Indonesian Twitter Cyberbullying Detection using Text Classification and User Credibility

Nurrahmi, Hani; Nurjanah, Dade

doi:10.1109/icoiact.2018.8350758

Cited by 41 publications

(28 citation statements)

References 6 publications

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“…On the other hand, Bai et al [31] focused on detecting offensive speech in German social media through a binary classification scheme that considers traditional supervised classifiers and neural networks models. Most recently, Nurrahmi and Nurjanah [32] employed text processing and machine learning techniques to detect bullies from the automatic analysis of Twitter posts written in the Indonesian language. Also, Febriana and Budiarto [33] constructed a dataset of Twitter posts collected during the presidential election period in Indonesia to promote the detection of hateful speech and tested its usefulness by submitting it to a basic sentiment analysis model.…”

Section: Related Workmentioning

confidence: 99%

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

Chavez¹,

Marianne²

2020

IJACSA

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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.

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Section: Related Workmentioning

confidence: 99%

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

Chavez¹,

Marianne²

2020

IJACSA

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“…Nurrahmi et al, in [30], proposed a model, that not only detects cyberbullying but also keeps track of bullying between users, which can help to determine the credibility of the user. Their aim is to detect cyberbullying for the Indonesian Language.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Multilingual Cyberbullying Detection System

Pawar

Raje

2019

2019 IEEE International Conference on Electro Information Technology (EIT)

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“…Saat ini penggunaan media sosial dapat dikatakan sebagai salah satu bentuk kegiatan yang tidak bisa dilepaskan dari keseharian kita, media sosial dapat dijadikan sebagai sebuah sarana untuk melakukan komunikasi, berbagi informasi dan juga untuk mengemukakan suatu pendapat, dampak dari penggunaan media sosial dapat memberikan hal yang bersifat positif maupun negatif yang dapat memengaruhi kehidupan seseorang [1] Kelas Netral Bukan kelas Netral…”

Section: Pendahuluanunclassified

Klasifikasi Ujaran Kebencian pada Media Sosial Twitter Menggunakan Support Vector Machine

Rahman

Abdillah

Komarudin

2021

RESTI

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Nowadays social media has become a place for peoples to express their opinions, there are many ways that can be done to express both positive and negative opinions. Hate speech is one of the problems that we find quite a lot in cyberspace, that things can be detrimental to many parties. Twitter as one of social media, can be used as a source of analysis about people's behavior in cyberspace. Many of our society that unconsciously act of hate speech on social media, therefore this study finds out how people's behavior patterns in cyberspace and the main issue of hate speech on a particular topic and time period by classify it into five classes, namely ethnicity, religion, race, inter-groups and neutral using Support Vector Machine. In this study also compares three kernel that common to use and the result is the system can classify hate speech by using RBF kernel and got the highest result with 93% accuracy on 700 data train and 300 data test.

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Indonesian Twitter Cyberbullying Detection using Text Classification and User Credibility

Cited by 41 publications

References 6 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

Klasifikasi Ujaran Kebencian pada Media Sosial Twitter Menggunakan Support Vector Machine

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