Cyberbullying detection across social media platforms via platform-aware adversarial encoding

Yi, Peiling; Zubiaga, Arkaitz

doi:10.48550/arxiv.2204.00334

Cited by 1 publication

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“…The authors in [11] presented XP-CB, a new cross-platform structure dependent upon Transformer and adversarial learning. The XP-CB is to improve a Transformer leveraging unlabelled data in the source and target platforms for appearing with a general representation but preventing platform-specific trained.…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

Duhayyim¹,

Mohamed²,

Alotaibi³

et al. 2022

Computers, Materials &Amp; Continua

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Cyberbullying (CB) is a challenging issue in social media and it becomes important to effectively identify the occurrence of CB. The recently developed deep learning (DL) models pave the way to design CB classifier models with maximum performance. At the same time, optimal hyperparameter tuning process plays a vital role to enhance overall results. This study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification (TLGODL-CBC) model in Social Media. The proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media context. Initially, the input data is cleaned and pre-processed to make it compatible for further processing. Followed by, independent recurrent autoencoder (IRAE) model is utilized for the recognition and classification of CBs. Finally, the TLGO algorithm is used to optimally adjust the parameters related to the IRAE model and shows the novelty of the work. To assuring the improved outcomes of the TLGODL-CBC approach, a wide range of simulations are executed and the outcomes are investigated under several aspects. The simulation outcomes make sure the improvements of the TLGODL-CBC model over recent approaches.

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Section: Introductionmentioning

confidence: 99%