Hierarchical Multi-label Classification to Identify Hate Speech and Abusive Language on Indonesian Twitter

Prabowo, Faizal Adhitama; Ibrohim, Muhammad Okky; Budi, Indra

doi:10.1109/icitacee.2019.8904425

Cited by 17 publications

(27 citation statements)

References 3 publications

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“…Prabowo et.al [20] proposed a classification process to recognize Indonesian abusive comments and hate speech on Twitter by implementing SVM. From the result, it was discovered that SVM with the help of the word unigram feature yields quite good results compared to other methods.…”

Section: Related Workmentioning

confidence: 99%

Abusive comment identification on Indonesian social media data using hybrid deep learning

Sari

Ardilla²,

Hayatin³

et al. 2022

IJ-AI

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<span lang="EN-US">Half of the entire social media users in Indonesia has experienced cyberbullying. Cyberbullying is one of the treatments received as an attack with abusive words. An abusive word is a word or phrase that contained harassment and is expressed be it spoken or in the form of text. This is a serious problem that must be controlled because the act has an impact on the victim's psychology and causes trauma resulting in depression. This study proposed to identify abusive comments from social media in Indonesian language using a deep learning approach. The architecture used is a hybrid model, a combination between <a name="_Hlk99537661"></a>recurrent neural network (RNN) and long short-term memory (LSTM). RNN can map the input sequences to fixed-size vectors on hidden vector components and LSTM implemented to overcome gradient vector growth components that have the potential to exist in RNN. The steps carried out include preprocessing, modelling, implementation, and evaluation. The dataset used is indonesian abusive and hate speech from Twitter data. The evaluation result showed that the model proposed produced an f-measure value of 94% with an increase in accuracy of 23%.</span>

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Section: Related Workmentioning

confidence: 99%

Abusive comment identification on Indonesian social media data using hybrid deep learning

Sari

Ardilla²,

Hayatin³

et al. 2022

IJ-AI

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“…Penelitian sebelumnya yang membahas mengenai multilabel ujaran kebencian pada teks Twitter telah dilakukan menggunakan algoritma Support Vector Machine (SVM) [9]. Penelitian ini menerapkan desain Hierarchical Multi-Label Classification (HMC) dalam melakukan pengujiannya.…”

Section: Metodologi Penelitian 21 Literatur Reviewunclassified

“…Lebih detail mengenai gambaran hirarki label pada dataset yang digunakan dapat dilihat pada Gambar 1. Penelitian pada literatur [9] berhasil mendapatkan akurasi terbaik sebesar 68,43%. Akurasi ini didapatkan dengan mereduksi jumlah label dari 12 menjadi 9.…”

Section: Metodologi Penelitian 21 Literatur Reviewunclassified

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Identifikasi Ujaran Kebencian Multilabel Pada Teks Twitter Berbahasa Indonesia Menggunakan Convolution Neural Network

Dwitama

Hidayat²

2021

json

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There has been a significant increase in communication activities between internet users in online media due to the increase in social media users. For instance, Twitter users may send messages via their tweets. However, tweets can also contain negative meanings. Therefore, it deserves special attention as it has the potential to contain hate speech. Even the government deems it necessary to publish regulations to deal with hate speech cases such as the Information and Electronic Transactions Law (ITE Law) issued in 2018 Article 28 paragraph 2 of the Hate Speech. Machine Learning (ML) is one of the techniques that can be used in identifying patterns. There are various types of data that ML can be applied to, including text (known as Text Analytic). Previous research has used the Support Vector Machine (SVM) method to identify hate speech on Twitter text with more than one label (multilabel). The purpose of this study was to identify hate speech on Twitter with a label of more than one (multilabel) via Convolutional Neural Network (CNN). The study obtained the best CNN model with an accuracy of 98.76% from the multi-label dataset on hate speech in Indonesian texts

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“…Much work on the toxic comments detection been carried out regarding different data sources. For example, Prabowo and colleagues evaluated Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest Decision Tree (RFDT) algorithms for detecting hate speech and abusive language on Indonesian Twitter [34]. The experimental results demonstrated an accuracy of 68.43% for the hierarchical approach with word uni-gram features and the SVM model.…”

Section: Related Workmentioning

confidence: 99%

Toxic Comments Detection in Russian

Smetanin¹

2020

Computational Linguistics and Intellectual Technologies

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Currently, social network sites tend to be one of the major communication platforms in both offline and online space. Freedom of expression of various points of view, including toxic, aggressive, and abusive comments, might have a long-term negative impact on people’s opinions and social cohesion. As a consequence, the ability to automatically identify and moderate toxic content on the Internet to eliminate the negative consequences is one of the necessary tasks for modern society. This paper aims at the automatic detection of toxic comments in the Russian language. As a source of data, we utilized anonymously published Kaggle dataset and additionally validated its annotation quality. To build a classification model, we performed fine-tuning of two versions of Multilingual Universal Sentence Encoder, Bidirectional Encoder Representations from Transformers, and ruBERT. Finetuned RuBERT achieved F1 = 92.20%, demonstrating the best classification score. We made trained models and code samples publicly available to the research community.

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Hierarchical Multi-label Classification to Identify Hate Speech and Abusive Language on Indonesian Twitter

Cited by 17 publications

References 3 publications

Abusive comment identification on Indonesian social media data using hybrid deep learning

Abusive comment identification on Indonesian social media data using hybrid deep learning

Identifikasi Ujaran Kebencian Multilabel Pada Teks Twitter Berbahasa Indonesia Menggunakan Convolution Neural Network

Toxic Comments Detection in Russian

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