Cyberbullying Detection on Twitter Using Deep Learning-Based Attention Mechanisms and Continuous Bag of Words Feature Extraction

Abstract

Since social media platforms are widely used and popular, they have given us more opportunities than we can even imagine. Despite all of the known benefits, some users may abuse these opportunities to humiliate, insult, bully, and harass other people. This issue explains why there is a need to reduce such negative activities and create a safe cyberspace for innocent people by detecting cyberbullying activity. This study provides a comparative analysis of deep learning methods used to test and evaluate their effectiveness regarding a well-known global Twitter dataset. To recognize abusive tweets and overcome existing challenges, attention-based deep learning methods are introduced. The word2vec with CBOW concatenated formed the weights included in the embedding layer and was used to extract the features. The feature vector was input into a convolution and pooling mechanism, reducing the feature dimensionality while learning the position-invariant of the offensive words. A SoftMax function predicts feature classification. Using benchmark experimental datasets and well-known evaluation measures, the convolutional neural network model with attention-based long- and short-term memory was found to outperform other DL methods. The proposed cyberbullying detection methods were evaluated using benchmark experimental datasets and well-known evaluation measures. Finally, the results demonstrated the superiority of the attention-based 1D convolutional long short-term memory (Conv1DLSTM) classifier over the other implemented methods.

A novel approach toward cyberbullying with intelligent recommendations using deep learning based blockchain solution

“… Advantages Disadvantages Techniques used Dataset Accuracy 1 Shetty et al ( 3 ) Masking of data at the before start of data preprocessing leading to higher data security High run time, two fold increase in computational load on the system. SBERT, Universal Sentence Encoders –DAN, Universal Sentence Encoders – Transformers Data from Kaggle, Youtube, Twitter 97.12% 2 Fati et al ( 8 ) Data pre processing is made a part of Deep Learning Methodologies, leading to a more holistic output. Running NLP and DL together leads to a dependency on one layer for processing the other layer.…”

Cyberbullying Detection on Social Media Using Stacking Ensemble Learning and Enhanced BERT

“…The Tensorflow library is used for applications such as computer vision and NLP is used to implement the proposed cyberbullying model and the other baseline models. The objective was to minimize the complexity of the model by removing unnecessary elements, such as the number of hidden nodes, and in the dense layer by finding optimal hyperparameters [47]. An input matrix of 35,873 tweets was constructed to divide the raw input data into tokens, which helped the cyberbullying model to understand the context and interpret the vital information in the text by analyzing the word sequence using tokenization in the Tensorflow library.…”

Correction: Fati et al. Cyberbullying Detection on Twitter Using Deep Learning-Based Attention Mechanisms and Continuous Bag of Words Feature Extraction. Mathematics 2023, 11, 3567

“…In the original paper [1], we mistakenly omitted Prince Sultan University (PSU) from the funding. PSU is one of the main funders for this paper, which was a joint effort between King Saud University, Universiti Teknologi Petronas, and Prince Sultan University.…”

“…In the original publication [1], the Funding statement was "The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project no. (IFKSUOR3-057-1)".…”