## 2 Related Work  
In [8], the authors explored the effects of COVID-19 on people's mental health using Weibo data (a Chinese social network). They used Online Ecological Recognition [23] that uses Naive Bayes (NB) [24] and Linear Regression [25] to detect psychological problems. The work [9] used Latent Dirichlet Allocation [26,27] to group behavior profiling from emotions (i.e., anger, fear, sadness, disgust, joy, anticipation, trust, and surprise) expressed in Twitter during the COVID-19 pandemic. The authors used Markov chains [28] to represent the transition between emotions. These works focused on analyzing positive and negative sentiments or emotions without employing ML models to classify depression. The work [10] identified depression and anxiety in the Brazilian population using RF and XGBoost models trained with data collected via a web survey requesting information about lifestyle behavior during the COVID-19 pandemic. Notably, these algorithms had been pre-trained with data collected in Spain. Ref. [11] detected the effects of depression suffered by the USA population during the COVID-19 pandemic using SVM and RF models trained with data collected via a web survey looking for indicators suggesting a predisposition to suffer mental disorders. Ref. [12] classified depression using SVM, RF, and NB algorithms trained with a few data (1000 samples) collected from Reddit's social network during the COVID-19 pandemic. Ref. [13] proposed a model based on SVM and k-nearest neighbor (KNN) to classify depression and compared it to SVM, RF, XGBoost, and CNN. The algorithms were trained using a few data (4996 samples) from Reddit's social network. The SVM + KNN model outperformed SVM, RF, XGBoost, and CNN. The work [14] used a Multi-Aspect Depression Detection with Hierarchical Attention Network (MDHAN) to estimate depression using tweets and user behavior data collected before the COVID-19 pandemic. MDHAN achieved better accuracy than SVM, BiGRU, MBiGRU, CNN, MCNN, and HAN. In [21], the author performed a benchmark between XGBoost, RF, CNN, Long Short-Term Memory (LSTM), BERT [22], and MentalBERT [29] when used to predict anxiety, bipolar, depression, and suicide watch from a small dataset comprising Reddit messages (16,205 samples related to depression) gathered before the COVID-19 pandemic; solutions based on BERT achieved better accuracy than other algorithms. Also, it is noteworthy that [21] did not show the process of labelling the mental illnesses dataset. The aforecited works (see Table 1) share three pivotal shortcomings. First, they trained the ML models with a small amount of depression-related data, mainly collected before the COVID-19 pandemic, limiting their learning. Second, they did not use a specific metric to measure the model performance in unbalanced datasets. Third, they did not present the process of labelling the depression-oriented dataset. Conversely, DEENT shows how to build a depression-oriented dataset with 123,984 tweets collected during the COVID-19 pandemic, during which the occurrence of depression grew exponentially; the labeling is performed using a BERT-based model and K-means clustering. Also, DEENT introduces two models based on encoder-only Transformer architecture to improve depression detection; the balanced accuracy and f1-score metrics measure their effectiveness in the unbalanced dataset built here.