Table 1. Summary of literature works on depression detection in online social networks.

Study	Data Source	Methods	Results	Limitations
AlSagri & Ykhlef (2020)	Twitter	Naïve Bayes, Support Vector Machines, Decision Tree	Accuracy: 0.825 Precision: 0.739 Sensitivity: 0.850 F-measure: 0.791 AUC: 0.780	Utilizing the different kernels in SVM Not avoiding overfitting dataset Lack of interpretability and comprehensibility
Kim et al. (2020)	Reddit	XGBoost, Convolutional Neural Network	Accuracy: 0.751 Precision: 0.891 Sensitivity: 0.718 F-measure: 0.795	Low success rate Lack of explainability Limited scope
Fatima et al. (2018)	LiveJournal	Random Forest	Accuracy: 0.918	Small size of data Single machine-learning algorithm Absence of other evaluation metrics Inefficiency in unbalanced dataset
Orabi et al. (2018)	Twitter	Convolutional Neural Network, Bidirectional LSTM	Accuracy: 0.850	Small size of data Lack of interpretability and comprehensibility Hardness task of exercising for RNN Report and Slope disappearing problems
De Choudhury et al. (2013)	Twitter	Support Vector Machines	Accuracy: 0.700	Low success rate Lack of explainability Single machine-learning algorithm Inappropriateness for large data sets
Thorstad & Wolff (2019)	Reddit	Cluster Analysis, Logistic Regression	Accuracy: = 0.390 F-measure = 0.380	Low performance Lack of interpretability
Nadeem (2016)	Twitter	Support Vector Machines, Decision Tree, Naïve Bayes, Logistic Regression	Accuracy: 0.860 Sensitivity: 0.830 F-measure: 0.840	Usage of the old dataset Emphasis on user confession Lack of interpretability
Aldarwish & Ahmad (2017)	Facebook, Twitter, and LiveJournal	Support Vector Machines, Naïve Bayes,	Accuracy: 0.633 Sensitivity: 0.570	Usage of old Arabic dataset Limited phrases and sentences Low success rate
Gaikar et al. (2019)	Twitter	Support Vector Machines–Naïve Bayes hybrid model	Accuracy: 0.850	High computational complexity for comparing the long-short snippets. Need for determining the appropriate values for combined methods Lack of interpretability
Islam et al. (2018)	Facebook	Support Vector Machines and LIWC	Accuracy: 0.700	Inappropriateness for large data sets Lack of interpretability
Wang et al. (2018)	Reddit	Convolutional Neural Network	F-measure: 0.670	Not encrypting the situation and alignment of an entity Lack of interpretability
Burdisso, Errecalde & MontesyGómez (2019)	Reddit	SS3	F-measure: 0.610 Precision: 0.630 Sensitivity: 0.600	Small size of data Low performance Lack of interpretability
Adarsh et al. (2023)	Reddit	One-shot Decision, Combining of SVM and KNN	Accuracy: 0.981 Precision: 0.968 Sensitivity: 0.976 F-measure: 0.973 AUC: 0.979	Lack of handling multiclass depression classification Requiring too many parameters that should be adjusted a priori
Gupta, Pokhriyal & Gola (2022)	Reddit	Combining Convolutional Neural Network and LSTM	Accuracy: 0.940 Precision: 0.942 Sensitivity: 0.937 F-measure: 0.940	Necessity of many values that should be adjusted a priori for the combined methods Lack of explainability Inappropriateness for unbalanced data sets
Chen et al. (2023)	Reddit	Combining Convolutional Neural Network and SBERT	Accuracy: 0.860 Precision: 0.850 Sensitivity: 0.870 F-measure: 0.860	High computational cost Lack of explainability Requiring too many parameters