Papers with mental health monitoring

2 papers
Exciting Mood Changes: A Time-aware Hierarchical Transformer for Change Detection Modelling (2024.findings-acl)

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Challenge: Existing work on temporally sensitive tasks focuses on predicting mood changes . however, there is little attention given to the importance of longitudinal language modelling .
Approach: They propose a Hawkes process-inspired transformation layer to model the influence of time on users’ posts, capturing both their immediate and historical dynamics.
Outcome: The proposed model outperforms existing models on two existing datasets and shows clear performance gains.
Combining Hierachical VAEs with LLMs for clinically meaningful timeline summarisation in social media (2024.findings-acl)

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Challenge: Existing studies have shown that social media users' posts can help identify depression, bipolar disorder or self-harm.
Approach: They propose a hybrid abstractive summarisation approach combining hierarchical VAEs with LLMs to produce clinically meaningful summaries from social media timelines.
Outcome: The proposed approach produces clinically meaningful summaries from social media user timelines, suitable for mental health monitoring.

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