Cyberbullying in Text Content Detection: An Analytical Review

Azumah, Sylvia W; Elsayed, Nelly; ElSayed, Zag; Özer, Murat

doi:10.48550/arxiv.2303.10502

Cited by 3 publications

(4 citation statements)

References 43 publications

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“…The outputs of both layers are concatenated to form the final network output. During training, the parameters of the Bi-LSTM network are updated using backpropagation through time, which involves computing gradients of the loss function at each time step and propagating them backward through the network ( Azumah et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

Detecting cyberbullying using deep learning techniques: a pre-trained glove and focal loss technique

El Koshiry,

Eliwa,

Abd El-Hafeez

et al. 2024

PeerJ Computer Science

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This study investigates the effectiveness of various deep learning and classical machine learning techniques in identifying instances of cyberbullying. The study compares the performance of five classical machine learning algorithms and three deep learning models. The data undergoes pre-processing, including text cleaning, tokenization, stemming, and stop word removal. The experiment uses accuracy, precision, recall, and F1 score metrics to evaluate the performance of the algorithms on the dataset. The results show that the proposed technique achieves high accuracy, precision, and F1 score values, with the Focal Loss algorithm achieving the highest accuracy of 99% and the highest precision of 86.72%. However, the recall values were relatively low for most algorithms, indicating that they struggled to identify all relevant data. Additionally, the study proposes a technique using a convolutional neural network with a bidirectional long short-term memory layer, trained on a pre-processed dataset of tweets using GloVe word embeddings and the focal loss function. The model achieved high accuracy, precision, and F1 score values, with the GRU algorithm achieving the highest accuracy of 97.0% and the NB algorithm achieving the highest precision of 96.6%.

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Section: Methodsmentioning

confidence: 99%

Detecting cyberbullying using deep learning techniques: a pre-trained glove and focal loss technique

El Koshiry,

Eliwa,

Abd El-Hafeez

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Victims are people who repeatedly receive hurtful and power-imbalanced messages and experiences. To conceal the identities of the individuals engaged in the cyberbullying case, some portions of the photograph have been blurred [30].…”

Section: B Definitions and Roles In Cyberbullyingmentioning

confidence: 99%

“…A real tweet from the social media platform Twitter that contains anactual instance of cyberbullying. To conceal the identities of the individuals engaged in the cyberbullying case, some portions of the photograph have been blurred[30].…”

mentioning

confidence: 99%

A Systematic Literature Review on Cyberbullying in Social Media: Taxonomy, Detection Approaches, Datasets, And Future Research Directions

Sahana V

2023

IJRITCC

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In the area of Natural Language Processing, sentiment analysis, also called opinion mining, aims to extract human thoughts, beliefs, and perceptions from unstructured texts. In the light of social media's rapid growth and the influx of individual comments, reviews and feedback, it has evolved as an attractive, challenging research area. It is one of the most common problems in social media to find toxic textual content. Anonymity and concealment of identity are common on the Internet for people coming from a wide range of diversity of cultures and beliefs. Having freedom of speech, anonymity, and inadequate social media regulations make cyber toxic environment and cyberbullying significant issues, which require a system of automatic detection and prevention. As far as this is concerned, diverse research is taking place based on different approaches and languages, but a comprehensive analysis to examine them from all angles is lacking. This systematic literature review is therefore conducted with the aim of surveying the research and studies done to date on classification of cyberbullying based in textual modality by the research community. It states the definition, , taxonomy, properties, outcome of cyberbullying, roles in cyberbullying along with other forms of bullying and different offensive behavior in social media. This article also shows the latest popular benchmark datasets on cyberbullying, along with their number of classes (Binary/Multiple), reviewing the state-of-the-art methods to detect cyberbullying and abusive content on social media and discuss the factors that drive offenders to indulge in offensive activity, preventive actions to avoid online toxicity, and various cyber laws in different countries. Finally, we identify and discuss the challenges, solutions, additionally future research directions that serve as a reference to overcome cyberbullying in social media.

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“…In addition, studies like Balakrishnan (2015) and Zhu et al (2021) have looked into the traits of cyberbullying perpetrators and victims, including age, gender, and online behaviour, offering insights into the factors contributing to involvement in cyberbullying incidents in Malaysia. Along this line, Zainudin et al (2016), Azman & Zamri, 2022), and Azumah et al (2023) have also investigated specific behaviours related to cyberbullying, such as the use of offensive language, spreading false information, and posting hurtful comments, aiming to categorise these actions and understand their motivations in cyberbullying. Besides these, studies by Balakrishnan & Fernandez (2018), Adebayo et al (2020), and Khairi et al (2022) have explored the impacts of cyberbullying, including the psychological, emotional, and social consequences of cyberbullying on Malaysian youth.…”

Section: Previous Studies On Cyberbullying Among Young Adults In Mala...mentioning

confidence: 99%

Cyberbullying in the Social Media Environment: Investigating Young Adults' Experiences in Malaysia

Mat So'od,

Ramamurthy,

Mohd Nawi

et al. 2023

E-BPJ

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The rise of social media in Malaysia has transformed communication, yet it has also ushered in cyberbullying. This study explores cyberbullying experiences among Malaysian young adults, focusing on their engagement, language use, and coping mechanisms. Data from 148 university students reveal that cyberbullying is prevalent on platforms like Facebook which is mainly instigated by strangers, online contacts, classmates, and friends. The most common cyberbullying types identified are physical-related and intelligence-related. While most participants acknowledged the harm of offensive language, a significant portion admitted to ignoring incidents, emphasising the need for targeted interventions and education to combat cyberbullying among Malaysian youth.

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Cyberbullying in Text Content Detection: An Analytical Review

Cited by 3 publications

References 43 publications

Detecting cyberbullying using deep learning techniques: a pre-trained glove and focal loss technique

Detecting cyberbullying using deep learning techniques: a pre-trained glove and focal loss technique

A Systematic Literature Review on Cyberbullying in Social Media: Taxonomy, Detection Approaches, Datasets, And Future Research Directions

Cyberbullying in the Social Media Environment: Investigating Young Adults' Experiences in Malaysia

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