# A Review on Deep-Learning-Based Cyberbullying Detection
## 4 Data Representation Techniques
### Text Data Representation
#### 4.1.8 Efficacy of Various Embeddings for Detecting Cyberbullying  
In this subsection, we will provide a concise overview of the efficacy of identifying cyberbullying behaviors. The ability of Word2 Vec to grasp the semantic meaning of words is crucial in detecting cyberbullying. [PERSON] et al. [75] demonstrates a comparative analysis of different embeddings along with Word2 Vec. For example, the word \"ugly\" can be used in cyberbullying to insult someone's physical appearance and cause emotional harm. However, it can also be used metaphorically in a harmless context, such as expressing dislike for a piece of clothing by saying \"That shirt is so ugly\". Word2 Vec can differentiate harmful and harmless messages in cyberbullying detection. The mapping between the target word to its context word implicitly builds the relationship into the vector space of words, which can be inferred by these word vectors. GloVe uses global matrix factorization \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Word-Embedding Technique** & \begin{tabular}{c} **Context Sensitive** \\ **Embedding** \\ \end{tabular} & **ML Based** & **RNN Based** & \begin{tabular}{c} **Transformer** \\ **Based** \\ \end{tabular} & **Pretrained** & \begin{tabular}{c} **Used in Cyberbullying** \\ **Application** \\ \end{tabular} \\ \hline One-hot Embedding & No & No & No & No & No & YouTube Bengali text [18] \\ \hline TF-IDF & No & No & No & No & No & \begin{tabular}{c} Chinese Weibo dataset and English tweets [5], Twitter \\ English text [63], YouTube \\ Bengali text [18] \\ \end{tabular} \\ \hline Word2 Vec & No & Yes & No & No & Yes & \begin{tabular}{c} Twitter Indonesian text [64], Twitter English text [63; 26] \\ Social media text [65; 66] \\ \end{tabular} \\ \hline GloVe & No & Yes & No & No & Yes & \begin{tabular}{c} Twitter English text [2; 67; 67] \\ and Wikipedia posts [25; 68], YouTube English text [25], Social media text [65] \\ \end{tabular} \\ \hline ELMo & Yes & Yes & Yes & No & Yes & \begin{tabular}{c} Social media text [65], \\ text [69; 70; 71] \\ \end{tabular} \\ \hline fastText & No & Yes & No & No & Yes & \
\begin{tabular}{c} Formspring English text [70], \\ Social media text [72] \\ \end{tabular} \\ \hline BERT & Yes & Yes & No & Yes & Yes &
\begin{tabular}{c} Arabic Social media text [73], \\ Formspring, Twitter, \\ Wikipedia English posts [22] \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison among different data-representation techniques. to generate word vectors, which can be particularly useful in examining the context of words. One of the advantages of using GloVe is that it can effectively grasp the associations between words and their co-occurrence patterns, allowing for a more subtle understanding of the meaning behind the text. For example, a model can be trained to categorize a text as cyberbullying or non-cyberbullying based on the presence or absence of certain word clusters identified by GloVe. FastText is similar to Word2 Vec but uses subword information to generate embeddings. The main advantage of FastText is its capability to handle morphological variations in text, which are common in cyberbullying messages, and thus, it can make precise predictions. Below are some studies that have compared the effectiveness of various word embedding techniques. [PERSON] et al. (2016) conducted a study to evaluate the performance of different word-embedding techniques, including Bag of Words (BoW), TF-IDF, word2 vec, GloVe, FastText, and several language models (ALBERT, ELECTRA, GPT-2, XL-NET, and RoBERTa), in detecting cyberbullying using a Twitter dataset labeled for sexism and racism. The study found that the majority of language models achieved high F1 scores compared to traditional word embeddings such as BoW and TF-IDF, as well as semantic word embeddings such as word2 vec, GloVe, and FastText. [PERSON] et al. (2017) investigated the efficacy of linguistically backed word embeddings in detecting cyberbullying.