Identification and Detection of Cyberbullying on Facebook Using Machine Learning Algorithms

Abstract

The use of social media platforms such as Facebook, Twitter, Instagram, WhatsApp, etc. have enabled a lot of people to communicate effectively and frequently with each other and this has enabled cyberbullying to occur more frequently while using these networks. Cyberbullying is known to be the cause of some serious health issues among social media users and creating a way to identify and detect this holds significant importance. This paper takes a look at unique features gotten from the Facebook dataset and develops a model that identifies and detect cyberbullying posts by applying machine learning algorithms (Naïve Bayes Algorithm and K-Nearest Neighbor). The project also uses a feature selection algorithm namely x2 test (Chi-Square test) to select important features which can improve the performance of the classifiers and decrease classification time. The result of this paper tends to detect cyberbullying in Facebook with a high degree of accuracy and also improve the performance of the machine learning classifiers.

An Intelligent Framework for Log Anomaly Detection Based on Log Template Extraction

“…Chaudhary et al (2021) uses multiple machine learning (ML) algorithms that can predict future values of solar radiation based on previously observed values and other environmental features measured. Similar applications exist in many other fields, such as recommendation systems (Tanwar et al, 2022), social networks (Azeez et al, 2021), financial systems (Naveed et al, 2023), and medical fields (Gupta & Gupta, 2019).…”

Cyberbullying Detection and Severity Determination Model

“…Cyberbullying detection is valuable because it assists in identifying and classifying cyberbullying activities, allows incidents to be dealt with after they have been identified, and helps internet users to take action to avoid becoming victims of cyberbullying [5]. The detection of cyberbullying occurring on social media platforms is difficult mainly because the interpretation of cyberbullying can vary from person to person, especially when classifying its severity: what might be a case of extreme severity for one person might not be for others.…”