Detecting Offensive Language on Arabic Social Media Using Deep Learning

Mohaouchane, Hanane; Mourhir, Asmaa; Nikolov, Nikola S.

doi:10.1109/snams.2019.8931839

Cited by 59 publications

(36 citation statements)

References 26 publications

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“…The results showed that ensemble machine learning achieved better results than single learner machine learning and bagging ensemble machine learning classifiers was the best in detecting offensive language. A comparison of four neural network classifiers, which are Convolutional Neural Network (CNN), Bidirectional Long Short Term Memory (Bi-LSTM), attention Bi LSTM, and a combined CNN-LSTM on a was done in Mohaouchane et al (2019). The data set used was created in Alakrot et al (2018) and the results showed that the combined CNN-LSTM achieved the best recall and the CNN achieved the best accuracy and precision among the classifiers for detecting offensive on Arabic social media (Mohaouchane et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

Automatic detection of cyberbullying and threatening in Saudi tweets using machine learning

Al-Ghamdi¹

2021

Int. j. adv. appl. sci.

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Social media has become a major factor in people's lives, which affects their communication and psychological state. The widespread use of social media has formed new types of violence, such as cyberbullying. Manual detection and reporting of violent texts in social media applications are challenging due to the increasing number of social media users and the huge amounts of generated data. Automatic detection of violent texts is language-dependent, and it requires an efficient detection approach, which considers the unique features and structures of a specific language or dialect. Only a few studies have focused on the automatic detection and classification of violent texts in the Arabic Language. This paper aims to build a two-level classifier model for classifying Arabic violent texts. The first level classifies text into violent and non-violent. The second level classifies violent text into either cyberbullying or threatening. The dataset used to build the classifier models is collected from Twitter, using specific keywords and trending hashtags in Saudi Arabia. Supervised machine learning is used to build two classifier models, using two different algorithms, which are Support Vector Machine (SVM), and Naive Bayes (NB). Both models are trained in different experimental settings of varying the feature extraction method and whether stop-word removal is applied or not. The performances of the proposed SVM-based and NB-based models have been compared. The SVM-based model outperforms the NB-based model with F1 scores of 76.06%, and 89.18%, and accuracy scores of 73.35% and 87.79% for the first and second levels of classification, respectively.

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Section: Related Workmentioning

confidence: 99%

Automatic detection of cyberbullying and threatening in Saudi tweets using machine learning

Al-Ghamdi¹

2021

Int. j. adv. appl. sci.

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“…In other work, Mohaouchane et al [23] measured the results of four different types of neural networks on the automatic detection of hate language. These models are CNN and LSTM bidirectional with or without attention, and CNN-LSTM.…”

Section: Related Workmentioning

confidence: 99%

Hate Speech in the Arab Electronic Press and Social Networks

Awane¹,

Benlahmar²,

Falaki³

2021

RIA

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Nowadays we are witnessing an open world, characterized by globalization which is accompanied by a technology through which information circulates without borders, especially with the widespread use of social networking sites being the most common communication tool, that gives access through various applications to a large space for the presentation of multiple ideas, including extremist ideas, and the spread of hate speech. This paper introduces a system of detection of hate speech in the texts of Arabic read media and social media, which is based on a combined use of NLP, and machine learning methods. The training of the detection model is done on a large Dataset of articles, tweets and comments, collected, balanced and tokenized afterwards using BERT in Arabic. The trained model detects hate speech in Arabic and various Arabic based dialects, by classifying the texts into two classes: Neutral and Abusive. The above-mentioned model is evaluated using precision metrics, recall and f1 score, it has reached an accuracy of 83%.

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“…TF-IDF vectors are commonly used in text classification. AraVec is an open source pre-trained word embedding for the Arabic language (Soliman et al, 2017) which has been used for detecting offensive language in Arabic YouTube comments (Mohaouchane et al, 2019) and for detecting religious hate speech on Twitter (Albadi et al, 2018). AraVec provides multiple models for generating word embeddings based on two main factors: (1) the technique used in building the embeddings, which is either skip-gram or Continuous Bag-Of-Word (CBOW), and ( 2) the corpus on which the embeddings are trained, either a Twitter corpus, a World Wide Web pages corpus, or an Arabic Wikipedia articles corpus that are collected by et al ( 2017).…”

Section: Feature Engineeringmentioning

confidence: 99%

SalamNET at SemEval-2020 Task 12: Deep Learning Approach for Arabic Offensive Language Detection

Husain

Lee

Henry

et al. 2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

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This paper describes SalamNET, an Arabic offensive language detection system that has been submitted to SemEval 2020 shared task 12: Multilingual Offensive Language Identification in Social Media. Our approach focuses on applying multiple deep learning models and conducting in depth error analysis of results to provide system implications for future development considerations. To pursue our goal, a Recurrent Neural Network (RNN), a Gated Recurrent Unit (GRU), and Long-Short Term Memory (LSTM) models with different design architectures have been developed and evaluated. The SalamNET, a Bi-directional Gated Recurrent Unit (Bi-GRU) based model, reports a macro-F1 score of 0.83.

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Detecting Offensive Language on Arabic Social Media Using Deep Learning

Cited by 59 publications

References 26 publications

Automatic detection of cyberbullying and threatening in Saudi tweets using machine learning

Automatic detection of cyberbullying and threatening in Saudi tweets using machine learning

Hate Speech in the Arab Electronic Press and Social Networks

SalamNET at SemEval-2020 Task 12: Deep Learning Approach for Arabic Offensive Language Detection

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