Multilingual cyberbullying detection system: Detecting cyberbullying in Arabic content

Haidar, Batoul; Chamoun, Maroun; Serhrouchni, Ahmed

doi:10.1109/csnet.2017.8242005

Cited by 52 publications

(35 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Özel et al [28] considered a feature selection stage within the machine learning pipeline, to detect cyberbullying in Turkish text messages using labeled data from Instagram and Twitter. Haidar et al [29] presented a machine learning-based approach to detect cyberbullying in the Arabic language from Twitter textual data collected across the Middle East Region countries. Furthermore, Mouheb et al [30] presented a real-time cyberbullying detection system in Twitter streams that classify bullying messages according to the offensive strength.…”

Section: Related Workmentioning

confidence: 99%

A Comparison of Classification Models to Detect Cyberbullying in the Peruvian Spanish Language on Twitter

Chavez¹,

Marianne²

2020

IJACSA

View full text Add to dashboard Cite

Cyberbullying is a social problem in which bullies' actions are more harmful than in traditional forms of bullying as they have the power to repeatedly humiliate the victim in front of an entire community through social media. Nowadays, multiple works aim at detecting acts of cyberbullying via the analysis of texts in social media publications written in one or more languages; however, few investigations target the cyberbullying detection in the Spanish language. In this work, we aim to compare four traditional supervised machine learning methods performances in detecting cyberbullying via the identification of four cyberbullying-related categories on Twitter posts written in the Peruvian Spanish language. Specifically, we trained and tested the Naive Bayes, Multinomial Logistic Regression, Support Vector Machines, and Random Forest classifiers upon a manually annotated dataset with the help of human participants. The results indicate that the best performing classifier for the cyberbullying detection task was the Support Vector Machine classifier.

show abstract

Section: Related Workmentioning

confidence: 99%

A Comparison of Classification Models to Detect Cyberbullying in the Peruvian Spanish Language on Twitter

Chavez¹,

Marianne²

2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…In the survey, they found that most of the work in this domain is focused on English texts. They attempted cyberbullying detection in the Arabic language in [26]. In their work, they used the ML learning approach to detect cyberbullying.…”

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

“…These works on the Arabic language can contribute to further research in cyberbullying detection on social media in Arabic-speaking people. Haider et al [76] collected tweets in the Arabic language from Twitter (35273 tweets) and manually annotated them as bullying and not bullying. F-measures for Naïve Bayes and SVM classifiers are 0.905 and 0.927, respectively.…”

Section: Text-based Cyberbullying Detectionmentioning

confidence: 99%

A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

Arif

2021

JISCR

View full text Add to dashboard Cite

Social media networks are becoming an essential part of life for most of the world’s population. Detecting cyberbullying using machine learning and natural language processing algorithms is getting the attention of researchers. There is a growing need for automatic detection and mitigation of cyberbullying events on social media. In this study, research directions and the theoretical foundation in this area are investigated. A systematic review of the current state-of-the-art research in this area is conducted. A framework considering all possible actors in the cyberbullying event must be designed, including various aspects of cyberbullying and its effect on the participating actors. Furthermore, future directions and challenges are also discussed.

show abstract

Multilingual cyberbullying detection system: Detecting cyberbullying in Arabic content

Cited by 52 publications

References 22 publications

A Comparison of Classification Models to Detect Cyberbullying in the Peruvian Spanish Language on Twitter

A Comparison of Classification Models to Detect Cyberbullying in the Peruvian Spanish Language on Twitter

Multilingual Cyberbullying Detection System

A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

Contact Info

Product

Resources

About