# A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
## 2 Background and Related Work
### Machine Learning in Cyberbullying Detection  
Machine learning-based cyberbullying keywords are another direction of cyberbullying detection, which has been used widely by several researchers. Moreover, Machine learning (ML) is a branch of artificial intelligence technology that gives systems the capability to learn and develop automatically from experience without being specially programmed, often categorized as supervised, semi-supervised or unsupervised algorithms [55]. Several training instances in supervised algorithms are utilized to build a model that generates the desired prediction (i.e., based on annotated/labeled data). In contrast, unsupervised algorithms are not based on data and are mainly utilized for clustering problems [55; 56]. [PERSON] and [PERSON] [57] proposed a model for identifying offensive comments on social networks through filtering or informing those involved. They have used comments with offensive words fromTwitter and Ask.fm to train this model. Other authors [58; 59] built communication systems based on smart agents that provide supportive emotional input to victims suffering from cyberbullying. [PERSON] [48] suggested a method for detecting cyberbullying in the social network \"Formspring,\" focused on detecting aggressive trends in user messages, by analyzing offensive words; moreover, it uses a rating level of the threat identified. Similarly, J48 decision trees obtained an accuracy of 81.7%. Authors in [60] describe an online application implementation for school staff and parents in Japan, with a duty to detect inadequate content on non-official secondary websites. The goal is to report cyberbullying cases to federal authorities; they used SVMs in this work and obtained 79.9% accuracy. [PERSON] [61] has proposed a Facebook framework to protect underage users from cyberbullying and sex-teasing. The system seeks to evaluate the content of photographs and videos and the user's actions to monitor behavioral changes. A list of offensive words was made in [62] using 3915 posted messages monitored from the Formspring.me web site. The accuracy obtained in this study was only 58.5% [62]. Another study [47] suggests a method for identifying and classifying cyberbullying acts as harassment, flaming, terrorism, and racism. The author uses a fuzzy classification rule; therefore, the results are inferior in terms of accuracy (around 40%), but using a set of rules, improved the classifier efficiency by up to 90%. In [63], authors have developed a cyberbullying detection model based on Sentiment analysis in Hindi-English code-mixed language. The authors carried out their experiments based on Instagram and YouTube platforms. The authors use a hybrid model based on top performers of eight baseline classifiers, which perform better with an accuracy of 80.26% and an f1-score of 82.96%. [PERSON] et al. [64] suggested applying a real case of cyberbullying detection in Twitter using supervised machine learning. The study uses two different feature extraction techniques with various machine learning algorithms, and Sequential Minimal Optimization (SMO) classifier obtained (68.47%), the highest accuracy among the rest. In [65], the authors have proposed a cyberbullying detection approach based on Instagram's social network. The experiments were carried out based on image contents analysis and user's comments. The results show that uses multiple features can improve the classification accuracy of linear SVM, where the accuracy of SVM jumped from 0.72 to 0.78 by using image categories as an additional feature. [PERSON] et al. [66] propose creating a weighted directed graph model for cyberbullying that can be used to calculate each user's predator and victim scores while using a weighted TF-IDF scheme with textual features (second-person pronouns and foul words) to improve online bullying. [PERSON] et al. [67] suggest a hate content detection approach for multiple social media networks.