Depression is a prevalent mental health condition that affects a substantial number of individuals worldwide [1]. It is characterized by persistent feelings of sadness, loss of interest, and impaired functioning, leading to a significant decline in overall well-being and quality of life [2]. Timely detection and intervention are crucial for the effective management and treatment of depression. Left untreated, depression can lead to severe impairments in personal, social, and occupational functioning [3].

The advent of social media platforms has provided an unprecedented opportunity to study mental health conditions, including depression, on a large scale [4]. Twitter, in particular, has emerged as a valuable source of data for understanding individuals’ thoughts and emotions. Twitter users often openly share their personal experiences, feelings, and emotions, making it possible to explore and identify signs of depression through their public messages [5].

Machine learning techniques have demonstrated immense potential in automatically analyzing vast volumes of textual data and extracting meaningful insights ^[6][7][6,7]. Natural language processing (NLP) algorithms, in particular, have been leveraged to develop computational models capable of detecting depression symptoms in user-generated content, such as tweets [8]. These models offer a promising avenue to complement traditional diagnostic approaches and provide an efficient, scalable, and cost-effective means of screening for depression at a large scale ^[9][10][9,10].

2. Deep Learning-Based Depression Detection from Social Media

In recent years, there has been a growing interest in the use of artificial intelligence (AI) and machine learning (ML) to improve mental health care. As Shikha et al. (2023) [11] discussed, AI and ML can be used to detect and diagnose mental health conditions, develop AI-powered interventions, and improve access to mental health care services. There has been a growing body of research exploring the detection of depression from social media data [12], particularly utilizing machine learning techniques. This section provides an overview of key studies and methodologies in the field, highlighting the advancements made in detecting depression through user tweets. Negative comments or expressions of pessimism are often associated with depressive tendencies [13]. Research studies have explored the link between negative language use and depression, providing evidence to support the statement [9]. In a study conducted by [5], the researchers analyzed social media data and found a significant correlation between the language used in tweets and the prevalence of depression symptoms. They identified that individuals with higher levels of depression were more likely to express negative sentiments in their tweets. In another study, [9] investigated the association between language markers and depression on social media platforms. They found that individuals with depressive symptoms tended to use more negative language, indicating a correlation between negative expression and depression. Gkotsis et al. [14] employed informed deep learning techniques to characterize mental health conditions in social media. They utilized a large-scale dataset of Twitter posts and applied deep learning algorithms to detect mental health conditions, including depression. Their approach showcased the potential of leveraging deep learning models to gain insights from user-generated content and improve mental health monitoring. Moreover, the study by Resnik et al. [15] explored the role of sentiment analysis and linguistic markers in detecting depression from Twitter data. They developed a machine learning framework that incorporated sentiment analysis features to predict depression levels in individuals. Their findings highlighted the importance of sentiment analysis in capturing emotional states and identifying signs of depression. The study [16] focused on detecting depression using social media data and machine learning employed various text classification algorithms, including Support Vector Machines (SVMs) and random forests, to classify tweets as depressive or non-depressive. As explained by Kim (2017) [17], SVMs work by finding a hyperplane in the data that separates the two classes (depressed vs. not depressed) with the maximum margin. The study achieved promising results in terms of classification accuracy, demonstrating the potential of machine learning approaches for depression detection. While the study demonstrated effective depression detection from social media data, it primarily focused on traditional machine learning algorithms. Incorporating more advanced deep learning models such as recurrent neural networks or transformers could potentially improve the performance and capture complex patterns within the tweet data. In [18], the researchers examined the use of natural language processing techniques to analyze social media posts for detecting depression. They applied sentiment analysis and topic modeling to identify linguistic markers associated with depression. The study highlighted the importance of linguistic cues in identifying mental health conditions from social media data. Although the study provided valuable insights into the linguistic markers of depression, the research focused solely on Twitter activity and did not explore the potential of utilizing additional contextual information from user profiles or network interactions. Integrating these additional features could enhance the accuracy and robustness of depression detection models. Guntuku et al. (2017) [19] investigated the relationship between language patterns on Twitter and depression symptoms. Their research explored the differences in linguistic style, linguistic content, and social engagement between depressed and non-depressed individuals. The study highlighted the importance of considering social context and interaction patterns in depression detection. The study provided a comprehensive review of the existing literature on detecting depression from social media. However, further research is needed to explore the generalizability of the findings to diverse populations and different social media platforms, as user behavior and language use may vary across platforms and cultural contexts. Other studies have also emphasized the significance of incorporating contextual information from social media platforms [20]. For example, Nguyen et al. [21] investigated the relationship between social context and depression detection, analyzing not only the content of tweets but also the social network connections between users. Their research highlighted the potential benefits of considering the social context in understanding mental health indicators on social media. Overall, these studies collectively demonstrate the potential of using machine learning techniques for detecting depression from user tweets. By analyzing linguistic patterns, social interactions, and contextual information, researchers have made strides in developing computational models capable of identifying individuals at risk of depression.