Table 9.

Summary of research methodologies employed in highly cited publications.

Data Source	Reference	Mental Health	Data Description	Machine learning
				Model	Feature	Output	Annotation	Results
Twitter	Budenz et al [28]	Mental illness, bipolar disorder	1,270,902 tweets including bipolar or mental health-related words	Logistic regression	Term frequency-inverse document frequency	Related to mental illness or bipolar disorder	Manually annotated 2047 tweets with topic, stigma, and social support messaging	10-fold cross validation (AUCa=0.83)
Twitter	Du et al [29]	Suicide	1,962,766 tweets including 21 suicide-related keywords/phrases	CNNb, SVMc, extra trees, random forest, logistic regression, Bi-LSTMd	One-hot-vector mapped to pretrained GloVe Twitter embedding	Related to suicide	Manually annotated 3263 tweets and trained classifier to select 3000 additional suicide-related tweets	Accuracy 0.74, recall 0.96, precision 0.78, F1 0.83
Facebook, Twitter	Guntuku et al [30]	Psychological stress	601 users’ Facebook and Twitter posts	Linear regression with several regularization methods (eg, ridge, elastic-net, LASSOe and L2 penalized SVMs)	LIWCf, latent Dirichlet allocation topic modeling, stress lexicon, user engagement	Stress	Qualtrics survey; fill out the demographic questions and Cohen 10-item Stress Scale	5-fold cross-validation (Pearson r=0.24); trained on Facebook and Twitter, tested on Twitter
Facebook, Instagram	Shuai et al [31]	Social network mental disorders (eg, cyber-relationship addiction, information overload, and net compulsion)	3126 users' Instagram and Facebook data	Decision tree learning, SVM, logistic regression, DTSVMg, SNMDDh (newly proposed model; tensor technique for deriving latent features)	Social interaction, personal profile, duration	Social network mental disorders	MTurk survey - fill out the standard social network mental disorder questionnaires; professional psychiatrists labeled users having a social network mental disorder	5-fold cross validation (accuracy0.78 for Instagram and 0.83 for Facebook)
Instagram	Reece and Danforth [32]	Depression	43,950 users’ Instagram images	Random forest classifier	Number of comments and likes, number of faces in photograph, 3 color properties (hue, saturation, value)	Depression	MTurk survey; Center for Epidemiologic Studies Depression Scale to measure depression level	Recall: 0.697; precision: 0.604; F1: 0.647
Weibo	Lin et al [33]	Stress	1 billion Weibo posts	SVM, softmax regression, gradient-boosted decision tree, LASSO-MTLi, L2-MTLj, cASO-MTLk	CNN features or word vector representations with hand-crafted features	12 stressor events (eg, marriage, financial, illness, school), 6 stressor subjects	30 volunteers manually annotated the stressor events and subjects	10-fold cross validation (F1>0.80)
Weibo	Cheng et al [34]	Suicide risk, depression, anxiety, stress	974 participants’ Weibo posts, suicide probability, Weibo suicide communication (WSC), depression, anxiety, and stress.	SVM	Simplified Chinese-LIWC	Suicide risk, emotional distress (depression, anxiety, stress), WSC	Survey and psychological test tools (ie, Suicide Probability Scale, Depression Anxiety Stress Scales-21)	leave-one-out cross-validation: suicide probability (AUC=0.61, P=.04), severe anxiety (AUC=0.75, P<.001)
Reddit	Gkotsis et al [35]	Bipolar, schizophrenia, anxiety, depression, self-harm, suicide watch, addiction, crippling alcoholism, opiates, autism	1,014,660 posts	CNN, FFl, linear regression, SVM	Word vector representation (16 vector size)	Mental health	N/Am	Accuracy: 91.8% (binary classification task), 79.8% (multiclass classification task)
Facebook, Twitter, Instagram, Reddit	Coppersmith et al [36]	Suicide risk	197,615 posts from 418 users	LSTM with attention	One-hot-vector mapped to pretrained GloVe	Suicide risk	Examining public self-stated data and using data donated through OurDataHelps.org	10-fold cross validation (AUC=0.94)
Online Community - Live Journal	Saha et al [37]	Mental health	620,060 posts from 78,647 users	MTL	Linguistic features of LIWC; topics by LDAn	Mental health subreddit (eg, Abuse, Anorexia, Anxiety, Bipolar disorder, Cutting, Death, Drugs, Eating disorders, Insomnia, Pain, Self-injury, and Suicide)	N/A	AUC=0.94 with the community on eating disorders

^aAUC: area under the curve.

^bCNN: convolutional neural network.

^cSVM: support vector machine.

^dBi-LSTM: bidirectional long short-term memory.

^eLASSO: least absolute shrinkage and selection operator.

^fLIWC: Linguistic Inquiry and Word Count.

^gDTSVM: decision tree support vector machine.

^hSNMDD: social network mental disorder detection.

ⁱMTL: multitask learning.

^jl2-MTL: multitask learning considering l2 loss.

^kcASO-MTL: clustered alternating structure optimization multitask learning.

^lFF: feed-forward.

^mN/A: not applicable.

ⁿLDA: latent Dirichlet allocation.