Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention Mechanism

Abstract

With the propagation of cyberbullying in social networks as a trending subject, cyberbullying detection has become a social problem that researchers are concerned about. Developing intelligent models and systems helps detect cyberbullying automatically. This work focuses on text-based cyberbullying detection because it is the commonly used information carrier in social networks and is the widely used feature in this regard studies. Motivated by the documented success of neural networks, we propose a complete model combining the bidirectional gated recurrent unit (Bi-GRU) and the self-attention mechanism. In detail, we introduce the design of a GRU cell and Bi-GRU’s advantage for learning the underlying relationships between words from both directions. Besides, we present the design of the self-attention mechanism and the benefit of this joining for achieving a greater performance of cyberbullying classification tasks. The proposed model could address the limitation of the vanishing and exploding gradient problems. We avoid using oversampling or downsampling on experimental data which could result in the overestimation of evaluation. We conduct a comparative assessment on two commonly used datasets, and the results show that our proposed method outperformed baselines in all evaluation metrics.

DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform

“…However, it cannot handle high-dimensional data with such accuracy. Fang et al [20] designed a classification model that combines a self-Attention mechanism and bidirectional Gated Recurrent Unit (Bi-GRU) to detect cyberbullying in tweets. This model employed merit for learning the underlying relationships between words using BI-GRU and used it together with a self-attention mechanism to improve the cyberbullying tweets classification process.…”

“…Text classification based on supervised machine learning (ML) models are commonly used for classifying tweets into bullying and non-bullying tweets [8] [9], [10], [11], [12], [13], [14], [15], [16],and [17]. Deep learning (DL) based classifiers have also been used for classifying tweets into bullying and non-bullying tweets [18], [19], [20], [21], [22], and [7]. Supervised classifiers have low performance in case the class labels are unchangeable and are not relevant to the new events [23].…”

A New Model for a Secure Social Media Application

“…The researchers in [14] presented an ML technique that (i) fine-tunes variants of BERT, a deep attention-based language technique that can detect patterns in a long and noisy body of text; (ii) extracts contextual data from various sources involving external knowledge sources, metadata, and images, and uses those characteristics for complementing the learning method; and (iii) effectively integrates textual and contextual characteristics through boosting and a wide-ranging framework. Fang et al [15] presented the advantages of GRU cells and Bi-GRU for learning the fundamental relationships among words in these two directions. Rupesh et al [16] used machine learning algorithms to efficiently detect hate speech on social networks.…”

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

“…Thusly, strategies that address the two perspectives (content and network) ought to be streamlined to recognize and control forceful ways of behaving in complex frameworks [10]. [11] focused on text-based CB recognition since it is the widely employed data transporter in social networks and is extensively commonly featured. Inspired by the recognized achievement of neural networks, we proposed a comprehensive model that combines the self-attention model and bi-directional gated recurrent unit (Bi-GRU).…”