## 2 Materials and Methods
### Deep Learning Model for Classification of Mental Health Disorders
#### 2.2.1 Dataset Construction
This method affords flexibility in labeling, potentially allowing for categorization based on severity, causative factors, or any variable stipulated in the labeling rules. This strategy aligns with traditional labeling and benchmark construction methodologies within the field of Natural Language Processing (NLP). However, the demand for expert involvement significantly constrains the speed of labeling relative to other benchmarks. Furthermore, it necessitates the implementation of agreement methods among multiple experts to accurately label challenging cases, ultimately limiting the achievable size of such datasets. A noteworthy effort directed towards these datasets is the construction of the Deptweet Benchmark [40]. The authors of this study collected and labeled 40,191 tweets, utilizing annotators trained by clinical psychologists. Despite the commendable effort, involving a collaborative endeavor of 90 annotators and an exhaustive validation and check process, the resulting dataset size remains small when measured against the standards of deep learning models. An alternative data labeling involves the determination of forum membership [41; 42; 43]. Here, users are classified based on their participation in public support forums related to specific mental health diagnoses. The underlying premise is that these forums are frequently sought for assistance and support following the diagnosis of a mental health condition; thus, forum membership can serve as a form of self-labeling. Despite the likely reduced clinical validation compared to expert labeling [44], the data volume available with this method vastly surpasses that of previous methods. Reddit presents a particularly resourceful platform for obtaining this type of data [45]. With over 1.6 billion active monthly users and more than 10 million monthly posts, Reddit is among the most popular social networks. Mental health discussions are prevalent on Reddit, with numerous specialized forums providing spaces for anonymous discourse on mental health issues. In particular, there are at least 15 famous subreddits (or subforums) devoted to mental health topics. The topics are resumed in Table 1.  
These subforums are dedicated to specific disorders, often aligned with diagnostic criteria as outlined in mental health disorder manuals [46; 47]. This alignment offers an opportunity to harness such data for training models capable of identifying social media posts that correspond to these condition categories. Several studies have used Reddit datasets to train machine learning models [45; 47; 48; 49]. For instance, ref. [48] details the creation of a training dataset and the deployment of a deep learning model using Convolutional Neural Networks (CNNs) [50] to automatically classify each post in the subreddit. Similarly, ref. [49] introduces a slightly different CNN-based model for subreddit classification, achieving results that are promising but not directly comparable to the previous work. A notable limitation of a dataset sourced primarily from Reddit is its incomplete representation of clinically valid diagnoses. The nature of discussions on Reddit typically skews towards more prevalent conditions like anxiety, depression, or addictions, resulting in a bias in the dataset. This skew limits the effectiveness of the dataset as a comprehensive training tool for a model tasked with automatically identifying and diagnosing a full spectrum of conditions. Overrepresentation of common conditions increases the likelihood of misdiagnosing rarer mental health problems. To address this challenge, we propose to augment the Reddit dataset with additional online sources. Reddit is not the sole platform for mental health discussions; numerous other forums and data sources specialize in this area, offering a broader range of discussions. An example is PsychForums, a forum analogous to Reddit but specifically dedicated to mental health topics.