to foster collaboration across fields such as computer science, mental health, and social sciences to develop robust, ethical, and scalable methods for identifying early signs of cognitive and mental health issues based on social media data. The deadline for paper submission is Aug 29, 2025 (11:59 P.M. AoE).
Opportunity for Workshop Authors:
Authors of outstanding papers will be invited to submit extended versions to the special issue "Mental Disorder Detection on Social
Media" under the IEEE Transactions on Computational Social Systems
Topic of Interest
CMH Datasets and Benchmarks:
Multi-modal and Large-scale Datasets
Data Annotation for Integrity and Quality
Evaluation Metrics and Benchmarking
Open-source Data and Repositories
Data Diversity and Representation
CMH Data Management:
Database Security and Privacy
Multi-modal Data Management
Distributed and Federated Data Management
Real-time Data Processing
Data Curation and Lifecycle Management
Data Mining and Knowledge Discovery for CMH:
Scalable and Efficient Data Mining Algorithms
Multi-modal and Cross-modal Data Mining
Pattern Recognition and Anomaly Detection
Sentiment Analysis and Emotion Extraction
Explainability, Fairness and Biases in Models
Sequential and Temporal Data Mining
Graphs, and Semi-structured Data Mining
Outlier Detection and Noise Robustness
Mining in Imbalanced Datasets
Automated Knowledge Discovery
Opportunity for Workshop Authors:
Authors of outstanding papers will be invited to submit extended versions to the special issue "Mental Disorder Detection on Social
Media" under the IEEE Transactions on Computational Social Systems
Topic of Interest
CMH Datasets and Benchmarks:
Multi-modal and Large-scale Datasets
Data Annotation for Integrity and Quality
Evaluation Metrics and Benchmarking
Open-source Data and Repositories
Data Diversity and Representation
CMH Data Management:
Database Security and Privacy
Multi-modal Data Management
Distributed and Federated Data Management
Real-time Data Processing
Data Curation and Lifecycle Management
Data Mining and Knowledge Discovery for CMH:
Scalable and Efficient Data Mining Algorithms
Multi-modal and Cross-modal Data Mining
Pattern Recognition and Anomaly Detection
Sentiment Analysis and Emotion Extraction
Explainability, Fairness and Biases in Models
Sequential and Temporal Data Mining
Graphs, and Semi-structured Data Mining
Outlier Detection and Noise Robustness
Mining in Imbalanced Datasets
Automated Knowledge Discovery