  With the increasing use of the internet and social media, it is obvious that
cyberbullying has become a major problem. The most basic way for protection
against the dangerous consequences of cyberbullying is to actively detect and
control the contents containing cyberbullying. When we look at today's internet
and social media statistics, it is impossible to detect cyberbullying contents
only by human power. Effective cyberbullying detection methods are necessary in
order to make social media a safe communication space. Current research efforts
focus on using machine learning for detecting and eliminating cyberbullying.
Although most of the studies have been conducted on English texts for the
detection of cyberbullying, there are few studies in Turkish. Limited methods
and algorithms were also used in studies conducted on the Turkish language. In
addition, the scope and performance of the algorithms used to classify the
texts containing cyberbullying is different, and this reveals the importance of
using an appropriate algorithm. The aim of this study is to compare the
performance of different machine learning algorithms in detecting Turkish
messages containing cyberbullying. In this study, nineteen different
classification algorithms were used to identify texts containing cyberbullying
using Turkish natural language processing techniques. Precision, recall,
accuracy and F1 score values were used to evaluate the performance of
classifiers. It was determined that the Light Gradient Boosting Model (LGBM)
algorithm showed the best performance with 90.788% accuracy and 90.949% F1
Score value.
