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

2020 ◽  
Author(s):  
Raghad Alshalan ◽  
Hend Al-Khalifa ◽  
Duaa Alsaeed ◽  
Heyam Al-Baity ◽  
Shahad Alshalan
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Hate Speech ◽  
Negative Impact ◽  
Pearson Correlation ◽  
Nonnegative Matrix ◽  
Arab Country ◽  
Learning Methods ◽  
Arab Region ◽  
Speech Detection

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, <i>P</i>=.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.

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

10.2196/22609 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e22609
Author(s):  
Raghad Alshalan ◽  
Hend Al-Khalifa ◽  
Duaa Alsaeed ◽  
Heyam Al-Baity ◽  
Shahad Alshalan
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Hate Speech ◽  
Negative Impact ◽  
Pearson Correlation ◽  
Nonnegative Matrix ◽  
Arab Country ◽  
Learning Methods ◽  
Arab Region ◽  
Speech Detection

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.

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.

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.

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

scholarly journals Review Paper on Hate Speech Detection

2021 ◽  
Vol 06 (12) ◽  
Author(s):  
Dr Ramakrishna Hegde ◽  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Review Paper ◽  
Hate Speech ◽  
Anxiety And Depression ◽  
Speech Detection ◽  
Speech Content ◽  
The Way

This is a review paper on the topic “Hate Speech Detection”. One of the main disadvantages of social media is the way it is used to spread hate. This hate can affect an individual or a group in different ways like, degrading their mental health leading to anxiety and depression. This can lead to suicides or homicide. So it is very important to control how a platform can be used in spreading a particular message. To do this we have to identify the hate speech content automatically, this can be done with the help of techniques in machine learning and deep learning. We have reviewed few papers that deal with the different methodologies of detecting hate speech in a given text

scholarly journals A Web Interface for Analyzing Hate Speech

2021 ◽  
Vol 13 (3) ◽  
pp. 80
Author(s):  
Lazaros Vrysis ◽  
Nikolaos Vryzas ◽  
Rigas Kotsakis ◽  
Theodora Saridou ◽  
Maria Matsiola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Graphical User Interface ◽  
Hate Speech ◽  
Web Interface ◽  
Learning Models ◽  
Speech Detection ◽  
Media Services ◽  
The Web ◽  
Machine Learning Models

Social media services make it possible for an increasing number of people to express their opinion publicly. In this context, large amounts of hateful comments are published daily. The PHARM project aims at monitoring and modeling hate speech against refugees and migrants in Greece, Italy, and Spain. In this direction, a web interface for the creation and the query of a multi-source database containing hate speech-related content is implemented and evaluated. The selected sources include Twitter, YouTube, and Facebook comments and posts, as well as comments and articles from a selected list of websites. The interface allows users to search in the existing database, scrape social media using keywords, annotate records through a dedicated platform and contribute new content to the database. Furthermore, the functionality for hate speech detection and sentiment analysis of texts is provided, making use of novel methods and machine learning models. The interface can be accessed online with a graphical user interface compatible with modern internet browsers. For the evaluation of the interface, a multifactor questionnaire was formulated, targeting to record the users’ opinions about the web interface and the corresponding functionality.

scholarly journals Online Multilingual Hate Speech Detection: Experimenting with Hindi and English Social Media

2020 ◽  
Author(s):  
Neeraj Vashistha ◽  
Arkaitz Zubiaga
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Model Performance ◽  
Academic Community ◽  
Human Interaction ◽  
Superior Performance ◽  
Competitive Performance ◽  
Speech Detection ◽  
Improve Model ◽  
Use Of The Internet

The exponential increase in the use of the Internet and social media over the last two decades has changed human interaction. This has led to many positive outcomes, but at the same time it has brought risks and harms. While the volume of harmful content online, such as hate speech, is not manageable by humans, interest in the academic community to investigate automated means for hate speech detection has increased. In this study, we analyse six publicly available datasets by combining them into a single homogeneous dataset and classify them into three classes, abusive, hateful or neither. We create a baseline model and we improve model performance scores using various optimisation techniques. After attaining a competitive performance score, we create a tool which identifies and scores a page with effective metric in near-real time and uses the same as feedback to re-train our model. We prove the competitive performance of our multilingual model on two langauges, English and Hindi, leading to comparable or superior performance to most monolingual models.

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