## 3 Research Methodology  
The proposed process uses multi-class classification models to categorize tweets as \"_normal_\", \"_potentially depressed_\" or \"_potentially anxious_\". In order to achieve these objectives, we rely on a rigorous methodology which allows us to obtain efficient classifiers by exploiting Twitter data. This process carries out a clear sequence of well-defined phases, as illustrated in Figure 1. In the following, we detail each phase by providing explanations on its role within the system.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref** & **Year** & **Data Source** & **Language** & **Prediction** & **Hybrid Approach** & **Accuracy (\%)** & **F1-Score (\%)** \\ \hline
[32] & 2022 & Twitter & English & Depression & MDHAN & 89.5 & 89.3 \\ \hline
[33] & 2023 & Twitter & English & Normal, Depression and Anxiety & CNN-BiLSTM & 88.93 & [Normal, Dep, Arx]: [86; 90; 91] \\ \hline
[34] & 2021 & Twitter & English & Depression & [NB, NBTree] & D1: [92; 34; 97.31] & - \\ \hline
[35] & 2023 & Twitter & Portuguese & Depression and Anxiety & [LogReg, LSTM, CNN, BERT] & - & Dep: [58; 53; 52; 63] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of recent studies using hybrid deep learning approaches to detect depression and anxiety disorders from different data sources.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Year** & **Source** & **Language** & **Prediction** & **DL Approach** & **Accuracy (\%)** & **F1-Score (\%)** \\ \hline
[24] & 2020 & Reddit & English & \begin{tabular}{c} Depression and \\ Non-Depression \\ \end{tabular} & [XGBoost, CNN] & [71.69, 75.13] & \
\begin{tabular}{c} (depression, \\ N-depression) \\ \end{tabular} &
\begin{tabular}{c} (depression, \\ N-depression) \\ \end{tabular} \\ \hline
[24] & 2020 & Reddit & English & \begin{tabular}{c} Anxiety and \\ Non-Anxiety \\ \end{tabular} & [XGBoost, CNN] & [70.41, 77.81] & \
\begin{tabular}{c} (Anxiety, \\ N-Anxiety) \\ \end{tabular} &
\begin{tabular}{c} (Anxiety) \\ \(\{55.92, 77.73\}, \\ (56.25, 85.14)] \\ \end{tabular} \\ \hline
[24] & 2020 & Reddit & English & \
\begin{tabular}{c} Bipolar and \\ Non-Bipolar \\ \end{tabular} & [XGBoost, CNN] & [85.53, 90.20] &
\begin{tabular}{c} (Bipolar, N-Bipolar) \\ \(\{53.59, 91.43\}, \\ (52.95, 94.53)] \\ \end{tabular} \\ \hline
[24] & 2020 & Twitter & English & Depression &
\begin{tabular}{c} SenseMood \\ system \\ \end{tabular} & 88.39 & 93.60 \\ \hline
[26] & 2021 & Twitter & English & Depression &
\begin{tabular}{c} LSTM-MDL-fine \\ tuner \\ \end{tabular} & 87.14 & - \\ \hline
[27] & 2022 & Twitter + Google & English & \
\begin{tabular}{c} Positive or Negative \\ Opinions about \\ COVID-19 \\ \end{tabular} &
\begin{tabular}{c} [Proposed Model, \\ CNN, BiGRU, \\ FastText, NBSVM, \\ DistilBERT1 \\ \end{tabular} & [85.8, 81.6, 79.7, \\ 79.6, 79.8, 85.5] & 79.6, 79.8, 85.5] \\ \hline
[28] & 2022 & Twitter & Arabic & Depression &
\begin{tabular}{c} Attention-based \\ Bi-LSTM \\ \end{tabular} & 83 & - \\ \hline
[29] & 2022 & Twitter & Hindi-English & Depression &
\begin{tabular}{c} [LSTM, BERT, \\ USE, Proposal] \\ \end{tabular} & [65; 60; 60; 67] & - \\ \hline
[30] & 2022 & Twitter & Indian & Depression &
\begin{tabular}{c} [CNN, LSTM, \\ Bi-LSTM] \\ \end{tabular} & [98.00, 94.84, 97.10] & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of recent studies using simple deep learning approaches to detect mental disorders from different data sources.