Cyberbullying Detection in Social Networks: Artificial Intelligence Approach

Abstract

Over the past decade, digital communication has reached a massive scale globally. Unfortunately, cyberbullying has become prevalent, with perpetrators hiding behind the mask of relative internet anonymity. In this work, efforts were made to review prominent classification algorithms and also to propose an ensemble model for identifying cases of cyberbullying, using Twitter datasets. The algorithms used for evaluation are Naive Bayes, K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, Linear Support Vector Classifier, Adaptive Boosting, Stochastic Gradient Descent and Bagging classifiers. Through experimentations, comparisons were made with the classifiers against four metrics: accuracy, precision, recall and F1 score. The results reveal the performances of all the algorithms used with their corresponding metrics. The ensemble model generated better results while Linear Support Vector Classifier (SVC) was the least effective of all. Random Forest classifier has shown to be the best performing classifier with medians of 0.77, 0.73 and 0.94 across the datasets. The ensemble model has shown to improve the results of its constituent classifiers with medians of 0.77, 0.66 and 0.94, as against the 0.59, 0.42 and 0.86 of Linear Support Vector Classifier.

Enhancing Learners' Performance in Contest Through Knowledge Mapping Algorithm

“…Anxiety is a psychological state that occurs when challenges are high and skills are low (Azeez et al, 2021). Anxiety is an important predictor of students' English grades.…”

Cyberbullying Conceptualization, Characterization and Detection in Social Media – A Systematic Literature Review

“…[46, 56, 84, 86, 93, 112, 113, 120-125, 129, 131, 134, 136]. Novel labelling techniques also used automated code observed [110,133] and a score-based system [119]. However, labelling is not always necessary, such as in [101], whose authors used an annotated dataset from a previous study.…”

A New Model for a Secure Social Media Application

“…While a multitude of techniques have attempted to work on the exactness of the cyberbullying-related task somewhat, we accept that they have not completely used precise data on past text-based information, which we call the setting in the context of this study [9]. The main classification of setting is an inner setting that incorporates any non-text-based data that can be separated from the dataset itself, e.g., the client's liked posts, adherents, or multimedia content connected to the social media post, e.g., pictures and recordings [10,11].…”