## 4 Experiments Setup
### Parameters of Methods  
We tested all combinations of hyperparameters' values and selected combinations with maximal classification accuracy. Due to a large number of experimental samples, only final values of the hyperparameters are presented below. Training parameters of ML models were determined empirically, based on the experience of previous studies [1, 52]:  
* For SVM training was used the sequential optimization method. The kernel was linear. The regularization parameter was 1, and the acceptable error rate is 0.00001. Normalization and compression heuristics were included as additional options.
* For KNN, different values of the parameter \(k\) were used: 3, 5, 7, 15, 25.
* To train LR were chosen: the liblinear optimization algorithm, regularization parameter 1, stopping criteria tolerance \(1\times 10^{-4}\), and limit number of 100 iterations.
* For DT training, gini was used as the partitioning quality function, and the maximum tree depth was 8.
* For RF training, 5, 15, 25, 35, and 50 decision trees were used.  
When training deep NNs, an embedding layer was used as an input layer with an output size equal to 300. The spatial dropout method was used with the parameter 0.3 on the next layer. The activation function of the output layer was the logistic function for the multidimensional case (Softmax). Loss function was categorical cross-entropy, optimization algorithm--adaptive moment estimation (Adam), metric was accuracy. Training parameters were selected based on the research experience in the field of text analysis [53, 54]:  
* 128 filters for LSTM and Bidirectional LSTM were chosen. Dropout and recurrent dropout were equal to 0.3 in both cases. Rectified linear unit (ReLU) was selected as an activation function.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Dataset Characteristic** & **Value of the Characteristic** \\ \hline Number of authors & 3075 \\ Number of texts & 202,892 \\ Dataset size, symbols & 30,652,109 \\ Dataset size, words & 4,708,619 \\ The average length of text, symbols & 151.1 \\ The average length of text, words & 23.7 \\ The average number of texts per author & 115.37 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the short texts’ dataset.  
* Number of convolution filters for CNN was 1024, GlobalMaxPooling was chosen as a pooling layer. For the CNN with CNN hybrid, a network with a number of 512 convolution filters was also involved. To prevent overfitting, spatial dropout value 0.2 was used. The activation function was similar to LSTM.
* Hyperparameters for LSTM with CNN and CNN with LSTM hybrids were: the number of convolution filters--256, number of recurrent filters--128, and kernel size was 3. The activation function was selected as a ReLU; The dropout was carried out similarly to LSTM and BiLSTM. The activation function was similar to CNN.
* For fastText, the number of \(n\)-grams was 2-4. The learning rate parameter was defined as 0.6, the dimension for short texts was 50, for long texts--500. As a loss function, 'ova' (Softmax loss for multi-label classification) was used. The maximum number of allocated memory segments was 2,000,000. The rest of the parameters were default.
* When training BERT, the tokenizers \"bert-base-multilingual-case\" and \"rubert-base-cased\" were used. A ReLU was used as an activation function for hidden layers, Softmax as an activation function for the output layer, Adam as an optimization algorithm. For regularization, dropout (0.1) was chosen. The learning rate (lr) was 4 \(\times\) 10\({}^{-5}\). The number of epochs was 5.