Table 2.

Summary of machine learning studies of detection of depression using text data from social media.

Reference	Sample	Platform	Outcome	Depression identification method	MLa approach type	Features examined	Cross- validation	Type of study
Wang et al [21], 2013	122 depressed and 346 nondepressed subjects, the ages of the samples were not reported	Sina microblog	Bayes: mean absolute error=0.186, ROCb=0.908, F-measure=0.85; Trees: mean absolute error=0.239, ROC=0.798, F-measure=0.762; Rules: mean absolute error=0.269, ROC=0.869, F-measure=0.812	Researcher-inferred	3 classification approaches: Bayes, trees and rules	Ten features from three dimensions, including microblog content, interactions, and behaviors. Four of the ten features, (1st person singular, 1st person plural, positive emoticons, and negative emoticons) pertain to microblog content, while three pertain to interactions (mentioning, [being] forwarding, and commenting), and two pertain to behaviors (original blogs and blogs posted between midnight and 6:00 am).	10-fold cross-validation	Observational cohort study
Burdisso et al [14], 2019	486 training subjects (83 depressed/403 nondepressed); 401 test subjects (52 depressed/349 nondepressed), the ages of the samples were not reported	Reddit	SS3c: F-measure=0.61, precision=0.63, recall=0.60	User-declared	The proposed model: SS3	Words in users’ online text posts on Reddit	4-fold cross-validation	Observational cohort study
Nguyen et al [22], 2014	5000 posts made by users from clinical communities and 5000 posts from control communities, the ages of the samples were not reported	LiveJournal	Lasso to classify communities (Accuracy): ANEWd=0.89, mood=0.96, topic=1, LIWCe=1; Lasso to classify posts (Accuracy): topic=0.93, LIWC=0.88	Community membership-based	The Lasso model	Affective features, mood tags, features topics from the LIWC, all extracted from posts on LiveJournal.	10-fold cross-validation	Observational cohort study
Fatima et al [23], 2018	4026 posts (2019/2007) from depressive and non-depressive communities, the ages of the samples were not reported	LiveJournal	The proposed RFf-based model (Accuracy): post=0.898, community=0.950, depression degree=0.923; SVMg (Accuracy): post=0.8, community=0.895	Community membership-based	Random forest, SVM	The values of the feature set serve as inputs to the classification algorithm, being extracted from first person singular, positive emotion, negative emotion, anxiety, cognitive process, insight, cause, affiliation health, and informal language of online text.	10-fold cross-validation	Observational cohort study
Tung & Lu [15], 2016	724 posts, the ages of the samples were not reported	PTTh	EDDTWi: precision=0.593, recall=0.668, F-measure=0.624	Researcher-inferred	EDDTW	Negative emotion lexicon, negative thought lexicon, negative event lexicon, and symptom lexicon.	10-fold cross-validation	Observational cohort study
Husseini Orabi et al [24], 2018	154 subjects (53 labeled as Depressed/101 labeled as Control), the ages of the samples were not reported	Twitter	The optimized CNNj model: accuracy=0.880	User-declared	CNN-based models, RNNk-based models, SVM	Twitter texts from among which all the @mentions, retweets, nonalphanumeric characters, and URLs were extracted by the researchers.	5-fold cross-validation	Observational cohort study
Islam et al [19], 2018	7145 Facebook comments (58% depressed/42% nondepressed), the ages of the samples were not reported	Facebook	Decision Tree (F-measure): emotional process=0.73, linguistic style=0.73, temporal process=0.73, all features=0.73; SVM (F-measure): emotional process=0.73, linguistic style=0.73, temporal process=0.73, all features=0.73; KNNl (F-measure): emotional process=0.71, linguistic style=0.70, temporal process=0.70, all features=0.67; Ensemble (F-measure): emotional process=0.73, linguistic style=0.73, temporal process=0.73, all features=0.73	User-declared	SVM, decision tree, ensemble, KNN	Emotional information (positive, negative, anxiety, anger, and sad), linguistic style (prepositions, articles, personal, conjunctions, auxiliary verbs), temporal process information (past, present, and future)	10-fold cross-validation	Observational cohort study
Shen et al [6], 2017	1402 depressed users, 36993 depression-candidate users, and over 300 million nondepressed users, the ages of the samples were not reported	Twitter	Accuracy: NBm=0.73, MSNLn=0.83, WDLo=0.77, MDLp=0.85	User-declared	MDL, NB, MSNL, WDL	Features of network interactions (number of tweets, social interactions, and posting behaviors), user profiles (users’ personal information in social networks), and visual, emotional, and topic-level features, domain-specific features	5-fold cross-validation	Observational cohort study
De Choudhury, Gamon, et al [25], 2013	476 users (171 depressed/305 nondepressed), with a median age of 25	Twitter	Accuracy: engagement=0.553, ego-network=0.612, emotion=0.643, linguistic style=0.684, depression language=0.692, demographics=0.513, all features=0.712	Researcher-inferred	SVM	Engagement, egocentric social graph, emotion, linguistic style, depression language, demographics	10-fold cross-validation	Observational cohort study
Mariñelarena-dondena et al [26], 2017	135 articles (20 depressed/115 nondepressed), the ages of the samples were not reported	Reddit	Precision=0.850, recall=0.810, F-measure=0.829, accuracy=0.948	User-declared	SVD, GBMq, SMOTEr	n-grams, use of which can create a large feature space and hold much important information	Not reported	Observational cohort study
Tsugawa et al [27], 2015	209 Japanese users (81 depressed/128 nondepressed), and users were aged 16-55, with a median age of 28.8 years	Twitter	Precision=0.61, recall=0.37, F-measure=0.46, accuracy=0.66	Researcher-inferred	LDAs, SVM	Frequencies of words used in the tweet, ratio of tweet topics found by LDA, ratio of positive-affect words contained in the tweet, ratio of negative-affect words contained in the tweet, hourly posting frequency, tweets per day, average number of words per tweet, overall retweet rate, overall mention rate, ratio of tweets containing a URL, number of users following, number of users followed	10-fold cross-validation	Observational cohort study
Chen et al [28], 2018	446 perinatal users, the ages of the samples were not reported	WeChat circle of friends	The result of LSTMw was similar to EPDSx	Researcher-inferred	LSTM	Top 10 emotions in the data set	Not reported	Observational cohort study
De Choudhury, Counts, et al [29], 2013	489 users, with a median age of 25 years	Twitter	Accuracy: eng.+ego=0.593, n-grams=0.600, style=0.658, emo.+ time=0.686, all features=0.701	Researcher-inferred	PCAt, SVM	Postcentric features (emotion, time, linguistic style, n-grams), user-centric features (engagement, ego-network)	5-fold cross-validation	Observational cohort study
Dinkel et al [30], 2019	142 speakers (42 depressed/100 nondepressed), the ages of the samples were not reported	Distress Analysis Interview Corpus-Wizard of Oz (WOZ-DAIC) database	Precision=0.93, recall=0.83, F-measure=0.87	Researcher-inferred	LSTM	Words from online posts	10-fold cross-validation	Observational cohort study
Sadeque et al [7], 2017	888 users (136 depressed/752 nondepressed), the ages of the samples were not reported	Reddit	F-measure: LibSVMu=0.40, WekaSVMv=0.30, RNN=0.34, Ensemble=0.45	User-declared	LibSVM, RNN, Ensemble, WekaSVM	Depression lexicony, metamap featuresz	5-fold cross-validation	Observational cohort study
Shatte et al [31], 2020	365 fathers in the perinatal period, the ages of the samples were not reported	Reddit	Precision=0.67, recall=0.68, F-measure=0.67, accuracy=0.66	Researcher-inferred	SVM	Fathers’ behaviors, emotions, linguistic style, and discussion topics	10-fold cross-validation	Observational cohort study
Li et al [32], 2020	1,410,651 users, the ages of the samples were not reported	Twitter	Accuracy:SVM (radial basis function kernel)=0.82, SVM (linear kernel)=0.87, logistic regression=0.86, naïve Bayes=0.81, simple neural network=0.87	Researcher-inferred	SVM, logistic regression, naïve Bayes Classifier, simple neural network	512 features that were extracted from tweets using a universal sentence encoder	Not reported	Observational cohort study

^aML: machine learning.

^bROC: receiver operating characteristic.

^cSS3: sequential S3 (smoothness, significance, and sanction).

^dANEW: affective norms for English words.

^eLIWC: linguistic inquiry and word count.

^fRF: random forest.

^gSVM: support vector machine.

^hPTT: the gossip forum on the Professional Technology Temple.

ⁱEDDTW: event-driven depression tendency warning.

^jCNN: convolutional neural networks.

^kRNN: recurrent neural network.

^lKNN: k-nearest neighbor.

^mNB: naive Bayesian.

ⁿMSNL: multiple social networking learning.

^oWDL: Wasserstein Dictionary Learning.

^pMDL: multimodal depressive dictionary learning.

^qGBM: gradient boosting machine.

^rSMOTE: synthetic minority oversampling technique.

^sLDA: latent Dirichlet allocation.

^tPCA: principal component analysis.

^uLibSVM: library for support vector machines.

^vWekaSVM: Waikato Environment for Knowledge Analysis for support vector machines.

^wLSTM: long short-term memory.

^xEPDS: Edinburgh Postnatal Depression Scale.

^yA cluster of unigrams that has a great likelihood of appearing in depression-related posts.

^zThe features were extracted using Metamap based on concepts from the Unified Medical Language System Metathesaurus.