Hate Speech Detection using Word Embedding and Deep Learning in the Arabic Language Context

2020 ◽  
Author(s):  
Hossam Faris ◽  
Ibrahim Aljarah ◽  
Maria Habib ◽  
Pedro Castillo
Keyword(s):  
Deep Learning ◽  
Hate Speech ◽  
Arabic Language ◽  
Word Embedding ◽  
Speech Detection ◽  
Language Context

Detecting hate speech against politicians in Arabic community on social media

2020 ◽  
Vol 16 (3) ◽  
pp. 295-313
Author(s):  
Imane Guellil ◽  
Ahsan Adeel ◽  
Faical Azouaou ◽  
Sara Chennoufi ◽  
Hanene Maafi ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Hate Speech ◽  
Short Term Memory ◽  
Arabic Language ◽  
Short Term ◽  
Speech Corpus ◽  
Term Memory ◽  
Content Type ◽  
Speech Detection

Purpose This paper aims to propose an approach for hate speech detection against politicians in Arabic community on social media (e.g. Youtube). In the literature, similar works have been presented for other languages such as English. However, to the best of the authors’ knowledge, not much work has been conducted in the Arabic language. Design/methodology/approach This approach uses both classical algorithms of classification and deep learning algorithms. For the classical algorithms, the authors use Gaussian NB (GNB), Logistic Regression (LR), Random Forest (RF), SGD Classifier (SGD) and Linear SVC (LSVC). For the deep learning classification, four different algorithms (convolutional neural network (CNN), multilayer perceptron (MLP), long- or short-term memory (LSTM) and bi-directional long- or short-term memory (Bi-LSTM) are applied. For extracting features, the authors use both Word2vec and FastText with their two implementations, namely, Skip Gram (SG) and Continuous Bag of Word (CBOW). Findings Simulation results demonstrate the best performance of LSVC, BiLSTM and MLP achieving an accuracy up to 91%, when it is associated to SG model. The results are also shown that the classification that has been done on balanced corpus are more accurate than those done on unbalanced corpus. Originality/value The principal originality of this paper is to construct a new hate speech corpus (Arabic_fr_en) which was annotated by three different annotators. This corpus contains the three languages used by Arabic people being Arabic, French and English. For Arabic, the corpus contains both script Arabic and Arabizi (i.e. Arabic words written with Latin letters). Another originality is to rely on both shallow and deep leaning classification by using different model for extraction features such as Word2vec and FastText with their two implementation SG and CBOW.

scholarly journals Deep Learning for Hate Speech Detection in Tweets

2017 ◽  
Author(s):  
Pinkesh Badjatiya ◽  
Shashank Gupta ◽  
Manish Gupta ◽  
Vasudeva Varma
Keyword(s):  
Deep Learning ◽  
Hate Speech ◽  
Speech Detection

Application of Data Augmentation Techniques for Hate Speech Detection with Deep Learning

2021 ◽  
pp. 778-787
Author(s):  
Lígia Iunes Venturott ◽  
Patrick Marques Ciarelli
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Hate Speech ◽  
Speech Detection ◽  
Augmentation Techniques

scholarly journals Hate Speech Detection for Indonesia Tweets Using Word Embedding And Gated Recurrent Unit

2019 ◽  
Vol 13 (1) ◽  
pp. 43
Author(s):  
Junanda Patihullah ◽  
Edi Winarko
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Deep Learning ◽  
Hate Speech ◽  
Support Vector ◽  
Speech Detection ◽  
The People ◽  
Gated Recurrent Unit ◽  
Rule Out

Social media has changed the people mindset to express thoughts and moods. As the activity of social media users increases, it does not rule out the possibility of crimes of spreading hate speech can spread quickly and widely. So that it is not possible to detect hate speech manually. GRU is one of the deep learning methods that has the ability to learn information relations from the previous time to the present time. In this research feature extraction used is word2vec, because it has the ability to learn semantics between words. In this research the GRU performance will be compared with other supervision methods such as support vector machine, naive bayes, decision tree and logistic regression. The results obtained show that the best accuracy is 92.96% by the GRU model with word2vec feature extraction. The use of word2vec in the comparison supervision method is not good enough from tf and tf-idf.

Deep Learning for Hate Speech Detection in social media

2021 ◽  
Author(s):  
Ashwini Kumar ◽  
Vishu Tyagi ◽  
Sanjoy Das
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Hate Speech ◽  
Speech Detection

Arabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model

Informatics ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 69
Author(s):  
Wassen Aldjanabi ◽  
Abdelghani Dahou ◽  
Mohammed A. A. Al-qaness ◽  
Mohamed Abd Elaziz ◽  
Ahmed Mohamed Helmi ◽  
...  
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Detection System ◽  
Language Model ◽  
Arabic Language ◽  
Social Phenomena ◽  
Speech Detection ◽  
Task Learning ◽  
Opinion Expression ◽  
Social Media Platforms

As social media platforms offer a medium for opinion expression, social phenomena such as hatred, offensive language, racism, and all forms of verbal violence have increased spectacularly. These behaviors do not affect specific countries, groups, or communities only, extending beyond these areas into people’s everyday lives. This study investigates offensive and hate speech on Arab social media to build an accurate offensive and hate speech detection system. More precisely, we develop a classification system for determining offensive and hate speech using a multi-task learning (MTL) model built on top of a pre-trained Arabic language model. We train the MTL model on the same task using cross-corpora representing a variation in the offensive and hate context to learn global and dataset-specific contextual representations. The developed MTL model showed a significant performance and outperformed existing models in the literature on three out of four datasets for Arabic offensive and hate speech detection tasks.

Deep Learning Ensembles for Hate Speech Detection

2020 ◽  
Author(s):  
Safa Alsafari ◽  
Samira Sadaoui ◽  
Malek Mouhoub
Keyword(s):  
Deep Learning ◽  
Hate Speech ◽  
Speech Detection ◽  
Learning Ensembles

scholarly journals Detecting White Supremacist Hate Speech using Domain Specific Word Embedding with Deep Learning and BERT

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hind S. Alatawi ◽  
Areej M. Alhothali ◽  
Kawthar M. Moria
Keyword(s):  
Deep Learning ◽  
Hate Speech ◽  
Word Embedding ◽  
White Supremacist ◽  
Domain Specific

Indonesia Hate Speech Detection Using Deep Learning

2018 ◽  
Author(s):  
Taufic Leonardo Sutejo ◽  
Dessi Puji Lestari
Keyword(s):  
Deep Learning ◽  
Hate Speech ◽  
Speech Detection
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