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Machine Learning-Based Approach for Depression Detection in Twitter Using Content and Activity Features
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Mental Illness and Suicide
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A Depression Detection Model Based on Sentiment Analysis in Micro-blog Social Network
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Top 10 algorithms in data mining
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An experimental comparison of performance measures for classification
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The use of the area under the ROC curve in the evaluation of machine learning algorithms
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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
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Forecasting the onset and course of mental illness with Twitter data
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Echo Chambers: Emotional Contagion and Group Polarization on Facebook
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An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods
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On estimating depressive tendencies of Twitter users utilizing their tweet data
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The relationship between Precision-Recall and ROC curves
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Recognizing Depression from Twitter Activity
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Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media
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Statistical Pattern Recognition
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Twitter: A Good Place to Detect Health Conditions
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Predicting Depression via Social Media
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Measuring Post Traumatic Stress Disorder in Twitter
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topicmodels : An R Package for Fitting Topic Models
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Monitoring Tweets for Depression to Detect At-risk Users
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Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution
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Quantifying Mental Health Signals in Twitter
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The University of Maryland CLPsych 2015 Shared Task System
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A Review of Machine Learning Algorithms for Text-Documents Classification
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Identifying Depression on Twitter
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Feature Studies to Inform the Classification of Depressive Symptoms from Twitter Data for Population Health
inferredcites →https://doi.org/10.48550/arxiv.1712.03538openalex-snapshot
Multi-Task Learning for Mental Health using Social Media Text
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Data mining: concepts and techniques
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Machine Learning Algorithms Applied with Questionnaire Dataset to Investigate Anxiety and Depression
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Exploring the Role of Conventional and Non-Conventional Technologies in Identifying MDD
inferred← citeshttps://doi.org/10.1109/ssd64182.2025.10989873openalex-snapshot
Depression Detection Using a Hybrid CNN-BiGRU-BiLSTM Model With BERT Embedding and Data Augmentation
inferred← citeshttps://doi.org/10.1109/taffc.2024.3521327openalex-snapshot
Speech-Based Depression Assessment: A Comprehensive Survey
inferred← citeshttps://doi.org/10.1109/taffc.2025.3571749openalex-snapshot
Detecting Signs of Depression Using Social Media Texts Through an Ensemble of Ensemble Classifiers
inferred← citeshttps://doi.org/10.1109/tcss.2023.3263128openalex-snapshot
Depression Detection From Social Networks Data Based on Machine Learning and Deep Learning Techniques: An Interrogative Survey
inferred← citeshttps://doi.org/10.1109/tnsre.2022.3224135openalex-snapshot
TAMFN: Time-Aware Attention Multimodal Fusion Network for Depression Detection
inferred← citeshttps://doi.org/10.1145/3569580openalex-snapshot
Depression Detection from Social Media Text Analysis using Natural Language Processing Techniques and Hybrid Deep Learning Model
inferred← citeshttps://doi.org/10.1155/2022/7893775openalex-snapshot
A Novel Text Mining Approach for Mental Health Prediction Using Bi-LSTM and BERT Model
inferred← citeshttps://doi.org/10.1155/2022/9655452openalex-snapshot
University Media Content Detection and Classification Based on Information Fusion Algorithm
inferred← citeshttps://doi.org/10.1155/int/6801530openalex-snapshot
Intelligent Internet of Medical Things for Depression: Current Advancements, Challenges, and Trends
inferred← citeshttps://doi.org/10.11591/eei.v12i2.4182openalex-snapshot
Depression detection in social media comments data using machine learning algorithms
inferred← citeshttps://doi.org/10.11591/eei.v12i6.5416openalex-snapshot
Predicating depression on Twitter using hybrid model BiLSTM-XGBOOST
inferred← citeshttps://doi.org/10.1162/coli_a_00479openalex-snapshot
Comparing Selective Masking Methods for Depression Detection in Social Media
inferred← citeshttps://doi.org/10.1177/0976030x221112529openalex-snapshot
Study of Early Risks of Depression by Analysing Social Media Posts
inferred← citeshttps://doi.org/10.1186/s12885-025-14650-7openalex-snapshot
Construction of a transfer learning-based depression detection model for female breast cancer patients: text sentiment analysis
inferred← citeshttps://doi.org/10.14569/ijacsa.2020.0110577openalex-snapshot
Quantifying Feature Importance for Detecting Depression using Random Forest
inferred← citeshttps://doi.org/10.14569/ijacsa.2022.0131158openalex-snapshot
An Automatic Adaptive Case-based Reasoning System for Depression Remedy Recommendation
inferred← citeshttps://doi.org/10.14716/ijtech.v14i6.6648openalex-snapshot
Pre- and Post-Depressive Detection using Deep Learning and Textual-based Features
inferred← citeshttps://doi.org/10.17762/ijritcc.v11i9.8325openalex-snapshot
Enhancing Mental Health Awareness through Twitter Analysis: A Comparative Study of Machine Learning and Hybrid Deep Learning Techniques
inferred← citeshttps://doi.org/10.18178/ijiet.2022.12.7.1668openalex-snapshot
Fuzzy Tsukamoto Implementation to Detect Physiological Condition on IoT-Based e-Learning Users
inferred← citeshttps://doi.org/10.18863/pgy.1749490openalex-snapshot
Artificial Intelligence–Based Clinical Assessment in Mood Disorders: A Narrative Review
inferred← citeshttps://doi.org/10.2196/49562openalex-snapshot
Identifying X (Formerly Twitter) Posts Relevant to Dementia and COVID-19: Machine Learning Approach
inferred← citeshttps://doi.org/10.2196/55067openalex-snapshot
Screening for Depression Using Natural Language Processing: Literature Review
inferred← citeshttps://doi.org/10.2196/59002openalex-snapshot
Text-Based Depression Prediction on Social Media Using Machine Learning: Systematic Review and Meta-Analysis
inferred← citeshttps://doi.org/10.22362/ijcert/2023/v10/i04/v10i0408openalex-snapshot
A Systematic Analysis of Text Classification Overfitting Recommendation Methods
inferred← citeshttps://doi.org/10.23919/indiacom54597.2022.9763147openalex-snapshot
A Review of Overfitting Solutions in Smart Depression Detection Models
inferred← citeshttps://doi.org/10.29207/resti.v6i4.4275openalex-snapshot
Depression Detection on Twitter Social Media Using Decision Tree
inferred← citeshttps://doi.org/10.31127/tuje.1763321openalex-snapshot
Feature Selection Approach to Optimize Depression Detection from EHR Data
inferred← citeshttps://doi.org/10.32604/cmc.2022.027058openalex-snapshot
Forecasting Mental Stress Using Machine Learning Algorithms
inferred← citeshttps://doi.org/10.3389/fpsyg.2021.802821openalex-snapshot
A Hybrid Feature Selection and Ensemble Approach to Identify Depressed Users in Online Social Media
inferred← citeshttps://doi.org/10.3389/fpsyt.2024.1280935openalex-snapshot
Diagnosing and tracking depression based on eye movement in response to virtual reality
inferred← citeshttps://doi.org/10.3389/frai.2023.1230649openalex-snapshot
Supervised machine learning models for depression sentiment analysis
inferred← citeshttps://doi.org/10.3389/frai.2026.1917139openalex-snapshot
Explainable detection of depression in Arabic tweets: identifying clinically meaningful linguistic markers using SHAP
inferred← citeshttps://doi.org/10.3390/app152011274openalex-snapshot
Temporal Modeling of Social Media for Depression Forecasting: Deep Learning Approaches with Pretrained Embeddings
inferred← citeshttps://doi.org/10.3390/diagnostics14010043openalex-snapshot
Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram
inferred← citeshttps://doi.org/10.3390/electronics11050676openalex-snapshot
RETRACTED: Deep Learning for Depression Detection from Textual Data
inferred← citeshttps://doi.org/10.3390/math12131926openalex-snapshot
Mental-Health: An NLP-Based System for Detecting Depression Levels through User Comments on Twitter (X)
inferred← citeshttps://doi.org/10.3390/math13182918openalex-snapshot
RAMHA: A Hybrid Social Text-Based Transformer with Adapter for Mental Health Emotion Classification
inferred← citeshttps://doi.org/10.3390/s24020348openalex-snapshot
Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches
inferred← citeshttps://doi.org/10.3902/jnns.29.78openalex-snapshot
Estimating Mental Health Using Human-generated Big Data and Machine Learning
inferred← citeshttps://doi.org/10.3991/ijoe.v18i04.29051openalex-snapshot
Detection of Depression Using Machine Learning Algorithms
inferred← citeshttps://doi.org/10.4018/978-1-6684-8531-6.ch012openalex-snapshot
Predicting Depression From Social Media Users by Using Lexicons and Machine Learning Algorithms
inferred← citeshttps://doi.org/10.4018/978-1-7998-9594-7.ch013openalex-snapshot
Depression Detection in Online Social Media Users Using Natural Language Processing Techniques
inferred← citeshttps://doi.org/10.4018/ijswis.342126openalex-snapshot
Machine Learning for Depression Detection on Web and Social Media
inferred← citeshttps://doi.org/10.4038/icter.v17i3.7282openalex-snapshot
A Machine Learning Approach for Depression Detection in Sinhala-English Code-Mixed
inferred← citeshttps://doi.org/10.4236/jdaip.2023.114022openalex-snapshot
A Stacking-Based Ensemble Approach with Embeddings from Language Models for Depression Detection from Social Media Text
inferred← citeshttps://doi.org/10.48084/etasr.13046openalex-snapshot
A High-Precision SVM Model for Depression Detection Among Farmers
inferred← citeshttps://doi.org/10.48175/ijarsct-18279openalex-snapshot
Depression Prediction using Machine Learning Algorithms
inferred← citeshttps://doi.org/10.48175/ijarsct-812openalex-snapshot
Detecting Depression in Social Media Using Sentiment Analysis
inferred← citeshttps://doi.org/10.51583/ijltemas.2025.1411000089openalex-snapshot
Evaluating Student Depression Detection from Social Media Post Using Multinomial Naïve Bayes Text Vectorization and Randomizedsearchcv
inferred← citeshttps://doi.org/10.52589/bjcnit-9lro2scjopenalex-snapshot
MindBridge: A Transformer-Based Conversational Agent for Real-Time Depression and Anxiety Detection and Support in University Students
inferred← citeshttps://doi.org/10.53759/7669/jmc202505037openalex-snapshot
Depression Classification using Bert Embedding Model on Social Media Posts
inferred← citeshttps://doi.org/10.5753/reic.2024.3430openalex-snapshot
Desenvolvimento de Ferramenta de Análise de Sentimentos para Identificação de Possíveis Sinais de Comportamento Depressivo na Rede Social Twitter
inferred← citeshttps://doi.org/10.59681/2175-4411.v16.iespecial.2024.1319openalex-snapshot
Características para detecção de depressão com dados do Twitter
inferred← citeshttps://doi.org/10.7287/peerj-cs.1070v0.1/reviews/1openalex-snapshot
Peer Review #1 of "An optimized deep learning approach for suicide detection through Arabic tweets (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1070v0.1/reviews/2openalex-snapshot
Peer Review #2 of "An optimized deep learning approach for suicide detection through Arabic tweets (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1070v0.2/reviews/1openalex-snapshot
Peer Review #1 of "An optimized deep learning approach for suicide detection through Arabic tweets (v0.2)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.1070v0.2/reviews/2openalex-snapshot
Peer Review #2 of "An optimized deep learning approach for suicide detection through Arabic tweets (v0.2)"
inferred← citeshttps://doi.org/10.7717/peerj-cs.1070openalex-snapshot
An optimized deep learning approach for suicide detection through Arabic tweets
inferred← citeshttps://doi.org/10.7717/peerj-cs.1661openalex-snapshot
New chaos-integrated improved grey wolf optimization based models for automatic detection of depression in online social media and networks
inferred← citeshttps://doi.org/10.7717/peerj-cs.2301openalex-snapshot
A novel multi-model feature generation technique for suicide detection
inferred← citeshttps://doi.org/10.7717/peerj-cs.2828openalex-snapshot
AI framework for DRIVE model based mental health detection in text: a case study on how coping strategies are expressed during COVID-19
inferred← citeshttps://doi.org/10.7717/peerj-cs.3045openalex-snapshot
Classification of psychiatry clinical notes by diagnosis: a deep learning and machine learning approach
inferred← citeshttps://doi.org/10.7717/peerj-cs.3559openalex-snapshot
Review of predictive techniques for detecting mental disorders from user-generated content on social media
inferred← citeshttps://doi.org/10.7759/s44389-026-00156-1openalex-snapshot
Depression Detection From Social Media Text Using Embedding-Enhanced Long Short-Term Memory and Recurrent Neural Network Models
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.