## 4 Experimental Results
### Classification Models  
In addition to the HSAN, the classification performance of several other models was determined. The proposed HSAN classification model was compared with two machine learning classification algorithms and ten deep learning classification models. All the classification models were rebuilt and trained by this study. The aforementioned models are described in the following text.  
1. HSAN: Proposed model. This model has a hierarchical structure and uses a bidirectional GRU and squashing-attention network to encode text into hidden features. It uses the SoftMax function for classification.
2. GRU: This model uses a bidirectional GRU to encode text into hidden features. It has a nonhierarchical structure and uses the SoftMax function for classification.
3. GRU(Soft): This model is similar to the GRU model but includes a soft-attention network.
4. GRU(Squashing): This model is similar to the GRU model but includes a squashing-attention network.
5. HAN [13]: This model is a baseline model with a hierarchical structure.
6. SVM [46]: This baseline model uses the SVM algorithm. It uses TFIDF to transform documents into vectors.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Data Sets**} & \multirow{2}{*}{**No. of Samples**} & \multicolumn{3}{c}{**Severity**} \\ \cline{3-5}  & & **Slight** & **Medium** & **Serious** \\ \hline Training set & 3500 & 1190 (34\%) & 870 (25\%) & 1440 (41\%) \\ Validation set & 500 & 201 (40\%) & 241 (48\%) & 58 (12\%) \\ Test set & 1000 & 447 (45\%) & 459 (46\%) & 94 (9\%) \\ Total & 5000 & 1838 (100\%) & 1570 (100\%) & 1592 (100\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental data statistics.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Annotator** & **A** & **B** & **C** & **D** & **E** & **Average** \\ \hline MAE & 0.51 & 0.53 & 0.48 & 0.60 & 0.48 & 0.52 \\ \hline \hline \end{tabular}
\end{table}
Table 2: MAE of the five annotations and the final classification results.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Type** & **Number** & **Mean** & **Mode** & **Maximum** & **Minimum** \\ \hline Title & Word & 10 & 10 & 21 & 1 \\ Content & Sentence & 64 & 100 & 1193 & 3 \\ word & 5 & 1 & 54 & 1 \\ Comment & Sentence & 150 & 24 & 1500 & 20 \\ word & 7 & 1 & 28 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cyberbullying data statistics.  
* RandomForest [47]: This baseline model uses the random forest algorithm. It uses TFIDF to transform documents into vectors
* TextCNN [38]: This model uses word2 vec pretraining word embeddings as input and multiple CNN kernels with max-pooling to extract multi-level features from the text.
* TextRCNN [39]: This model uses bidirectional LSTM with max-pooling to extract critical features.
* DPCNN [48]: This model is known as the deep pyramid CNN. It contains a word-level CNN with max pooling. The amount of collected data is halved to form a pyramid structure.
* Transformer [49]: In this model, an encoder and a decoder are constructed through multilayer stacking and used with the self-attention mechanism.
* CapsuleNet [45]: This model comprises neurons. In CapsuleNet, dynamic routing of the last layer of category capsules is conducted. A new squashing function is used to compress the vector length of the category, which represents the final category probability.
* BERT: The model is known as the BERT classifier. It contains a pretrained BERT model with a final classification layer (linear layer). The study uses three pre-trained models such as _bert-base-chinese_[40], _distilbert-base-multilingual-cased_[42], and _allenai/longformer-base-4096_[41].