{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/depression-detection/papers/2","list_of":"/task/depression-detection","task":"Depression Detection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,157],"of":157,"counts":{"archive_papers_tagged":157,"with_a_code_link":39,"where_syntology_ran_a_sample":1,"not_listed_spam_title":0,"listed":157,"listed_where_code_ran":1,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":0,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":0,"listed_every_run_a_failure_of_syntologys_instrument":1,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/depression-detection","prev":"/task/depression-detection","next":null,"papers":[{"url":null,"slug":"self-supervised-representations-in-speech","title":"Self-supervised representations in speech-based depression detection","date":"2023-05-20","arxiv_id":"2305.12263","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-symptoms-and-how-long-an-interpretable","title":"What Symptoms and How Long? An Interpretable AI Approach for Depression Detection in Social Media","date":"2023-05-18","arxiv_id":"2305.13127","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-graph-representation-learning-for","title":"Dynamic Graph Representation Learning for Depression Screening with Transformer","date":"2023-05-10","arxiv_id":"2305.06447","repositories_listed":0,"syntology":null},{"url":null,"slug":"read-diagnose-and-chat-towards-explainable","title":"Read, Diagnose and Chat: Towards Explainable and Interactive LLMs-Augmented Depression Detection in Social Media","date":"2023-05-09","arxiv_id":"2305.05138","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-chatgpt-for-nlp-based-mental","title":"Evaluation of ChatGPT for NLP-based Mental Health Applications","date":"2023-03-28","arxiv_id":"2303.15727","repositories_listed":0,"syntology":null},{"url":null,"slug":"depression-detection-in-social-media-posts","title":"Depression detection in social media posts using affective and social norm features","date":"2023-03-24","arxiv_id":"2303.14279","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-for-real-time-deployment-of","title":"Transfer Learning for Real-time Deployment of a Screening Tool for Depression Detection Using Actigraphy","date":"2023-03-14","arxiv_id":"2303.07847","repositories_listed":0,"syntology":null},{"url":null,"slug":"depression-detection-using-digital-traces-on","title":"Depression Detection Using Digital Traces on Social Media: A Knowledge-aware Deep Learning Approach","date":"2023-03-06","arxiv_id":"2303.05389","repositories_listed":0,"syntology":null},{"url":null,"slug":"handwriting-and-drawing-for-depression","title":"Handwriting and Drawing for Depression Detection: A Preliminary Study","date":"2023-02-05","arxiv_id":"2302.02499","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-algorithms-for-depression","title":"Machine Learning Algorithms for Depression Detection and Their Comparison","date":"2023-01-09","arxiv_id":"2301.03222","repositories_listed":0,"syntology":null},{"url":null,"slug":"care-for-the-mind-amid-chronic-diseases-an","title":"Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT","date":"2022-11-08","arxiv_id":"2211.04509","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-temporal-modelling-of-clinical","title":"Deep Temporal Modelling of Clinical Depression through Social Media Text","date":"2022-10-28","arxiv_id":"2211.07717","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-attention-network-for","title":"Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings","date":"2022-09-15","arxiv_id":"2209.07494","repositories_listed":0,"syntology":null},{"url":null,"slug":"depression-symptoms-modelling-from-social","title":"Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach","date":"2022-09-06","arxiv_id":"2209.02765","repositories_listed":0,"syntology":null},{"url":null,"slug":"sercnn-stacked-embedding-recurrent-1","title":"SERCNN: Stacked Embedding Recurrent Convolutional Neural Network in Detecting Depression on Twitter","date":"2022-07-29","arxiv_id":"2207.14535","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-instance-discriminative-learning","title":"Unsupervised Instance Discriminative Learning for Depression Detection from Speech Signals","date":"2022-06-27","arxiv_id":"2206.13016","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-step-towards-preserving-speakers-identity","title":"A Step Towards Preserving Speakers' Identity While Detecting Depression Via Speaker Disentanglement","date":"2022-06-20","arxiv_id":"2206.09530","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-mental-health-forums-for-user","title":"Leveraging Mental Health Forums for User-level Depression Detection on Social Media","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-networks-with-different-initialization","title":"Neural Networks with Different Initialization Methods for Depression Detection","date":"2022-05-10","arxiv_id":"2205.04792","repositories_listed":0,"syntology":null},{"url":null,"slug":"ssn-mlrg3-lt-edi-acl2022-depression-detection","title":"SSN_MLRG3 @LT-EDI-ACL2022-Depression Detection System from Social Media Text using Transformer Models","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"climate-and-weather-inspecting-depression","title":"Climate and Weather: Inspecting Depression Detection via Emotion Recognition","date":"2022-04-29","arxiv_id":"2204.14099","repositories_listed":0,"syntology":null},{"url":null,"slug":"facial-expression-analysis-using-decomposed","title":"Facial Expression Analysis Using Decomposed Multiscale Spatiotemporal Networks","date":"2022-03-21","arxiv_id":"2203.11111","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-depression-detection-via-learning","title":"Automatic Depression Detection via Learning and Fusing Features from Visual Cues","date":"2022-03-01","arxiv_id":"2203.00304","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-of-depression-severity-based-on","title":"Prediction of Depression Severity Based on the Prosodic and Semantic Features with Bidirectional LSTM and Time Distributed CNN","date":"2022-02-25","arxiv_id":"2202.12456","repositories_listed":0,"syntology":null},{"url":null,"slug":"fraug-a-frame-rate-based-data-augmentation","title":"FrAUG: A Frame Rate Based Data Augmentation Method for Depression Detection from Speech Signals","date":"2022-02-11","arxiv_id":"2202.05912","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-mental-health-classifier","title":"Improving Mental Health Classifier Generalization with Pre-Diagnosis Data","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-for-depression-detection","title":"Data Augmentation for Depression Detection Using Skeleton-Based Gait Information","date":"2022-01-04","arxiv_id":"2201.01115","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-approach-for-depression","title":"Machine Learning Approach for Depression Detection in Japanese","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sercnn-stacked-embedding-recurrent","title":"SERCNN: Stacked Embedding Recurrent Convolutional Neural Network in Depression Detection on Twitter","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"significance-of-speaker-embeddings-and","title":"Significance of Speaker Embeddings and Temporal Context for Depression Detection","date":"2021-07-24","arxiv_id":"2107.13969","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpreting-depression-from-question-wise","title":"Interpreting Depression From Question-wise Long-term Video Recording of SDS Evaluation","date":"2021-06-25","arxiv_id":"2106.13393","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-exploratory-analysis-of-the-relation","title":"An Exploratory Analysis of the Relation Between Offensive Language and Mental Health","date":"2021-05-31","arxiv_id":"2105.14888","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-depression-recognition-with","title":"Deep Learning for Depression Recognition with Audiovisual Cues: A Review","date":"2021-05-27","arxiv_id":"2106.00610","repositories_listed":0,"syntology":null},{"url":null,"slug":"depressionnet-a-novel-summarization-boosted","title":"DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media","date":"2021-05-23","arxiv_id":"2105.10878","repositories_listed":0,"syntology":null},{"url":null,"slug":"social-behaviour-understanding-using-deep","title":"Social Behaviour Understanding using Deep Neural Networks: Development of Social Intelligence Systems","date":"2021-05-20","arxiv_id":"2105.09489","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-bag-of-sub-emotions-for-depression","title":"Deep Bag-of-Sub-Emotions for Depression Detection in Social Media","date":"2021-03-01","arxiv_id":"2103.01334","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multi-task-learning-for-depression","title":"Deep Depression Prediction on Longitudinal Data via Joint Anomaly Ranking and Classification","date":"2020-12-05","arxiv_id":"2012.02950","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-topic-enriched-auxiliary-learning","title":"Multimodal Topic-Enriched Auxiliary Learning for Depression Detection","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-sentiment-analysis-engine-for","title":"A Novel Sentiment Analysis Engine for Preliminary Depression Status Estimation on Social Media","date":"2020-11-29","arxiv_id":"2011.14280","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-generalized-models-for","title":"Generalized Dilated CNN Models for Depression Detection Using Inverted Vocal Tract Variables","date":"2020-11-13","arxiv_id":"2011.06739","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-depressive-symptoms-from-tweets","title":"Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework","date":"2020-11-12","arxiv_id":"2011.06149","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-early-onset-of-depression-from","title":"Detecting Early Onset of Depression from Social Media Text using Learned Confidence Scores","date":"2020-11-03","arxiv_id":"2011.01695","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multitask-deep-learning-approach-for-user","title":"A Multitask Deep Learning Approach for User Depression Detection on Sina Weibo","date":"2020-08-26","arxiv_id":"2008.11708","repositories_listed":0,"syntology":null},{"url":null,"slug":"depression-detection-with-multi-modalities","title":"Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media","date":"2020-07-03","arxiv_id":"2007.02847","repositories_listed":0,"syntology":null},{"url":null,"slug":"author2vec-a-framework-for-generating-user","title":"Author2Vec: A Framework for Generating User Embedding","date":"2020-03-17","arxiv_id":"2003.11627","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-based-approach-for","title":"Machine Learning-based Approach for Depression Detection in Twitter Using Content and Activity Features","date":"2020-03-09","arxiv_id":"2003.04763","repositories_listed":0,"syntology":null},{"url":null,"slug":"depression-detection-using-resting-state","title":"Depression Detection using Resting State Three-channel EEG Signal","date":"2020-02-27","arxiv_id":"2002.09175","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-speech-based-screening-of","title":"Automated speech-based screening of depression using deep convolutional neural networks","date":"2019-12-02","arxiv_id":"1912.01115","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-approaches-for-detecting-the","title":"Machine Learning Approaches for Detecting the Depression from Resting-State Electroencephalogram (EEG): A Review Study","date":"2019-09-06","arxiv_id":"1909.03115","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-depression-in-social-media-using","title":"Detecting Depression in Social Media using Fine-Grained Emotions","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-attention-network-interpretable","title":"Feature Attention Network: Interpretable Depression Detection from Social Media","date":"2018-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-levels-of-depression-in-text-based","title":"Detecting Levels of Depression in Text Based on Metrics","date":"2018-07-09","arxiv_id":"1807.03397","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-linguistically-informed-fusion-approach-for","title":"A Linguistically-Informed Fusion Approach for Multimodal Depression Detection","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-depression-detection-of","title":"Deep Learning for Depression Detection of Twitter Users","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"early-text-classification-using-multi","title":"Early Text Classification Using Multi-Resolution Concept Representations","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"topic-modeling-based-multi-modal-depression","title":"Topic Modeling Based Multi-modal Depression Detection","date":"2018-03-28","arxiv_id":"1803.10384","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-cross-modal-review-of-indicators-for","title":"A Cross-modal Review of Indicators for Depression Detection Systems","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"0bb82a83b36f23c42d37e7a87ce164143ad6255e2517f65bcaf5dc3e7353b2a2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}