# A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
## 3 Materials and Methods
### Dataset  
Detecting cyberbullying in social media through cyberbullying keywords and using machine learning for detection are theoretical and practical challenges. From a practical perspective, the researchers are still attempting to detect and classify the offensive contents based on the learning model. However, the classification accuracy and the implementation of the right model remain a critical challenge to construct an effective and efficient cyberbullying detection model. In this study, we  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **Year** & **Feature** & **Classifier** & **Accuracy** & **Dataset used** \\ \hline [PERSON] and & & & & & They did not evaluate their & Twitter and Ask.fm \\ [PERSON] [57] & 2016 & N/A & N/A & proposed model & datasets \\ [PERSON], & & & Sequential Minimal & The model was capable of & Formspring \\ Kontostathis & 2011 & Bag of Words (BoW) & Optimization & recognizing 78.5\% & Link: www. \\ and [PERSON] [48] & & & JRip, J48 & posts in Formspring dataset & Formspring.me \\ [PERSON] et al. [66] & 2014 & TF-IDF unigrams & Ensemble & NA & MySpace, Slashdot, \\  & & & & & Kongregate, Twitter \\ [PERSON] et al. [47] & 2009 & TF-IDF & SVM & Kongregate (0.289) Slashdot & MySpace, Slashdot, \\  & & & & (0.273) MySpace (0.351) & Kongregate \\  & & & & Correctly identify 85.3\% as & \\ [PERSON] et al. [110] & 2011 & Second p Insult word & & cyberbullying in & \\  & & & & posts and 51.91\% as & MySpace \\  & & & & innocent posts of & \\  & & & & MySpace dataset & \\ [PERSON] et al. [26] & 2018 & Negative comments, & & & \\  & & & & & \\  & & & & & \\ [PERSON] et al. [64] & 2016 & TF-IDF, N-gram & NB, KNN, RE, J48, & - J48 (65.81) & Twitter \\  & & & SMO & - RF (66.48\%) & \\ [PERSON] et al. [60] & 2015 & Unigram & SVM, NB & - [PERSON] (71\%) & Twitter \\  & & & & - SVM (78\%) & \\ [PERSON] et al. [67] & 2020 & TI-IDF & LR, NB, SVM, & - NB (60.6\%) & \\  & & & XGBoost & - SVM (64.8\%) & \\  & & & & XGBoost (77.4\%) & \\ [PERSON] et al. [44] & 2012 & Profanity, BoW, & J48, SVM, NB & - NB (63\%) & \\  & & & & - J48 (61\%) & Formspring, Youtube \\  & & & & -SVM (72\%) & \\  & & & & - Rule-based Jrp (70.39\%) & \\ [PERSON] et al. [68] & 2013 & Longth, & SVM & NA & YouTube \\  & & & keywords, Pronouns & & \\ [PERSON] et al. [69] & 2018 & Character n-gram & & F1 score of 64\% and 61\% & Posts of ASKfm in \\  & & & BoW, Word n-gram & LSVM & for English and Dutch & Dutch \\ [PERSON] et al. [111] & 2019 & NA & LR, LSVM, RF & NA & Vine, Instagram \\  & & & & - LR (90.57\%) & \\  & & & & - LGBM (90.55\%) & \\ Authors in this & 2020 & TF-IDF and & LR, LGBM, SGD, & - SGD (90.6\%) & \\ study & 2020 & Word2 Vec & RF, AdaBoost, NB, & - RF (89.84\%) & Twitter \\  & & & and SVM & - AdaBoost (89.30\%) & \\  & & & & - NB (81.39\%) & \\  & & & & - SVM (67.13\%) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of The Related Studies.  
used a global dataset of 37,373 tweets to evaluate seven classifiers that are commonly used in cyberbully content detection. Therefore, our dataset is taken from two sources [8,45]; and has been divided into two parts. The first part contains 70% of the tweets used for training purposes, and the other part contains 30% used for predications purpose. The evolution of each classifier will be conducted based on the performance metrics, as discussed in Section 4.