A Deep Learning Approach for Automatic Hate Speech Detection in the Saudi Twittersphere

Alshalan, Raghad; Al‐Khalifa, Hend S.

doi:10.3390/app10238614

Cited by 90 publications

(68 citation statements)

References 41 publications

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“…The processed tweets were analyzed for hate speech using the pretrained CNN model. The CNN model used in this study is based on our previous work [ 20 ] in which we addressed the problem of hate speech spread in the Arabic Twittersphere. In this study, a CNN was trained on almost 10,000 tweets that were manually labeled as hate tweets or non–hate tweets.…”

Section: Methodsmentioning

confidence: 99%

Detection of Hate Speech in COVID-19–Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach

Alshalan¹,

Al‐Khalifa²,

AlSaeed³

et al. 2020

J Med Internet Res

Self Cite

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Background The massive scale of social media platforms requires an automatic solution for detecting hate speech. These automatic solutions will help reduce the need for manual analysis of content. Most previous literature has cast the hate speech detection problem as a supervised text classification task using classical machine learning methods or, more recently, deep learning methods. However, work investigating this problem in Arabic cyberspace is still limited compared to the published work on English text. Objective This study aims to identify hate speech related to the COVID-19 pandemic posted by Twitter users in the Arab region and to discover the main issues discussed in tweets containing hate speech. Methods We used the ArCOV-19 dataset, an ongoing collection of Arabic tweets related to COVID-19, starting from January 27, 2020. Tweets were analyzed for hate speech using a pretrained convolutional neural network (CNN) model; each tweet was given a score between 0 and 1, with 1 being the most hateful text. We also used nonnegative matrix factorization to discover the main issues and topics discussed in hate tweets. Results The analysis of hate speech in Twitter data in the Arab region identified that the number of non–hate tweets greatly exceeded the number of hate tweets, where the percentage of hate tweets among COVID-19 related tweets was 3.2% (11,743/547,554). The analysis also revealed that the majority of hate tweets (8385/11,743, 71.4%) contained a low level of hate based on the score provided by the CNN. This study identified Saudi Arabia as the Arab country from which the most COVID-19 hate tweets originated during the pandemic. Furthermore, we showed that the largest number of hate tweets appeared during the time period of March 1-30, 2020, representing 51.9% of all hate tweets (6095/11,743). Contrary to what was anticipated, in the Arab region, it was found that the spread of COVID-19–related hate speech on Twitter was weakly related with the dissemination of the pandemic based on the Pearson correlation coefficient (r=0.1982, P=.50). The study also identified the commonly discussed topics in hate tweets during the pandemic. Analysis of the 7 extracted topics showed that 6 of the 7 identified topics were related to hate speech against China and Iran. Arab users also discussed topics related to political conflicts in the Arab region during the COVID-19 pandemic. Conclusions The COVID-19 pandemic poses serious public health challenges to nations worldwide. During the COVID-19 pandemic, frequent use of social media can contribute to the spread of hate speech. Hate speech on the web can have a negative impact on society, and hate speech may have a direct correlation with real hate crimes, which increases the threat associated with being targeted by hate speech and abusive language. This study is the first to analyze hate speech in the context of Arabic COVID-19–related tweets in the Arab region.

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Section: Methodsmentioning

confidence: 99%

Detection of Hate Speech in COVID-19–Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach

Alshalan¹,

Al‐Khalifa²,

AlSaeed³

et al. 2020

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

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“…Abusive messages in social media is a complex phenomenon with a broad range of overlapping modes and goals [17]. Cyberbullying and hate speech are typical examples of abusive languages that researchers have put more interest on in the past few decades due to it negative impacts in our societies.…”

Section: B Related Workmentioning

confidence: 99%

Advances in Machine Learning Algorithms for Hate Speech Detection in Social Media: A Review

Mullah

Zainon

2021

IEEE Access

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The aim of this paper is to review machine learning (ML) algorith ms and techniques for hate speech detection in social media (SM). Hate speech problem is normally model as a text classification task. In this study, we examined the basic baseline components of hate speech classification using ML algorithms. There are five basic baseline componentsdata collection and exploration, feature extraction, dimensionality reduction, classifier selection and training, and model evaluation, were reviewed. There have been improvements in ML algorithms that were employed for hate speech detection over time. New datasets and different performance metrics have been proposed in the literature. To keep the researchers informed regarding these trends in the automatic detection of hate speech, it calls for a comprehensive and an updated state-of-the-art. The contributions of this study are two-fold. First to equip the readers with the necessary information on the critical steps involved in hate speech detection using ML algorithms. Secondly, the weaknesses and strengths of each method is critically evaluated to guide researchers in the algorithm choice dilemma. The different variants of ML techniques were reviewed which include classical ML, ensemble approach and deep learning methods. Researchers and professionals alike will benefit immensely from this study.

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“…It applies the Softmax activation function to classify the high-level features obtained from the images into various categories with labels. The common pre-trained CNNs are VGGNet- 16 Theoretically, the deeper the network, the more information can be acquired and the richer the features. However, experiments have shown that with the deepening of the network, the optimization effect may become worse, and the accuracy of the test data and training data may decrease.…”

Section: Model Architecture 1) Deep Convolutional Neural Networkmentioning

confidence: 99%

“…In the past few years, as a special type of machine learning, deep learning methods have made great progress. Nowadays, the deep learning methods have been applied in various fields [15][16][17][18][19][20][21]. As Dawei et al [20] proposed a pest identification and detection system based on transfer learning, the recognition accuracy can reach 93.84%.…”

Section: Introductionmentioning

confidence: 99%

Maize Diseases Identification Method Based on Multi-Scale Convolutional Global Pooling Neural Network

Zhao

Zhai

et al. 2021

IEEE Access

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Deep learning is thought of as a promising mean to identify maize diseases. However, the drawback of deep learning is the huge sample data and low accuracy. In this paper, we proposed a multiscale convolutional global pooling neural network to improve the accuracy of maize diseases identification. Firstly, on the basis of the AlexNet model, a convolutional layer and new Inception module are added to enhance the ability of AlexNet features extraction. Then, in order to avoid the over-fitting problem caused by too many parameters, we use the global pooling layer to replace the original fully-connected layer. Besides, we also adopt the transfer learning method to solve the over-fitting problem caused by insufficient sample data. The improved model can reduce over-fitting and epochs to enhance the performance of maize diseases recognition. From the considerable experimental results, we can conclude that the proposed model has better performance compared with convolutional neural network models VGGNet-16, DenseNet, ResNet-50 and AlexNet in recognition accuracy.

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A Deep Learning Approach for Automatic Hate Speech Detection in the Saudi Twittersphere

Cited by 90 publications

References 41 publications

Detection of Hate Speech in COVID-19–Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach

Detection of Hate Speech in COVID-19–Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach

Advances in Machine Learning Algorithms for Hate Speech Detection in Social Media: A Review

Maize Diseases Identification Method Based on Multi-Scale Convolutional Global Pooling Neural Network

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