Depression is a pervasive mental health disorder that affects millions of individuals worldwide, often without being consciously acknowledged. It can lead to a significant diminishment of interest in everyday activities, potentially culminating in suicidal ideation. Traditionally, depression diagnosis is based on standardized clinical criteria, which encompasses current symptomatology and medical history (Smith et al., 2013). The integration of big data and machine learning algorithms presents an effective and efficient means of automating depression detection, providing valuable support to healthcare professionals and patients alike. At its core, classic depression detection constitutes a classification problem, wherein the distinction between healthy and depressed individuals or the prediction of severity is paramount (Dinkel et al., 2019).

Researchers have conducted an in-depth examination of behavioral indicators, encompassing facial expressions, speech patterns, and other multi-modal signals. For example, Shin et al. (2022) leveraged clinical interview transcripts to train a machine learning model for the detection of depression and suicidal risk. This study employed the Naive Bayes classifier, yielding an area under the curve (AUC) of 0.905, sensitivity of 0.699, and specificity of 0.964 for diagnosing depression. A study conducted by Hadžić et al. (2024) explored the potential of artificial intelligence, specifically machine learning, as a diagnostic tool for depression in clinical interviews. This investigation focused on the performance of a BERT-based natural language processing (NLP) model, which achieved an accuracy of 0.71. In contrast, an untrained GPT-3.5 model demonstrated superior performance, achieving an accuracy of 0.88.

The integration of social media into mental health studies presents a novel avenue for accessing data from a broader population beyond traditional clinical settings. This expansion enables researchers to gather insights from a wider subset of individuals, including those who may not have been previously accounted for in clinical studies (Cabral et al., 2024). Younger generations have been found to be more inclined to express suicidal ideation on social media, rather than disclosing it to healthcare professionals or family members (John et al., 2018). In contrast to clinical data, social media data offers several advantages, including ease of access, richer content, and lower concealment potential (Malhotra and Jindal, 2022). Several studies have demonstrated the potential of social media data in detecting mental health issues. For instance, Govindasamy and Palanichamy (2021) utilized Twitter data to develop a depression detection model, employing a combination of Naïve Bayes and NBTree classifiers to achieve optimal results. Ayyalasomayajula’s (2024) investigation into the impact of linguistic features on depression detection through Reddit posts yielded impressive outcomes, with an exceptional F₁-score achieved using a TF-IDF-based logistic regression model on the same dataset. This research highlights the potential of social media data in mental health studies, and its ability to outperform traditional methodologies. The findings of this study contribute to the growing body of evidence on the use of social media data in mental health research, and underscore the importance of exploring this avenue in future studies.

The application of deep learning technologies, excluding traditional machine learning approaches, has been increasingly employed in the detection of depression. Orabi et al. (2018) leveraged Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to identify individuals exhibiting signs of mental illness, utilizing limited amounts of unstructured data sourced from Twitter. Their CNNWithMax models demonstrated a higher accuracy of 87.957%, with optimized embedding, and were found to outperform RNN models in depression detection. In contrast, Mihov et al. (2022) utilized heterogeneous graph convolution to develop a depression detection model, MentalNet, by representing users’ social circles, including analysis of user interactions and the intimacy of user contacts. The framework achieved excellent performance on Twitter data. Liu (2024) integrated XLM-RoBERTa and TextGCN to propose a depression clinical detection model based on data from Twitter, Reddit, and Weibo. XLM-RoBERTa was employed to extract semantic insights from multilingual text, while TextGCN was leveraged to acquire knowledge of the multilingual text structure. The study demonstrated that the incorporation of TextGCN significantly enhanced the performance of the model. TextGCN represents a pioneering approach, wherein the entire corpus is represented as a heterogeneous graph, enabling the simultaneous construction of word and document representations via Graph Neural Networks (GNNs). This novel framework outperforms state-of-the-art text classification methods, without relying on pre-trained word embeddings or external knowledge, and has been successfully evaluated on a range of benchmark datasets. In contrast, prior studies (Lin et al., 2021; Han et al., 2022; Li et al., 2023) have sought to augment GCN models by incorporating pre-trained representations or integrating external knowledge.

Embeddings are numerical vector representations of text data, enabling efficient processing and analysis by computers. This technique is commonly employed in natural language processing tasks, such as sentiment analysis, and serves as a crucial component in deep learning methods. Embeddings have been developed in various forms, including character, word, and sentence levels, and have been widely explored in existing studies (Zhang and Han, 2025).

Research has demonstrated the potential of word embeddings in detecting depression on social media platforms, such as Reddit. A study by Lestandy and Abdurrahim (2023) investigated the impact of word embedding dimensions on the detection of depression using a Bidirectional Long Short-Term Memory (BiLSTM) model in conjunction with Word2Vec and GloVe methods. The findings highlight the efficacy of combining word embeddings with Recurrent Neural Network (RNN) technology for depression identification, with the Word2Vec approach yielding significant advantages. Couto et al. (2022) proposed a framework for early depression detection by analyzing changes in language use on social media, utilizing temporal word embeddings. The proposed method employed two temporal word embedding models: TWEC, based on word2vec, and DCWE, built on pre-trained language models like BERT. The experimental results demonstrated that the DCWE model outperformed most participants in the CLEF eRisk tasks of 2017 and 2018, achieving top performance in ERDE5 and ERDE50 metrics, and even surpassing state-of-the-art methods in some cases. In a study by Goswami et al. (2024) explored depression detection by combining embeddings extracted by BERT or RoBERTa with a classifier RNN, whose models achieved a validation accuracy of 99.9%. Hong et al. (2022) proposed a novel approach to node attributes within a Graph Neural Network (GNN)-based model, where node-specific embeddings are captured for each word in the vocabulary to measure the severity of depression symptoms. Xin et al. (2024) compared the suitability of various pre-trained language models, including BERT, MentalBERT, MentalLongformer, Llama 2-7B, and Llama 3-8B, across three different segmentations of interview transcripts. The findings revealed that LLM embeddings facilitated efficient classification of outcomes in qualitative studies on adolescent depression, and confirmed their potential in future detection and treatment of depression. Bucur’s (2024) study compared two transformer-based embedding methods, MentalRoBERTa and an MPNet variant, for representing Reddit social media posts and BDI-II questionnaire responses. The results showed that the model designed for semantic search performed better than the mental health pre-trained model in embedding tasks, highlighting the performance differences of various embedding methods in depression symptom detection.

Briefly, numerous studies explore the model integrating emotion representation and machine learning or deep learning technology for depression detection, achieving certain results. And GNN can improve the models’ ability of representation generation. In the study, we attempt to detect depression from different social media platforms and websites specifically designed to support patients with depression to further expand the scope of our model using; we apply the deep learning method that can use those measures for the detection of individuals who are suffering from depression; we propose a novel computational framework that can improve the accuracy for automatic depression detection.

We enhance the TextGCN (Liang et al., 2018) by incorporating emotion representation to investigate the trend of depression detection in social media. At its core, TextGCN posits that a text corpus can be effectively represented as a graph, G = ( V , E , A ) , where V comprises a set of word and document nodes. The edges E consist of word-word connections E w i w j , word-document relationships E w i d j , and document-document connections E d i d j . A ∈ R n × n is the graph adjacency matrix, representing the weights between nodes in the graph. Figure 1 illustrates the architecture of our depression detection classification model enhanced by emotion representation.

To inform our model, we leverage pre-trained models that have been domain-adapted or fine-tuned using mental health materials or other relevant information to generate emotion embeddings.

We construct a text graph, where nodes represent words and documents in the corpus, and edges represent their interconnections. Our fine-tuned pre-trained language model generates emotion representations as initial weights for the TextGCN model. The constructed graph is then passed through two layers of Graph Convolutional Networks (GCN) for depression detection tasks.

MentalBERT and MentalRoBERTa are pre-trained language models tailored variants of the BERT and RoBERTa architectures, respectively, specifically designed for processing mental health-related text data. Ji et al. (2021) employed a domain-adaptive pretraining approach, wherein they continued to fine-tune the models on a mental health corpus after initializing them with general pre-trained weights to adapt the models to the distinctive characteristics of mental health-related text. The mental health corpus utilized in this study comprises 13,671,785 sentences extracted from mental health-related posts on Reddit, sourced from various subreddits, such as “r/depression,” “r/SuicideWatch,” and “r/Anxiety.” The pre-training process was conducted using Huggingface’s Transformers library, with the Masked Language Modeling (MLM) task employed, consistent with BERT and RoBERTa, where MentalBERT utilized static masking, whereas MentalRoBERTa employed dynamic masking. The experimental results demonstrate that MentalBERT and MentalRoBERTa outperform baseline models across a range of mental health detection tasks, with notable superiority in depression detection.

Additionally, RoBERTaDepressionDetection (Hugging Face, 2025) is a fine-tuned variant of the twitter-roberta-base model, specifically designed for depression detection tasks. This model is initialized with the twitter-roberta-base model, a RoBERTa-base variant that has undergone extensive pre-training on approximately 58 million tweets. The model was further refined through fine-tuning by the developer, who utilized a corpus from the LT-EDI 2022 shared task, a dataset aimed at detecting depression through social media text analysis. In accordance with the task’s requirements, the model was trained to categorize text into three distinct classes: moderate depression, severe depression, and non-depression. The training dataset comprises 53,909 sentences, with 7,884 sentences classified as non-depression, 36,114 sentences categorized as moderate depression, and 9,911 sentences labeled as severe depression. The average document length for each class is 4, 6, and 11 sentences, respectively, with corresponding average sentence lengths of 78, 100, and 140 words. During the fine-tuning process, the developer employed a Multilayer Perceptron (MLP) as the classifier and utilized the Adam optimizer.

Compared to baseline models, MentalBERT, MentalRoBERTa, and RoBERTaDepressionDetection significant advantages in generating emotion representations. These models achieve superior performance in capturing domain-specific semantic features through domain-adaptive pretraining or task-specific fine-tuning, thereby enhancing their accuracy. MentalBERT and MentalRoBERTa demonstrate particular strength in producing embeddings that effectively represent domain-specific terminology, emotional expressions, and contextual subtleties. In contrast, RoBERTaDepressionDetection excels in decoding depression-related linguistic patterns, such as mood fluctuations and symptom descriptions, present in social media texts. The domain-specific training employed in these models enables them to adapt more robustly to nuanced mental health expressions, including self-negation and anxiety, as well as common noise patterns found in social media texts, such as abbreviations and informal syntax. Furthermore, the emotion representations generated by these models can be directly applied to downstream tasks with reduced fine-tuning requirements, and have been shown to outperform general models in mental health detection tasks.

We conduct an evaluation of the enhanced TextGCN model, which leverages emotion representation learned from fine-tuned pre-trained language models to perform depression detection in a post-based framework. This approach allows for the benefits of post-based classification to be realized without the necessity of incorporating social media components, such as historical posts. By constructing a graph of words and documents, we then pass it through a two-layer Graph Convolutional Network (GCN) designed for depression detection. Utilizing categorical cross-entropy as the loss function, we employ single-label classification to achieve accurate depression detection outcomes.

We employed a diverse set of publicly available datasets to assess the efficacy of our model. The statistics and distribution of categories for each dataset are summarized in Table 1, while Figure 2 provides a visual representation of the categorical distribution.

The Twitter depression dataset (MacAvaney et al., 2021), which served as the basis for the practice dataset for the CLPsych 2021 competition, was constructed by collecting tweets related to depression, removing hashtags, and annotating them using binary classes (depression, D, 26.34%, and non-depression, ND, 73.66%). We denoted this dataset as “Dataset 1” and the majority of the dataset consists of brief, emotive posts with emojis.

In addition to the Twitter depression dataset, we also utilized the multiple languages Twitter datasets proposed by Cha et al. (2022). These datasets comprise 921 k tweets from Korean users, 10 M tweets from English users, and 15 M tweets from Japanese users. Each language-specific dataset was labeled using a depression lexicon collected from previous studies on detecting depression on social media, with depression and non-depression categories assigned a binary classification (1 for depression, 0 for non-depression). In this study, we focused on the English-language dataset, which primarily consists of brief posts with emojis. This was denoted as “Dataset 2”.

Furthermore, we drew upon the Identifying-Depression Datasets collected by Pirina and Çöltekin (2024), which aggregated social media data from Reddit and web forums to identify depression. We annotated each post according to the labeling provided by the authors. The datasets were divided into three sub-datasets, denoted as “Dataset 3,” “Dataset 4,” and” “Dataset 5”. The “Dataset 3” and “Dataset 4” consisted of long posts with emojis, sourced from web forums and Reddit, respectively. The “Dataset 5” combined content from both sources, providing a comprehensive dataset for depression detection.

To assess the overal performance of our depression detection models, we employ four widely used evaluation metrics: Accuracy, Precision, Recall, and class F₁-score. These metrics provide a comprehensive evaluation of the models’ ability to correctly identify depressive cases.

Accuracy is a fundamental metric that quantified as the proportion of correctly predicted instances over the total number of instances. It is defined as follows (Equation 5):

where TP (true positive) represents the count of depressed users who are accurately identified. TN (true negative) signifies the count of non-depressed users who are accurately identified. FP (false positive) indicates the count of non-depressed users who are inaccurately identified. FN (false negative) denotes the count of depressed users who are inaccurately identified.

Precision is calculated as the ratio of true positives among all positive predictions. It is defined as follows (Equation 6):

A high precision indicates that the model produces fewer false positives, which is crucial in clinical applications where wrong detection could lead to unnecessary interventions.

Recall is defined as the proportion of true positives that are correctly identified. It measures the ability of the model to correctly identify all actual depressive cases. It is defined as follows (Equation 7):

A high recall value ensures that most depressive cases are correctly identified, ensuring that individuals in need receive appropriate attention.

The F₁-score, representing the harmonic mean of Precision and Recall, serves as a balanced measure of both, providing a holistic assessment of a model’s performance. It is defined as follows (Equation 8):

A higher F₁-score reflects a better trade-off between Precision and Recall, making it a valuable metric for evaluating depression detection models.

We propose an advanced TextGCN framework that leverages diverse emotion representations, derived from pre-trained language models, as input. To substantiate its efficacy, we conduct a comprehensive evaluation of its performance across five publicly available datasets in the depression domain, in comparison to the basic TextGCN with one-hot encoding. Figure 3 shows the diagram of this experiment.

In our experiments, we assess the performance of our proposed system in relation to five pre-trained language models (PLMs) that utilize transformation-based approaches. The BERT model (Devlin et al., 2018) is a transformer-based pre-trained language representation model that has achieved state-of-the-art results on numerous NLP tasks through its next sentence prediction and masked language modeling capabilities. RoBERTa (Liu et al., 2019), a variant of BERT, builds upon the same training setup but incorporates dynamic masking, expands the training dataset, and increases the batch size to enhance its performance. In contrast, MentalBERT, MentalRoBERTa, and RoBETaDepressionDetection are domain-specific pre-trained language models, tailored to the depression detection task, and are built upon either BERT or RoBERTa, respectively. These models have demonstrated improved performance in mental health detection tasks and depression detection tasks through fine-tuning on mental health-related data.

We employed a 80:20 train/test split to train our model, with a training duration of 200 epochs and an early stopping criterion of 10 epochs, utilizing the Adam optimizer for training. To further ensure the robustness and generalizability of the proposed method, we also conducted K-fold cross-validation, where the dataset was partitioned into K subsets and each subset was used as the test set exactly once. We specified the following hyperparameters: a hidden dimension size of 200, a dropout rate of 0.5, a learning rate of 0.02, and a configuration of two GCN layers. Hyperparameter tuning was conducted using Optuna, a hyperparameter optimization framework. The optimized hyperparameters included the number of hidden layers (L = {2, 3, 4, 5}), hidden layer dimensions (H = {100, 200, 300, 400, 500}), dropout rates (dr = {0.01, 0.05, 0.1, 0.5}), learning rates (lr = {0.01, 0.02, 0.03, 0.04, 0.05}), and weight decay values (wd = {0, 0.005, 0.05}). We acknowledge the potential for information leakage resulting from the inclusion of test nodes in the graph during training. This issue may inadvertently introduce indirect supervision signals and affect the validity of performance evaluation. In future work, we plan to explore alternative graph construction strategies or transductive-to-inductive adaptations to mitigate such leakage and ensure a more rigorous experimental setup.

Our model evaluation protocol involves a depression detection task, which serves as a benchmark for assessing the performance of our proposed approach. Table 2 provides a comparative analysis of our model’s results with the initial TextGCN model, which relies solely on a one-hot vector input without incorporating pre-trained emotion representations or external knowledge. For each evaluation metric (Accuracy, Precision, Recall, F₁-Score), p-values were calculated by paired t-tests comparing each optimized model to the baseline (One Hot). Significant improvements are highlighted where p < 0.05.

Firstly, pre-trained models have demonstrated superior performance compared to One-Hot encoding across various tasks, particularly in terms of recall and F₁-score metrics. In dataset 1, the RobertaDepressionDetection model achieved a statistically significant improvement of 24.162% in F₁-score and 0.787% in accuracy over One-Hot encoding, showcasing its enhanced semantic capture capabilities. Similarly, in dataset 3, the MentalRoBERTa model outperformed One-Hot encoding by 21.511% in F₁-score and 7.012% in accuracy, further substantiating its superiority in domain adaptation. In dataset 5, pre-trained models experienced some decline in performance, but MentalRoBERTa retained marginal gains, indicating its robustness in handling diverse datasets.

Secondly, Roberta has been found to outperform BERT in most tasks, with notable improvements in F₁-score and accuracy. In dataset 3, RoBERTa surpassed BERT by 3.328% in F₁-score and 5.875% in accuracy, highlighting its enhanced performance in specific tasks. Furthermore, in dataset 5, RoBERTa achieved a 8.6325% higher accuracy and a 5.416% higher F₁-score compared to BERT, validating its superiority across various datasets.

Thirdly, the domain-adaptive pre-trained models (MentalBERT and MentalRoBERTa) and the depression detection fine-tuned model (RobertaDepressionDetection) have demonstrated enhanced performance compared to their base counterparts (BERT/RoBERTa) in specific tasks.

MentalBERT has consistently outperformed the basic BERT model in mental health-related tasks, as evident in dataset 5, where it achieved a notable 5.068% accuracy boost and a 2.837% F₁-score enhancement. However, in dataset 1, MentalBERT’s accuracy was lower than BERT, indicating a trade-off between domain specialization and generalization. Conversely, MentalRoBERTa has consistently outperformed RoBERTa, as seen in dataset 3, where it achieved a 4.933% accuracy increase and a 3.421% F₁-score improvement. Additionally, RoBERTaDepressionDetection outperformed RoBERTa in dataset 3, with a 3.020% higher F₁-score, but only a 0.456% increase in accuracy in dataset 2, highlighting the dependence on data quality.

Additionally, as shown in Table 2, the optimized models consistently outperformed the baseline across all metrics. Statistically significant improvements were denoted by ^*p < 0.05, ^**p < 0.01, and ^***p < 0.001.

Table 3 provides the text length statistics for the five datasets, whereas Figure 4 visualizes the distribution of text lengths within these datasets. The average text length varies considerably across the datasets. Dataset 1 has a mean length of 96 words with a standard deviation of 53, ranging from 6 to 374 words. Dataset 2 shows a slightly longer average text length of 133 words (SD = 85), with lengths ranging from 4 to 327 words. In contrast, Datasets 3 to 5 contain substantially longer texts. Dataset 3 has a mean length of 1,101 words (SD = 817), with a minimum of 52 words and a maximum of 5,717. Dataset 4 exhibits a higher variability, with a mean of 1,123 words and a standard deviation of 1,328, spanning from 11 to 17,601 words. Dataset 5 is similar to Dataset 4, with a mean length of 1,133 words (SD = 1,148), and the same minimum and maximum text lengths ranging from 11 to 17,601.

These statistics indicate that the datasets differ significantly in text length, from short texts in Datasets 1 and 2 to much longer and more variable texts in Datasets 3 to 5. Consistent with the findings of Wongkoblap et al. (2021) models trained on longer posts (e.g., Dataset 3, Dataset 4, and Dataset 5) exhibited superior performance, likely attributable to richer contextual signals. Social media texts often convey nuanced expressions spanning multiple sentences, requiring the preservation of complete contextual integrity. Future research should explore hybrid methods to balance context preservation with computing efficiency.

In previous studies, emoticons were often regarded as noise and removed during mental health detection or emotion analysis (Maghraby and Ali, 2022; Samee et al., 2023). In this study, however, we preserve emoticons and emojis by treating them as individual tokens for contextualization. Meanwhile, emoticon recognition technology is emerging. For example, mapping emojis to affect-aware embeddings has shown significant potential for leveraging their affective value (Chen et al., 2018). Although this study did not further utilize emojis beyond tokenization, future research could incorporate recognition techniques to better exploit the affective information carried by emoticons in social media data, without introducing additional noise.

Balanced datasets (e.g., F₁-score in Dataset2 & Dataset5) outperformed skewed distributions, corroborating class imbalance as a critical challenge in mental health detection (Adel et al., 2024). Notably, domain-specific models like MentalRoBERTa partially mitigated imbalance effects (Dataset 4 F₁-score + 2.61%), suggesting that domain-aware feature learning compensates for distributional skewness. Figure 5 illustrates the performance comparison on TextGCN models enhanced with emotion representation learned from five different pre-trained language models.

RoBERTa and RoBERTa-based models outperform BERT and BERT-based models in most tasks. This suggests that RoBERTa’s unique features, such as dynamic masking and large-batch training, make it more adaptable to multitasking requirements. This aligns with clinical NLP studies where RoBERTa variants excel at detecting subtle symptom patterns (Lewis et al., 2020).

MentalRoBERTa’s success highlights the effect of domain adaptation, whereas RobertaDepressionDetection’s data-dependent performance reveals the limitations of task-specific fine-tuning in low-resource settings. A promising direction involves hybrid strategies like staged training, which has achieved state-of-the-art results in biomedical NLP (Beltagy et al., 2019).

MentalBERT and MentalRoBERTa exhibit superior performance on datasets comprising Reddit posts, given their pre-training data originates from Reddit, while RobertaDepressionDetection excels on Twitter datasets. The results reflect cross-platform generalization challenges arising from platform-specific linguistic norms, user language and demographic characteristics (Liu et al., 2019). Adversarial domain adaptation, as explored in prior work, may mitigate such discrepancies (Sun et al., 2019). But all pre-trained models outperform than the foundational ones. This emphasizes the pioneering exploratory value of the proposed framework in the field of early depression detection.