## 1 Introduction
### Detection of Depression and Anxiety Disorders on the Twitter Platform  
In general, social media allows users to post and share their feelings and moods. This helped significantly analyze these contents in order to understand several mental disorders and make predictions accordingly. More specifically, the growing popularity of Twitter (known currently as X platform) has contributed to making it an excellent data source for performing such content analyses, in particular for depression and anxiety detection. Indeed, people with severe symptoms of mental disorders are affected in their professional, \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Type of Symptoms**} & **Depressive Disorder** & **Anxiety Disorder** \\ \hline \multicolumn{2}{c}{Age} & - & 35–45 \\ \cline{2-3} \multicolumn{2}{c}{Duration of the disorder} & 15 days & 6 months \\ \hline \multicolumn{2}{c}{Gender} & \multicolumn{2}{c}{Women \textgreater{}Male} \\ \cline{2-3} \multicolumn{2}{c}{Physical Diagnoses} & \begin{tabular}{c} In common with the same degree \\ \end{tabular} & \begin{tabular}{c} Disturbed sleep, fluctuations in appetite or weight, agitation, anxiety, isolation \\ (absenteeism) and sexual inhibition. \\ \end{tabular} \\ \cline{2-3} \multicolumn{2}{c}{In common but of} & \begin{tabular}{c} Intense fatigue (loss of energy) *** \\ \end{tabular} & Intense fatigue (loss of energy) * \\ \multicolumn{2}{c}{different degree} & \begin{tabular}{c} Suicidal thoughts *** \\ \end{tabular} & Suicidal thoughts * \\ \cline{2-3} \multicolumn{2}{c}{Which are not} & - & Dizziness, heart palpitations. \\ \hline \multicolumn{2}{c}{In common with the} & \begin{tabular}{c} Difficulty concentrating, fear, excessive worry and nightmares. \\ \end{tabular} \\ \cline{2-3} \multicolumn{2}{c}{\begin{tabular}{c} Psychological \\ diagnoses \\ \end{tabular} } & \
\begin{tabular}{c} In common but of \\ different degree \\ \end{tabular} & Sad/melancholy *** & Sad/melancholy * \\ \cline{2-3} \multicolumn{2}{c}{Which are not} &
\begin{tabular}{c} Loss of interest (loss of pleasure = anhedonia, \\ despair about the future), feelings of guilt or \\ failure, low self-esteem, \\ \end{tabular} & Panic attack \\ \hline \multicolumn{2}{c}{The symbols “x”, “** and “*** refer to the degree of symptom by taking values 33\%, 66\% and 100\%, respectively.} \\ \end{tabular}
\end{table} 
Table 1: Differences and commonalities between depressive and anxiety disorders [2]. family and social lives. This is why the automatic detection of these symptoms through social media would have important implications for those affected. In this paper, we focus on the analysis of data extracted from the Twitter platform (i.e., tweets) with the aim of developing models capable of detecting mental disorders in users, more specifically depression and anxiety. In this regard, much research has been conducted in order to understand the statements expressed through tweets and to classify them into positive and negative sentiments while taking into account certain parameters (e.g., population, language, etc.). Traditional approaches used classic machine learning algorithms such as decision trees and SVMs (support vector machines) (see for instance [3; 4; 5; 6; 7; 8; 9]). However, as the data volumes have become very large, recent research has shifted towards deep learning techniques such as recurrent neural networks (RNN) and convolutional neural networks (CNN) (see for example [10; 11]). Even if the detection of depressive and anxious disorders using deep learning could give satisfactory results, these approaches nevertheless mainly rely on binary classification models by treating each mental disorder separately (i.e., depressive or non-depressive/anxious or non-axious). This is because dealing with one single mental disorder is easier. Table 1 shows us the severity of the distinction between these mental disorders due to the existence of several symptoms in common (e.g., disturbed sleep, fluctuations, etc.).