Cyberbullying Detection: A Survey on Multilingual Techniques

Haidar, Batoul; Chamoun, Maroun; Yamout, Fadi

doi:10.1109/ems.2016.037

Cited by 28 publications

(13 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [25], Haider et al conducted a survey on multilingual cyberbullying detection. In the survey, they found that most of the work in this domain is focused on English texts.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Multilingual Cyberbullying Detection System

Pawar

Raje

2019

2019 IEEE International Conference on Electro Information Technology (EIT)

View full text Add to dashboard Cite

“…In [25], Haider et al conducted a survey on multilingual cyberbullying detection. In the survey, they found that most of the work in this domain is focused on English texts.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Multilingual Cyberbullying Detection System

Pawar

Raje

2019

2019 IEEE International Conference on Electro Information Technology (EIT)

View full text Add to dashboard Cite

“…An overview of the published literature review papers between 2009 and 2021 on the topic of automated cyberbullying detection is provided in TABLE 1. The works shown in TABLE 1 cover the following aspects of the examined problem: systematic review or how the literature was collected [27], [31], [32]; cyberbullying definition [32], [33], [35], [39]; cyberbullying types [25], [26], [29]; datasets [25], [28], [32]; feature selection [27], [35], [36]; model selection [26], [27], [31]; and evaluation metrics [26], [30], [35]. However, only few are comprehensive [26], [30].…”

Section: Introductionmentioning

confidence: 99%

“…The works shown in TABLE 1 cover the following aspects of the examined problem: systematic review or how the literature was collected [27], [31], [32]; cyberbullying definition [32], [33], [35], [39]; cyberbullying types [25], [26], [29]; datasets [25], [28], [32]; feature selection [27], [35], [36]; model selection [26], [27], [31]; and evaluation metrics [26], [30], [35]. However, only few are comprehensive [26], [30]. There are some important aspects that are rarely covered in the literature, like data annotation [31] and data preprocessing [27].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

When the Timeline Meets the Pipeline: A Survey on Automated Cyberbullying Detection

et al. 2021

View full text Add to dashboard Cite

Web 2.0 helped user-generated platforms to spread widely. Unfortunately, it also allowed for cyberbullying to spread. Cyberbullying has negative effects that could lead to cases of depression and low self-esteem. It has become crucial to develop tools for automated cyberbullying detection. The research on developing these tools has been growing over the last decade, especially with the recent advances in machine learning and natural language processing. Given the large body of work on this topic, it is vital to critically review the literature on cyberbullying within the context of these latest advances. In this paper, we conduct a survey on the automated detection of cyberbullying. Our survey sheds light on some challenges and limitations for the field. The challenges range from defining cyberbullying, data collection and feature representation to model selection, training and evaluation. We also provide some suggestions for improving the task of cyberbullying detection. In addition to the survey, we propose to improve the task of cyberbullying detection by addressing some of the raised limitations: 1) Using recent contextual language models like BERT for the detection of cyberbullying; 2) Using slang-based word embeddings to generate better representations of the cyberbullying-related datasets. Our results show that BERT outperforms state-of-the-art cyberbullying detection models and deep learning models. The results also show that deep learning models initialised with slang-based word embeddings outperform deep learning models initialised with traditional word embeddings.

show abstract

“…As stated in the Abstract, this work is an extension to the author's previous work [1] which was published in the EMS2016 conference.…”

Section: Introductionmentioning

confidence: 85%

A Multilingual System for Cyberbullying Detection: Arabic Content Detection using Machine Learning

Haidar¹,

Chamoun²,

Serhrouchni³

2017

Adv. sci. technol. eng. syst. j.

Self Cite

View full text Add to dashboard Cite

With the abundance of Internet and electronic devices bullying has moved its place from schools and backyards into cyberspace; to be now known as Cyberbullying. Cyberbullying is affecting a lot of children around the world, especially Arab countries. Thus, concerns from cyberbullying are rising. A lot of research is ongoing with the purpose of diminishing cyberbullying. The current research efforts are focused around detection and mitigation of cyberbullying. Previously, researches dealt with the psychological effects of cyberbullying on the victim and the predator. A lot of research work proposed solutions for detecting cyberbullying in English language and a few more languages, but none till now covered cyberbullying in Arabic language. Several techniques contribute in cyberbullying detection, mainly Machine Learning (ML) and Natural Language Processing (NLP). This journal extends on a previous paper to elaborate on a solution for detecting and stopping cyberbullying. It first presents a thorough survey for the previous work done in cyberbullying detection. Then a solution that focuses on detecting cyberbullying in Arabic content is displayed and assessed.

show abstract

Cyberbullying Detection: A Survey on Multilingual Techniques

Cited by 28 publications

References 18 publications

Multilingual Cyberbullying Detection System

Multilingual Cyberbullying Detection System

When the Timeline Meets the Pipeline: A Survey on Automated Cyberbullying Detection

A Multilingual System for Cyberbullying Detection: Arabic Content Detection using Machine Learning

Contact Info

Product

Resources

About