Depression Detection
25 papers with code • 4 benchmarks • 5 datasets
Depression Detection is the problem of identifying signs of depression in individuals. These signs might be identified in peoples’ speech, facial expressions and in the use of language.
Benchmarks
These leaderboards are used to track progress in Depression Detection
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Datasets
SMHD
Perla Dataset
Latest papers
Multi Class Depression Detection Through Tweets using Artificial Intelligence
About 4. 89 billion individuals are social media users.
Detecting mental disorder on social media: a ChatGPT-augmented explainable approach
In the digital era, the prevalence of depressive symptoms expressed on social media has raised serious concerns, necessitating advanced methodologies for timely detection.
Reading Between the Frames: Multi-Modal Depression Detection in Videos from Non-Verbal Cues
Depression, a prominent contributor to global disability, affects a substantial portion of the population.
DeepLearningBrasil@LT-EDI-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text
eduagarcia/depsign-2023-ranlp
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In this paper, we delineate the strategy employed by our team, DeepLearningBrasil, which secured us the first place in the shared task DepSign-LT-EDI@RANLP-2023, achieving a 47. 0% Macro F1-Score and a notable 2. 4% advantage.
Attention-Based Acoustic Feature Fusion Network for Depression Detection
To rectify this, we present the novel Attention-Based Acoustic Feature Fusion Network (ABAFnet) for depression detection.
Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews
idiap/Node_weighted_GCN_for_depression_detection
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We propose a simple approach for weighting self-connecting edges in a Graph Convolutional Network (GCN) and show its impact on depression detection from transcribed clinical interviews.
A Framework for Identifying Depression on Social Media: MentalRiskES@IberLEF 2023
simonsanvil/earlydepression-mentalriskes
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This paper describes our participation in the MentalRiskES task at IberLEF 2023.
Non-uniform Speaker Disentanglement For Depression Detection From Raw Speech Signals
We find that a greater adversarial weight for the initial layers leads to performance improvement.
Detection of depression on social networks using transformers and ensembles
As the impact of technology on our lives is increasing, we witness increased use of social media that became an essential tool not only for communication but also for sharing information with community about our thoughts and feelings.
It's Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers
cosmaadrian/time-enriched-multimodal-depression-detection
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In this work, we propose a flexible time-enriched multimodal transformer architecture for detecting depression from social media posts, using pretrained models for extracting image and text embeddings.
