## 2 Materials and Methods
### Deep Learning Model for Classification of Mental Health Disorders  
#### 2.2.1 Dataset Construction  
Building a useful dataset to train a model that can diagnose mental health disorders is a challenging task. The dataset must be large enough to train a deep learning model and diverse enough to cover a wide range of mental health disorders. In this section, we describe the construction of the dataset used to train our models. Our goal is to build a labeled dataset that is large and consistent with the diagnostic criteria of the DSM-V manual so that the model can learn to classify symptoms in a way that is clinically sound. During recent years, a growing body of literature has begun to explore the use of data obtained from social media platforms to detect mental health disorders [18; 19; 20; 21]. The underlying premise is to discern discursive markers indicative of potential mental health issues, thus facilitating the prediction of users who may require mental health support. For instance, in [22], the authors used both the network of friends of users and the textual content of the posts to predict the risk of depression. In [23], the authors proposed a model that incorporated the temporal variable, using the textual content of tweets and Twitter metadata to predict the onset of a depressive episode in users. The reader is invited to read [18] for a recent survey on work using data from social media to detect or predict mental health disorders. Although a range of social media platforms have been used for data extraction, such as Facebook [24], Twitter has been widely documented in the literature as a preferred data source, something attributable to the simplicity of large-scale data extraction via its API. The popularity of Twitter among younger demographics and the accessibility of tweets make it a convenient choice, albeit with a caveat. The scarcity of labeled data poses a restriction for the training of cutting-edge deep learning models, recognized as the state-of-the-art in natural language processing [25; 26; 27; 28; 29]. To obtain labeled data from Twitter and other social networks, in [19], four distinct methods are proposed to label mental health-related data. The first approach involves the use of self-report questionnaires. In this method, the user completes a standardized questionnaire designed to assess a specific mental health condition [30; 31; 32; 33]. Due to their clinical validity, questionnaires are a conventional method to identify mental health disorders in patients and are widely employed in various studies [23; 24; 34; 35]. Nonetheless, the amount of labeled data is directly dependent on the willingness of users to participate in the survey, limiting its scope. A second methodology for data labeling involves self-declaration, where data are extracted from the own disclosure of users of a mental health diagnosis in their tweets, looking for key phrases such as \"I was diagnosed with depression\" or \"I suffer from depression\" [36; 37; 38]. This approach leads to a greater data volume as it allows full automation, encompassing raw data collection and subsequent automated labeling; thus, it has been used in the construction of benchmark datasets, such as the one created for the 2015 CLPsych Workshop [39]. However, this method of labeling is generally deemed less reliable than manual labeling or clinical questionnaires, and despite the automation, it does not necessarily guarantee a significant data volume. This limitation is largely due to the relatively infrequent public declarations of mental health issues on social platforms, a consequence of the persisting stigma surrounding such disclosures. A more robust method for data labeling entails manual annotation by experts. This approach necessitates that annotators, either with clinical experience or under the supervision of clinical experts, apply established criteria to classify a post or tweet.