## 1 Introduction  
Cyberbullying has been a progressive and always developing issue all through most recent years, and thus programmed recognition of it has been an engaging point for scientists. Cyberbullying or disdain discourse is the demonstration of posting scornful substance on social media, which is found to be harmful because of its tedious and oppressive nature [1]. Cyberbullying can influence individuals' mental states and different parts of life. Particularly for youngsters, cyberbullying could even lead them to cause self-harm and self-destruction [2]. Scientists have likewise concentrated on the relationship between cyberbullying exploitation and the risk of self-destructive ideation [3, 4]. It is a fundamental and provoking task to distinguish cyberbullying. Concerning its significance, although the mischief of cyberbullying should be limited as much as can be expected, it is more vital to foster a programmed model to foresee cyberbullying occasions, rather than checking physically or helping victims a while later.  
The difficulties of cyberbullying discovery are as follows [5]. To begin with, according to the point of view of manual verification, the judgment of whether specific conduct is cyberbullying changes from one individual to another. For instance, when a sentence contains sweat words, it may very well be considered cyberbullying activity, yet now and again, this is not accurate, such as when some teenagers speak with their companions on social networks. After that, where cyberbullying happens most often [6], social organization's platforms generally have the idea of communicating with the public and unknown commenters. Thus, posts are more autonomous and context-free, and it is simple to cause doubt or misdirect others. Third, one significant issue with cyberbullying research is the absence of standard information [7]. Although the information utilized in numerous past investigations was obtained from similar social networks (e.g., Twitter), they were made by freely utilizing public API or by site scraping. Subsequently, individual data cannot be measured against each another and are not helpful for checking the technique's comprehensiveness.  
This study introduces a novel Artificial Fish Swarm Algorithm with Weighted Extreme Learning Machine (AFSA-WELM) model for cybersecurity on social media. The AFSA-WELM model presented here is mainly intended to detect the existence of cyberbullying on social media. The main contributions of this study are summarized as follows:  
* Introducing an efficient AFSA-WELM model to detect cyberbullying on social media using an artificial fish swarm algorithm and weighted extreme machine learning.
* Using the TF-IDF model for feature extraction, using the WELM model for classification, and applying the AFSA model for parameter tuning.
* The presented AFSA-WELM model has been validated through extensive experimental evaluations on various datasets. It achieved the maximum precision-recall performance for various datasets.  
Finally, the presented AFSA-WELM has shown maximum accuracy compared with existing algorithms such as the SVM and Naive Bayes algorithms.  
The rest of this study is organized as follows. Section 2 presents the related work and discussion. Section 3 introduces the proposed model. Section 4 presents the detailed results and discussion. This is followed by the conclusions in Section 5.