## 2 Materials and Methods
### Deep Learning Model for Classification of Mental Health Disorders
#### 2.2.1 Dataset Construction
In PsychForums, discussions are organized into clinically valid categories and diagnoses with a high level of specificity, enabling users to engage in more specialized conversations. This approach helps to create a more balanced and diverse dataset, enhancing the potential accuracy and scope of our model. The vast quantity of data available in these forums paves the way for the training of data-intensive models such as large language models. An emerging practice in training these models, particularly in the linguistic domain, is the use of semi-supervised learning as an initial stage to supervised training [25; 26; 51]. The objective of pre-training is to enable the model to learn contextualized vector representations of words, which can subsequently be utilized in classification tasks. These contextualized representations encapsulate crucial syntactic and semantic information that can improve performance in later tasks. Multiple studies have demonstrated that pre-training models on data derived from the specific domain of subsequent application markedly improve performance [52; 53; 54]. This improvement can be attributed to the fact that general models, trained on general domain data scraped from the web, often lack exposure to domain-specific terminology, re \begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**Subreddit** & **Description** \\ \hline BPD & A community for people with BPD (Borderline Personality Disorder), as well as for their family and friends, to exchange stories, provide support, and discuss coping strategies. \\ \hline bipolar & A subreddit for people affected by bipolar disorder to share personal stories, discuss treatment options, and offer support to each other. Also includes the related subreddits BipolarReddit and BipolarSOs. \\ \hline Schizophrenia & A subreddit dedicated to creating a supportive environment for those affected by schizophrenia, whether personally or in their relationships. \\ \hline Anxiety & A place for people with anxiety disorders and those who know them to share their experiences and strategies for managing anxiety. \\ \hline depression & A subreddit dedicated to sharing experiences, providing support, and fostering discussion about depression. \\ \hline selfharm & A community dedicated to offering support, advice, and a safe space for individuals who are dealing with self-harm behaviors or thoughts. Also includes the related subreddit StopSelfHarm. \\ \hline suicidewatch & A supportive community for individuals who are experiencing feelings of suicide, with the goal of providing emotional support and directing people to appropriate professional help. \\ \hline addiction & A community offering support, advice, and a non-judgmental platform for discussing addiction and recovery. \\ \hline cripplingalcoholism & A subreddit specifically for people dealing with severe, often life-threatening alcoholism. \\ \hline Opiates & A supportive community for people who are trying to recover from opioid addiction. This subreddit offers a platform to share experiences, coping strategies, and resources. Also includes the related subreddit OpiatesRecovery. \\ \hline Autism & A community that serves as a platform for individuals on the autism spectrum, as well as their family members, friends, and anyone else interested in learning about autism. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Subset of subreddits related to mental health; some smaller ones are left out. sulting in a loss of important contextual information about word meaning. Although some of these models are trained on data that potentially contain specific domain information, this information can be partially lost due to phenomena such as catastrophic forgetting [55]. In the domain of psychology, a project that follows this path is MentalBert, which involves a BERT model fine-tuned using Reddit data. However, MentalBert does not aim to develop a model for categorizing a range of clinically valid diagnoses.