Cyberbullying Identification System Based Deep Learning Algorithms

Aldhyani, Theyazn H. H.; Al-Adhaileh, Mosleh Hmoud; Alsubari, Saleh Nagi

doi:10.3390/electronics11203273

Cited by 20 publications

(11 citation statements)

References 32 publications

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“…The preprocessing technique reduces unnecessary data and makes it more classifiable. Certainly, there are many pre-processing operations done by different researchers to clean the datasets (special characters, punctuation, white spaces, contractions, and stop words), convert all characters to lowercase, as well as return the words to their root using stemming or lemmatization [32][33][34]. Feature Extraction: A PC can utilize advanced information.…”

Section: Methodsmentioning

confidence: 99%

A Review of the Past Decade's Research Outcomes on Classifying Cyberbullying

Haider Ameen,

Saad Al-Mukhtar,

Qahtan Ahmed

et al. 2024

ANJS

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Cyberbullying is a new form of online violence that has emerged as a result of the social media industry's explosive expansion since it allows for indirect communication. Despite the usefulness of digital media, it has been used by weak people to threaten and bully others online. In the last ten years, research has shown that children and teenagers are increasingly experiencing cyberbullying as a concern. This paper examines the research conducted during the previous ten years, categorizes it, and presents statistical analyses of the data collected during that time. A table is used to present various data, including the dataset that was used, its size (number of samples, posts, or messages), the methods that were employed, and the metrics that were gathered from the examined research that was taken from the publications that were looked into. This survey will be helpful to everyone who wants to advance their understanding of how machine learning may be used to identify cyberbullying, and it may help create a social media environment that is safe and relatively healthy by automatically identifying bullying communications.

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Section: Methodsmentioning

confidence: 99%

A Review of the Past Decade's Research Outcomes on Classifying Cyberbullying

Haider Ameen,

Saad Al-Mukhtar,

Qahtan Ahmed

et al. 2024

ANJS

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“…6) show the accuracy, precision, recall, and F1-score of BERT model. In an attempt to further increase model performance, [30] have used a modified version of this dataset where the other cyberbullying class has been removed from the dataset.…”

Section: F P R E C I S I O N R E C a L L P R E C I S I O N R E C A L ...mentioning

confidence: 99%

Comparative Analysis of Word Embeddings for Multiclass Cyberbullying Detection

Faraj,

Utku

2024

UHD J SCI TECH

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Cyberbullying has emerged as a pervasive concern in modern society, particularly within social media platforms. This phenomenon encompasses employing digital communication to instill fear, threaten, harass, or harm individuals. Given the prevalence of social media in our lives, there is an escalating need for effective methods to detect and combat cyberbullying. This paper aims to explore the utilization of word embeddings and to discern the comparative effectiveness of trainable word embeddings, pre-trained word embeddings, and fine-tuned language models in multiclass cyberbullying detection. Distinguishing from previous binary classification methods, our research delves into nuanced multiclass detection. The exploration of word embeddings holds significant promise due to its ability to transform words into dense numerical vectors within a high-dimensional space. This transformation captures intricate semantic and syntactic relationships inherent in language, enabling machine learning (ML) algorithms to discern patterns that might signify cyberbullying. In contrast to previous research, this work delves beyond primary binary classification and centers on the nuanced realm of multiclass cyberbullying detection. The research employs diverse techniques, including convolutional neural networks and bidirectional long short-term memory, alongside well-known pre-trained models such as word2vec and bidirectional encoder representations from transformers (BERT). Moreover, traditional ML algorithms such as K-nearest neighbors, Random Forest, and Naïve Bayes are integrated to evaluate their performance vis-à-vis deep learning models. The findings underscore the promise of a fine-tuned BERT model on our dataset, yielding the most promising results in multiclass cyberbullying detection, and achieving the best-recorded accuracy of 85% on the dataset.

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“…The LSTM contains a memory that saves the most important information and neglects the less important information through four gates: forgets gate, input gate, cell state, and output gate. Figure 1 shows the LSTM structure [4], [16].…”

Section: Preliminariesmentioning

confidence: 99%

“…When victims experience cyberbullying, they might start to feel ashamed, nervous, anxious, and insecure about what people say or think about them. This can lead to withdrawal from friends and family, and it may lead to the victim's suicide [3], [4]. So, it has become necessary to search for and find solutions to detect cyberbullying messages.…”

Section: Introductionmentioning

confidence: 99%

Word embedding for detecting cyberbullying based on recurrent neural networks

Shaker,

Dhannoon

2024

IJ-AI

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<span lang="EN-US">The phenomenon of cyberbullying has spread and has become one of the biggest problems facing users of social media sites and generated significant adverse effects on society and the victim in particular. Finding appropriate solutions to detect and reduce cyberbullying has become necessary to mitigate its negative impacts on society and the victim. Twitter comments on two datasets are used to detect cyberbullying, the first dataset was the Arabic cyberbullying dataset, and the second was the English cyberbullying dataset. Three different pre-trained global vectors (GloVe) corpora with different dimensions were used on the original and preprocessed datasets to represent the words. Recurrent neural networks (RNN), long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and Bidirectional GRU (BiGRU) classifiers utilized, evaluated and compared. The GRU outperform other classifiers on both datasets; its accuracy on the Arabic cyberbullying dataset using the Arabic GloVe corpus of dimension equal to 256D is 87.83%, while the accuracy on the English datasets using 100 D pre-trained GloVe corpus is 93.38%.</span>

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Cyberbullying Identification System Based Deep Learning Algorithms

Cited by 20 publications

References 32 publications

A Review of the Past Decade's Research Outcomes on Classifying Cyberbullying

A Review of the Past Decade's Research Outcomes on Classifying Cyberbullying

Comparative Analysis of Word Embeddings for Multiclass Cyberbullying Detection

Word embedding for detecting cyberbullying based on recurrent neural networks

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