Mental disorders, also known as psychiatric disorders, encompass a wide range of conditions that disrupt thoughts, emotions, and behaviors, often impairing an individual's ability to function in daily life (Orabi et al., 2018; Zhang T. et al., 2022). These include depression, anxiety, schizophrenia, and bipolar disorder, with causes rooted in genetic, environmental, and biological factors. Among these, depression is particularly prevalent and debilitating, affecting health, relationships, and productivity. Despite the availability of treatments like psychotherapy and pharmacotherapy (Ive et al., 2020), depression remains a global public health concern. According to the World Health Organization, over 322 million people suffer from depression worldwide, yet many cases go undiagnosed due to stigma and limited access to mental health services, especially in low- and middle-income countries (Velupillai et al., 2018; World Health Organization, 2021).

To address these challenges, researchers are increasingly leveraging digital data sources—particularly social media—to detect mental health issues using Natural Language Processing (NLP) and Machine Learning (ML) techniques (World Health Organization, Regional Office for the Eastern Mediterranean). Social media platforms like Twitter (X), Reddit, and Facebook offer real-time insights into users' emotions, behavior, and potential mental distress. Recent studies have demonstrated the potential of analyzing linguistic and behavioral cues to predict mood disorders and related symptoms, such as stress, self-harm, and emotional deterioration, without requiring traditional clinical assessments (Yazdavar et al., 2020; Olusegun et al., 2023; AbuRaed et al., 2024; Chancellor and De Choudhury, 2020). These digital approaches offer scalable, non-intrusive alternatives to traditional mental health diagnostics, especially in underserved populations.

Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) that has facilitated various tasks in recent years, including the management and analysis of large amounts of textual data, information extraction, sentiment analysis, emotion detection, and mental health surveillance, among others (Steinkamp and Cook, 2021a). Feature extraction techniques in NLP are essential for transforming unstructured textual data into structured numerical representations, thereby enabling machine learning models to perform tasks such as classification, sentiment analysis, and topic modeling. Traditional methods such as Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and N-grams convert text into sparse feature vectors by capturing lexical patterns and word co-occurrence frequencies (Salton and Buckley, 1988; Jurafsky and Martin, 2000). While effective for basic NLP tasks, these approaches cannot capture deeper semantic relationships and contextual nuances. To address these limitations, more advanced feature extraction techniques such as Word2Vec, GloVe (Pennington et al., 2014), and contextual embeddings from transformer-based models like BERT (Devlin et al., 2019) have been developed. These methods represent words in dense vector spaces and incorporate semantic and syntactic context, significantly improving performance across a wide range of downstream NLP applications.

In general, users can convey their emotions through various written formats, such as posts on social media platforms, interview transcripts, and professional notes that include patient descriptions of their mental states. X (formerly known as Twitter) is most commonly known as a platform for micro-blogging because it has a straightforward user interface that enables the publication of short stories of no more than 280 characters. Tweets posted by virtually every user are available to the general public and can be retrieved using the user's own X API (Govindasamy and Palanichamy, 2021). X enables users to analyse and understand current events and trends, regardless of their geographical location. More recently, the research work focused on determining whether a person is depressed by analyzing their tweets. More precisely, Sentiment analysis can determine whether a piece of writing has been produced in a positive, negative, or neutral tone. Comments and posts from other X users can reveal whether a user is happy or sad at any given moment. Each tweet is evaluated based on its positive, negative, or neutral sentiments. The NLP system may classify tweets as either depressive or non-depressive by identifying depressive symptoms.

More recently, black box ML algorithms have demonstrated exceptional performance in text classification and analysis, yielding accurate and efficient results across various applications. However, their intrinsic black box nature poses notable challenges to transparency, interpretability, and trustworthiness, especially in critical domains where comprehensible decision-making processes are essential (Cesarini et al., 2024; Jahromi et al., 2024). The lack of transparency in ML and its black box nature are significant issues in its implementation in critical domains, such as healthcare (Khan et al., 2024; Weerts et al., 2019; Chakraborty et al., 2021; Kawakura et al., 2022; Nauman et al., 2021). Explainable Artificial Intelligence (XAI) is a subdomain of AI that aims to improve transparency by explaining the internal decision-making processes of such models (Chen et al., 2021).

One the contrary, a few known efforts to explain the black box models in literature include SHAP (Chelgani et al., 2023) and LIME (Hakkoum et al., 2020). Recent research in explainability focuses on revealing the primary features that significantly impact a model's decision-making process (Zhang Y. et al., 2022). As AI-based systems only make predictions without explaining their rationale, there is a need for mechanisms to explain and interpret their decisions. Furthermore, Local Interpretable Model-agnostic Explanations (LIME) is an explainability technique used to interpret the predictions made by machine learning models. The LIME technque approximates a complex model with a local, interpretable one around the prediction to be explained, thereby offering insights into the model's behavior on individual predictions (Ribeiro et al., 2016b). This method is particularly valuable in domains where understanding the decision-making process is crucial, such as healthcare and mental health diagnosis, as it helps build trust and provides transparency in the model's decisions (Ribeiro et al., 2016a). By applying LIME to the ML models, we can identify which features contribute most to the predictions, thus making the model's decisions more understandable and actionable for stakeholders.

Recent research on depression detection from social media platforms like Twitter and Reddit has shown that ML and deep learning models—such as CNNs, LSTMs, and BERT—are effective in identifying mental health indicators from user-generated content (Amanat et al., 2022b; Ji et al., 2022b). However, key gaps remain: many models operate as black boxes with limited interpretability, which is problematic in clinical contexts requiring transparency (Guo et al., 2023a; Ibrahimov and Ali, 2024). Most studies rely on a single feature representation method, overlooking the benefits of combining traditional and semantic features. Comparative evaluations across diverse models ranging from classical ML to deep neural networks are rare, limiting insight into their relative performance. Additionally, while XAI tools like LIME and SHAP are gaining traction, their integration into end-to-end depression detection systems remains limited. Finally, many datasets are weakly labeled, often based on heuristics or self-reports without clinical validation, undermining the reliability of resulting models (Chancellor and De Choudhury, 2020; Yazdavar et al., 2020).

This research distinguishes itself from existing literature by employing multiple feature extraction techniques and the LIME (Ribeiro et al., 2016b) method to elucidate the internal decision-making processes of machine learning models. This work focuses on interpreting the detection decisions made by ML models to enhance early mental disorder detection and support healthcare professionals. The research results will enable physicians to identify life-threatening diseases in their early stages, ultimately facilitating a healthier society. This work aims to leverage advanced ML techniques and XAI to facilitate the early detection of myocardial infarction through the analysis of X data. By addressing the gap in understanding how social media conversations can be leveraged for mental health insights, this research contributes novel methodologies for pre-processing data, feature extraction, and model interpretation.

The main contributions of this research work are as follows:

The detection of mental disorder through social media content is garnering significant attention from the research community (Santos et al., 2023; Amanat et al., 2022a; Chanda et al., 2022; Ji et al., 2022a; Haque et al., 2022; Wani et al., 2022; Rizwan et al., 2022; Ramírez-Cifuentes et al., 2021; Ghosh and Anwar, 2021; Mohammed et al., 2021). Table 1 provides a summary of related studies in the literature on mental disorder detection using machine learning and NLP techniques.

More recently, Ibrahimov et al. (2025) emphasized model transparency alongside performance, introduced Depression X, a knowledge-infused residual attention model achieving a 7% F1 improvement while providing interpretable insights. Similarly, Qasim et al. (2025) utilized transformer-based architectures (e.g., BERT/RoBERTa) to assess depression severity directly from social media text. In another study, Friedman et al. (2024) proposed EAC-Net, an emotion-aware encoder leveraging contrastive learning and self-attention, demonstrating superior recall on depression and stress detection across multiple datasets. Expanding into multimodal input, Cha et al. (2024) presented MOGAM, which integrates video, text, and metadata via graph-attention mechanisms, achieving 0.87 accuracy on clinically labeled users. Additionally, Al Asad et al. (2024) developed a BERT+Bi-LSTM pipeline for both English and Arabic, highlighting the importance of explainability in achieving top F1 scores across languages.

In another work, Ibrahimov et al. (2024) highlighted the critical role of XAI frameworks in making mental health AI models transparent and trustworthy. Moving beyond surveys, Chen and Lin (2025) developed LLM-MTD, a large-language-model based multi-task system that simultaneously classifies depression and generates medically informed explanations, achieving state-of-the-art results on the RSDD benchmark. Empirical studies, such as those by Hoque et al. (2025), demonstrate the effective application of explainability tools like SHAP and LIME in real-world educational datasets. Their model attained over 91% accuracy in detecting depression in Bangladeshi university student posts, reinforcing the value of interpretability in high-risk settings.

Tadesse et al. (2019) proposed an approach to identify depression-related posts on Reddit using NLP and ML techniques. Their approach highlighted the significant improvement in detection accuracy by using a combination of linguistic features and classifiers, achieving up to 91% accuracy with a Multilayer Perceptron (MLP) classifier. Their work underscores the importance of feature selection and combination in enhancing the performance of depression detection systems. Furthermore, Guntuku et al. (2017) explored the detection of depression through social media data, highlighting significant advances in NLP and ML that facilitate large-scale mental health screening. Despite these technological advances, the generalizability of such studies to diverse populations and alignment with established clinical criteria remains uncertain. Furthermore, recent work highlighted the pressing need to address ethical, legal, and clinical considerations, particularly concerning data ownership, privacy protection, and the integration of these methods into existing healthcare systems.

Cho et al. (2019) proposed an approach to a comprehensive evaluation of the research that has been conducted on the use of ML algorithms for the diagnosis of depression, as well as recommendations for the practical uses of ML and predicted clinical remission following treatment with citalopram for twelve weeks. The dataset consisted of 1949 sad individuals who were participating in level 1 of the Sequenced Therapy Options to Relieve Depression study. In analyzing mental health using ML techniques, the primary focus is on providing a supervised learning environment for classification.

Another work by Helmy et al. (2024) explored the application of machine learning techniques to identify signs of depression in X data. It introduces manually labeled Arabic and automatically labeled English depression corpora, evaluates various pre-processing, feature extraction, and supervised classification techniques, and demonstrates the viability of machine learning for early depression detection despite recent trends favoring deep learning. This work underscores the importance of diverse, language-specific corpora and provides valuable insights into effective combinations of methodologies for predicting depression severity. The experiments demonstrated the significant impact of feature representation and resampling techniques on classifier performance, with Random Forest (Breiman, 2001) and RBF-SVM (Schölkopf and Smola, 2002; Cortes and Vapnik, 1995) models showing high effectiveness across different scenarios.

Santos et al. (2023) proposed the use of Mixture of Experts models combined with BERT-based approaches for predicting depression and anxiety from self-reports on social media in Portuguese was proposed. Their findings indicate that models outperform traditional feature engineering methods while also allowing for more interpretable models. The finding suggests potential improvements through modifications such as attention mechanisms, hierarchical mixtures, and multi-task learning. More recently, Amanat et al. (2022a) investigated the application of deep learning models for detecting signs of depression in textual data sourced from social media. The proposed framework leveraged LSTM networks and RNNs to analyse and classify text, achieving an impressive accuracy of 99% in early depression detection. The findings underscore the potential of advanced machine learning techniques to enable timely and precise identification of depressive tendencies, offering valuable support for mental health interventions and early assistance strategies.

Guo et al. (2023b) investigated mental health detection using text data from online forums, employing advanced machine learning techniques, including CNNs and LSTM networks. These models captured complex patterns and contextual nuances in textual data. To enhance the interpretability of these inherently black box models, the authors used the LIME technique. The LIME provided insights into the specific language patterns and features that influenced the model's predictions, enabling researchers to link certain textual expressions to mental health conditions. The interpretability increased the model's trustworthiness and supports its integration into clinical settings, where understanding decision rationale is critical for adoption and application.

Furthermore, Joyce et al. (2023) introduced the TIFU framework to enhance the trustworthiness of AI in psychiatry by focusing on transparency and interpretability. The author emphasized the importance of explainable AI, particularly through methods like LIME, to make complex models more understandable for healthcare professionals and patients, enhancing their reliability and acceptance in mental health applications. Abd Yusof et al. (2017) developed a computational model to identify potential causes of depression by analyzing user-generated content. This work identified prominent causes of depression and how they evolved, highlighting differences between individuals with varying levels of neuroticism. Another study, Sabaneh et al. (2023) integrated several advanced methodologies, including the use of ChatGPT-3 for translating Arabic text to English, QuickUMLS (Soldaini and Goharian, 2016) for extracting medical concepts from the translated text, and machine learning algorithms for classification. The researchers utilized a variety of classification algorithms, such as RF, SVM, and LR, with RF achieving the highest accuracy of 80.24%.

Saxena et al. (2022) explored the challenge of multi-class causal categorization of mental health issues on social media, focusing on the problem of incorrect predictions due to overlapping causal explanations. Their work identified inconsistencies in causal explanations as a key reason for varying accuracy by fine-tuning classifiers and applying LIME and Integrated Gradient methods (Sundararajan et al., 2017; Kokhlikyan et al., 2020; Ancona et al., 2018). The proposed approach was validated on the CAMS dataset, achieving category-wise average scores of 81.29% and 0.906 using cosine similarity and word mover's distance, respectively. Furthermore, Adarsh et al. (2023) utilized LIME to enhance the explainability of their classification model. The LIME was employed to identify and highlight specific words within social media posts that significantly contribute to the classification of posts as either containing suicidal ideations or not. The LIME was used in their approach to enhance the transparency and interpretability of the depression detection model, making it easier to understand and trust the model's decisions, particularly in identifying critical language markers of suicidal ideation.

This research work presents a robust approach for detecting depression from X posts. The proposed approach consists of three steps: First, preprocessing techniques and feature extraction methods; second, machine learning classifiers; and third, interpretability analysis using LIME. The comprehensive methodology is depicted in Figure 1.

The robustnsess of the proposed approach lies in our preprocessing pipeline, which refers to its ability to effectively clean and normalize noisy, informal social media text, including irregular spellings, emoticons, repeated characters, and abbreviations which are common on platforms like X (Chancellor and De Choudhury, 2020; Steinkamp and Cook, 2021b). This pipeline improved the quality of feature extraction by reducing vocabulary sparsity and enhancing model generalizability. Our approach also ensured consistent performance across all tested classifiers (ANN, SVM, XGBoost, RF), demonstrating resilience against data variation, which is crucial when working with user-generated, unstructured data. Compared to existing methods (e.g., Ji et al., 2022a; Amanat et al., 2022a), our framework achieves higher or comparable accuracy using simpler architectures (e.g., GloVe+RF: 88%, SVM+TFIDF: 79%), while maintaining interpretability through LIME, which is rarely integrated in similar works (Ji et al., 2022b; Amanat et al., 2022b). This hybridization of multiple NLP features with black box models, accompanied by transparent explanations, offers a practical and explainable solution for early depression detection. Furthermore, our system does not require deep learning or transformer-based models, making it more computationally efficient and suitable for real-world deployment.

Once the performance of the ML model has been evaluated, it becomes essential to analyse and interpret the findings to gain deeper insights into the model's performance. This involves discerning the crucial features influencing the model's predictions, comprehending the relationships between these features and the target variable, and identifying any pertinent patterns or trends within the dataset. This work employed a comprehensive set of experiments to detect depression from X posts using various combinations of feature extraction methods and ML classifiers. To visualize the most prominent terms that express emotions, a word cloud representation is utilized. Figure 3a demonstrates the depressive user's feelings, experiences, and stories. This method summarizes the ideas and phrases most commonly linked to mental disorders in the analysis of social media discussions. Figure 3b illustrates the word cloud representing the positive core words of the dataset. Word clouds are visual representations that draw attention to the most prevalent terms in a collection of text. In addition, the prominence of a word in the cloud reflects its frequency of use in the corresponding tweets. This method summarizes the ideas and phrases most commonly linked to mental disorders in the analysis of social media discussions. On the contrary, Table 5 presents the words correlated with the specific topics generated from the posts. These topics comprise a lexicon of words commonly used among accounts associated with depression.

The results of research experiments are summarized in Table 6, which shows the accuracy and prediction probabilities for each combination of feature extraction method and classifier. Additionally, Table 7 provides a comparative analysis of several ML classifiers, namely ANN, XGBoost, RF, and SVM, using different feature extraction techniques. These features include LDA, TF-IDF, N-gram, BOW, GloVe, and various combinations of these techniques. The classifiers are evaluated based on their performance metrics: Precision, Recall, and F1-score.

It should be noted from Table 6 that the GloVe feature extraction technique combined with RF achieved the highest accuracy of 88%, demonstrating its ability to capture rich semantic information effectively. SVM also performed well with GloVe, achieving an accuracy of 85%. TF-IDF and N-gram modeling showed competitive performance, with XGB achieving an accuracy of 77% and 78%, respectively, demonstrating their effectiveness in capturing text features. BOW proved to be a reliable feature extraction method, with XGB and ANN yielding accuracies of 78% and 73%, respectively. The combination of LDA with TF-IDF and N-gram yielded an accuracy of 78% for ANN, showcasing the potential of combining multiple feature extraction techniques. Our experiments demonstrate the varied strengths of feature extraction methods and classifiers, with GloVe providing the most insightful semantic information. At the same time, N-gram and BOW maintained a consistent balance of performance across models.

Figure 4 presents a comparative analysis of four ML classifiers ANNs, XGB, RF, and SVM across various feature extraction methods: LDA, TF-IDF, N-gram, BOW, GloVe, LDA+TF-IDF+Ngram, and LDA+BOW+TFIDF. The performance of each combination is evaluated and depicted in terms of accuracy percentages. The highest performance is achieved by the GloVe feature extraction method, followed closely by the SVM algorithm at 86%. Overall, the GloVe method consistently outperforms other feature extraction techniques, indicating its effectiveness in capturing word semantics and improving classification accuracy. Other notable performances include the LDA+TF-IDF+Ngram method, which demonstrates balanced accuracy across different classifiers, particularly with SVM classifiers. This comprehensive comparison underscores the importance of selecting suitable feature extraction methods to boost the predictive power of machine learning models in text classification tasks.

Despite their excellent performance, several ML models are often characterized as black boxes that produce outputs without offering explicit insights into the underlying reasoning behind their decisions. Understanding and interpreting the decision-making processes of such models is critical, particularly in applications where trust, transparency, and accountability are paramount. Consequently, it is imperative to examine the outputs of these models thoroughly and, more importantly, to develop methodologies that enable the generation of interpretable explanations for their decisions (Søgaard, 2021; Gohel et al., 2021). Providing explanations for a model's output enhances our ability to evaluate its predictions critically, thereby fostering greater confidence in determining whether to trust or question its outcomes.

To investigate the model's explainability, we employed LIME, a popular XAI technique that facilitates the interpretation of outputs without requiring direct inspection of the model's internal structure. LIME achieves this by perturbing the local features surrounding a specific target prediction and analyzing the corresponding changes in the model's output. In our experiments, the words surrounding a target entity were modified systematically, and the effects on the model's predictions were subsequently assessed to gain insights into the decision-making process.

Each subplot in Figure 5 illustrates the LIME analysis visualizations, providing an interpretable explanation of the predictions made by different classifiers for specific instances. Each subplot highlights the contribution of individual words (features) toward the prediction of either “Depression” or “Non-Depression” labels. The importance of the words is represented as bars, where positive contributions toward “Depression” are shown in blue, and contributions toward “Non-Depression” are shown in orange.

Figure 5a demonstrates LIME analysis for ANN with LDA features extraction on the 10th instance. It can be noted that the word “snow” contributed significantly to the “Depression” label, with a probability score of 0.54, compared to 0.46 for “Non-Depression.” This shows how specific context-sensitive words can influence predictions. Similarly, Figure 5b demonstrates LIME analysis for XGB and TF-IDF on the 960th instance, the words “wish,” “sleep,” and “tooo” strongly contributed to the “Depression” label, achieving a high probability score of 0.87 for “Depression”. This highlights the model's ability to capture sentiment-relevant features using the TF-IDF approach.

In addition, Figure 5c demonstrates a dominant contribution of the words “miss” and “sorry” toward the “Depression” label, achieving a probability score of 0.93. This emphasizes the BOW model's capability to detect sentiment-related words. Similarly, Figure 5d visualizes that, RF with N-Gram on the 2000th instance, the words “work,” “desk,” and “10am” contribute strongly toward the “Depression” label, with a probability score of 0.91. This demonstrates how the N-Gram technique effectively captures contextual word co-occurrences. On the other hand, Figure 5e indicates that ANN with GloVe embeddings on the 3000th instance, provided a contrasting prediction, with the word “islandiva147” contributing more toward “Non-Depression” than “Depression.” This suggests that GloVe embeddings capture semantic nuances but may misinterpret words out of context.

Overall, these visualizations illustrate the interpretability of the classifiers and their reliance on feature-specific contributions. While ANN and SVM demonstrated strong performance on LDA and BOW features, respectively, XGB and RF highlighted the importance of TF-IDF and N-Gram features. GloVe embeddings, while semantically rich, occasionally misinterpret specific instances, underscoring the importance of feature selection.

Our study examines various feature sets and classifiers, achieving notable accuracies, particularly when combining GloVe with classifiers such as RF and SVM. GloVe+RF, achieved 88% accuracy obtained by Ji et al. (2022a). In comparison, Amanat et al. (2022a) reported accuracies up to 96.4% using a combination of TF-IDF, BOW, SVM, and RF. While our results for GloVe+RF (88%) and GloVe+SVM (85%) are slightly lower, they are still competitive given the different data sources and methodologies used. which is within the range reported by comparable studies on depression detection using traditional machine learning approaches. Variations in reported accuracy across the literature are largely attributable to differences in datasets, preprocessing steps, and feature-classifier combinations, making direct score comparisons less meaningful. Instead, the emphasis here is on showing that competitive performance can be achieved alongside enhanced interpretability.

Among feature extraction methods, GloVe embeddings achieved the highest average accuracy across classifiers (82.75%), followed by LDA+BOW+TF-IDF (77.00%) and TF-IDF (76.50%). This indicates that semantic embeddings like GloVe capture richer contextual information than purely lexical representations. These results are consistent with prior work—for instance, Tong et al. (2022) reported 86% accuracy using GloVe-based features in a multi-classifier ensemble, whereas our GloVe+RF model reached 88% on this dataset. Although Amanat et al. (2022a) achieved higher performance (96.4%) with TF-IDF+BOW+SVM/RF, differences in datasets and preprocessing make direct comparisons indicative rather than conclusive. Notably, N-gram and BOW features, while ranking lower in average performance (76.00%), still matched or exceeded the accuracy of some deep learning models in the literature, such as the CNN (78%) and LSTM (80%) reported by de Souza et al. (2022), demonstrating that simpler representations can be competitive for certain datasets.

Among ML classifiers, XGBoost and SVM obtained the highest average accuracy across feature sets (77.29%), closely followed by Random Forest (76.86%), with ANN ranking lowest (73.71%). This suggests that tree-based ensembles and margin-based classifiers are generally better suited to the depression detection task when trained on short, noisy social media text. These trends align with the findings of Wani et al. (2022), where SVM achieved 71% and KNN 62% using N-gram features, and with Ji et al. (2022a), who reported competitive performance with SVM on short-text datasets. In our experiments, ANN performed best with the combined LDA+TF-IDF+N-gram feature set (78%), which slightly exceeds the BiLSTM performance (79%) reported by Haque et al. (2022), showing that under certain feature configurations, neural networks can still achieve strong results.

A key finding is that performance depends on the interaction between the feature set and the classifier. For example, while GloVe consistently ranks highest among features, its combination with RF (88%) and SVM (85%) outperformed its pairing with ANN (72%). Similarly, the LDA+TF-IDF+N-gram feature set worked particularly well with ANN (78%), but less so with RF (72%). These variations underscore the importance of evaluating both dimensions independently before selecting an optimal configuration.

Another distinctive aspect of this study is the systematic application of LIME across all feature-classifier combinations, rather than to a single model. LIME provided interpretable, instance-level explanations, identifying key linguistic cues such as first-person pronouns, negative emotion words, and self-referential phrases that heavily influenced depressive content predictions. This transparency is essential for building trust with mental health professionals and differentiates our work from most prior studies, where explainability is rarely addressed at this scale.

It should be noted that the accuracy of depression detection models heavily relies on the authenticity and consistency of users' social media content. Factors such as bias for social desirability, stigma, and personal tendencies can influence the way users express themselves online, potentially leading to inaccuracies in detection (Shah et al., 2025). In general, detecting depression from social media posts inherently depends on the assumption that users share relevant depressive symptoms or emotional cues in their online interactions. However, not all individuals with depression disclose their condition or express depressive symptoms publicly on social media, which presents a notable limitation of computational models relying solely on social media text. This limitation leads to potential false negatives, where affected individuals who do not manifest depressive behavior online may be missed by such systems.

Similarly, another recent study (Aldkheel and Zhou, 2024) highlighted that social media detection methods that focus on observable linguistic, visual, and behavioral signals indicative of depression cannot account for users who do not publicly share or mask their symptoms due to privacy concerns, social stigma, or personal choice. Moreover, reliance on textual content alone restricts detection to expressed emotions and behaviors, which may not comprehensively represent every user's mental health status.

The necessity for combining social media analysis with clinical validation and offline data is emphasized to address these limitations and improve detection reliability. Verification of ground truth through clinical evaluations or integration of medical records along with social media data can help overcome false negatives caused by the absence of explicit online depressive expression (Aldkheel and Zhou, 2024). Thus, while social media-based depression detection offers valuable early screening potential, it cannot substitute for comprehensive clinical diagnosis and does not capture all cases, especially among users who do not disclose symptoms online.

GloVe word embeddings worked better with models like Random Forest and ANN because they capture the meaning and relationships between words. Unlike methods like TF-IDF or Bag of Words that just count word frequency, GloVe places similar words (like “sad” and “unhappy”) close together in a way that shows their meaning. This helps the models better tell the difference between depressive and non-depressive posts, especially since social media posts are usually short. Traditional models like SVM and Random Forest did well with TF-IDF because they can handle large sets of sparse features. However, the ANN model didn't perform as well with TF-IDF or BoW since those features don't carry the deeper meaning that neural networks are designed to learn from. When we used LIME to explain the model's decisions, we found that GloVe helped the models focus on important words like “lonely,” “worthless,” and “help”— strong signs of depression. In contrast, TF-IDF often picked up common but less meaningful words, which made the learning less effective.

We used social media data from platform X (formerly Twitter) because it is public, real-time, and short in format making it ideal for spotting signs of mental health issues. Compared to sites like Reddit or medical records, Twitter has more variety in language, which helps our model work better in real-world situations (De Choudhury et al., 2013).

To get useful features from the text, we used both basic methods (like TF-IDF, LDA, N-gram, and Bag of Words) and word embeddings (like GloVe). While advanced models like BERT understand context better, they are slower and harder to explain. GloVe gave us good results without needing too much computing power, which is important if we want to use this in real-time systems (Pennington et al., 2014).

We picked simple models like SVM and Random Forest because they work well with smaller datasets, are faster to train, and are easier to understand—especially when used with tools like LIME. These models also don't need powerful hardware and can be used in apps or cloud platforms. We used LIME to explain how our models make decisions. It works with many kinds of models and helps make the results clearer for doctors and other users. This is important for mental health tools, where we need to be careful and ethical in how results are used (Ribeiro et al., 2016b).

Overall, our findings demonstrate the effectiveness of combining traditional and advanced NLP techniques with robust classifiers to achieve competitive performance in text classification tasks. Our results indicate that, while advanced deep learning models are powerful, conventional methods and hybrid approaches can also achieve competitive accuracy, offering more interpretable and computationally efficient alternatives.

To address the identified research gaps in the literature, our study proposes an interpretable and comparative framework for depression detection using Twitter data, comprising four key components. First, multiple NLP feature representations are employed, including TF-IDF, BoW, LDA, N-grams, and GloVe embeddings, to effectively capture both statistical and semantic characteristics of textual content. Second, a range of ML classifiers, including SVM, RF, ANN, and XGBoost, were evaluated under identical experimental conditions to assess their predictive performance. Third, explainability was integrated into the framework using the LIME method, which highlights the contribution of individual features to classification outcomes, thereby enhancing transparency and trust in the model. Finally, a comparative evaluation is performed in which all models and feature extraction techniques are applied to the same dataset and evaluated using consistent metrics, accuracy, precision, recall, and F1 score, to ensure fairness, reproducibility, and applicability in the real world.

Mental illness is a prevalent social issue driven by socioeconomic, clinical, and individual risk factors, and the rise of social media has allowed the analysis of user-generated content for early detection of depression. In this work, we evaluated the effectiveness of various feature extraction methods and machine learning classifiers in detecting depression from X posts. Our findings demonstrate that social media data can be effectively utilized for mental health monitoring, with methods such as N-gram, BOW, and TF-IDF providing significant insights. In particular, the combination of TF-IDF with XGB achieved the highest precision of 87%, while the GloVe embeddings with RF reached an accuracy of 88% with lower interpretability. The use of LIME highlighted the importance of balancing accuracy and interpretability in model outcomes. Future research should focus on incorporating advanced deep learning models such as Transformers and BERT, developing real-time detection systems, integrating multimodal data, expanding analyses to additional social media platforms, and addressing ethical and privacy concerns in mental health monitoring.