## 1 Introduction  
Depression is a persistent and pressing challenge within contemporary society that presents itself as a significant public health concern. The prevalence of this mental illness continues to increase, exerting profound effects on individuals and communities alike [1]. According to the World Health Organization (WHO) [2], around 280 million people of all ages suffer from depression worldwide. Depressive disorder encompasses a wide array of distressing symptoms that can profoundly impact an individual's well-being and functioning. These symptoms can involve persistent sadness, fatigue, difficulty concentrating, and feelings of hopelessness, among others [3]. Such symptoms lead to a heavy burden that includes reduced productivity, impaired social functioning, increased healthcare costs, and a higher risk of comorbidity. Furthermore, depression is one of the leading causes of disability and is associated with a higher risk of suicide [4]. The far-reaching impact of depression underscores the urgent need for innovative and effective approaches to its comprehensive detection and treatment. This has become an essential priority and has increased mental health promotion initiatives focused on detecting, preventing, and treating depression [5]. However, self-denial and poor recognition of the problem may cause a significant number of individuals to remain undiagnosed or avoid seeking timely treatment [6]. Depression assessment has traditionally relied on self-report measures [7; 8] to measure individuals' depressive symptoms and severity. Although these methods have shown validity, their frequent delay in diagnosis and inherent bias caused by their subjectivity and dependence on subjects' recall usually become notable limitations [9]. Remarkable progress in the field of artificial intelligence presents a promising avenue to overcome these drawbacks and enhance the accuracy and efficacy of depression detection and monitoring [10]. Popular approaches that benefit from this progress include the analysis of physiological signals [11], facial expression [12], or speech [13; 14]. Although the reliability of these methods has been proven [15], their implementation in real-life settings often requires costly and obtrusive sensing equipment. This challenge has encouraged research on more economical and unobtrusive solutions. Text analysis and natural language processing (NLP) technologies have created numerous opportunities to detect depression by taking advantage of the vast amounts of textual data generated through digital platforms and online communication [16]. These platforms serve as a rich source of freely shared thoughts and feelings, allowing researchers to explore diverse dimensions of psychological concerns and human behavior [17]. The use of digital content to identify people with depression has potential applications in disease surveillance and the provision of timely expert support [18]. The most popular approaches in the literature are based on the use of feature extraction methodologies to represent texts as vectors in a feature space [19]. These vectors are then fed into machine learning algorithms to classify the text. However, the difficulty in identifying appropriate sets of characteristics coupled with generalization problems prevalent in traditional machine learning algorithms led to the use of more complex methods [20]. Novel approaches, such as the use of deep neural networks and transformer-based models, have emerged to overcome these limitations. While these methods offer huge potential, they pose challenges such as the need for large amounts of data or increased computational demands [21]. Additionally, its black-box nature may raise concerns about model interpretability and transparency [20], which are crucial considerations in applications related to mental health promotion [22]. The ongoing research efforts aim to strike a balance between the advantages of traditional and modern methodologies, capitalizing on their respective strengths.