A Bi-GRU with attention and CapsNet hybrid model for cyberbullying detection on social media

Abstract

As a constructive mode of information sharing, collaboration and communication, social media platforms offer users with limitless opportunities. The same hypermedia can be transposed into a synthetic and toxic milieu that provides an anonymous, destructive pedestal for online bullying and harassment. Automatic cyberbullying detection on social media using synthetic or real-world datasets is one of a proverbial natural language processing problem. Analyzing a given text requires capturing the existent semantics, syntactic and spatial relationships. Learning representative features automatically using deep learning models efficiently captures the contextual semantics and word order arrangement to build robust and superlative predictive models. This work puts forward a hybrid model, Bi-GRU-Attention-CapsNet (Bi-GAC), that benefits by learning sequential semantic representations and spatial location information using a Bi-GRU with self-attention followed by CapsNet for cyberbullying detection in the textual content of social media. The proposed Bi-GAC model is evaluated for performance using F1-score and ROC-AUC curve as metrics. The results show a superior performance to the existing techniques on the benchmark Formspring.me and MySpace datasets. In comparison to the conventional models, an improvement of nearly 9% and 3% in F-score is observed for MySpace and Formspring.me dataset respectively.

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

“…However, this model does not classify the highly class-imbalanced data effectively. Kumar and Sachdeva [51] proposed a hybrid approach to detect CB in social media. This approach integrates the capsule network (CapsNet) and Bi-GRU encoder, namely (Bi-GAC).…”

“…The authors investigated the effectiveness of FD utilizing linguistically-backed pre-processing such as stop words filtering, Parts of Speech (POS), Named Entity Recognition (NER), etc., approaches for assessing classification performance and the complexity of the dataset. On the other side, some recent studies presented multi-models to detect CB in 3 various modalities of social data networking, namely visual and info-graphic and textual such as [51][54] [55]. Kumari et al [56] presented DL based model to classify various levels of cyber aggression over networking social media comments in a bilingual.…”

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

“…This approach employs multi-channel DL based three methods, such a transformer block, the bidirectional gated recurrent unit (BiGRU), and convolution neural network (CNN), to categorize Tweet posts into two classes: not aggressive and aggressive. In [15], a hybrid method, Bi-GRU-Attention-CapsNet (Bi-GAC), benefitted by spatial location information and learning consecutive semantic representation through a Bi-GRU with self-attention afterward CapsNet for CB recognition in the text content of social network. The presented method is estimated to perform with ROC-AUC and curve F1-score as metrics.…”

Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks

“…NLP has extensive applications in this field, as authors have used various feature extraction methods for textual content. Fundamental attempts involve supervised categorization by utilizing bag-of-words at character-level representation through numerous conventional ML methods [10]. Deep learning (DL) methods were used for defeating the restrictions of conventional ML, reducing the manual feature extraction stage, and getting superior outcomes on large scale datasets.…”