A systematic review of Hate Speech automatic detection using Natural Language Processing

Saroar, Jahan, Md; Oussalah, Mourad

doi:10.48550/arxiv.2106.00742

Cited by 12 publications

(19 citation statements)

References 68 publications

(103 reference statements)

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“…In this line of work, previous studies have used deep learning models, such as word embeddings [10,92] and several neural architectures [93,94]. Recent advances in NLP learning methods have used the transformer architecture [95], which has been applied to hate speech and similar content [96]. These mentioned approaches can be easily added to the intelligent engine due to the presented architecture and its corresponding semantic modeling.…”

Section: Discussionmentioning

confidence: 99%

Making Sense of Language Signals for Monitoring Radicalization

et al. 2022

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Understanding radicalization pathways, drivers, and factors is essential for the effective design of prevention and counter-radicalization programs. Traditionally, the primary methods used by social scientists to detect these drivers and factors include literature reviews, qualitative interviews, focus groups, and quantitative methods based on surveys. This article proposes to complement social science approaches with computational methods to detect these factors automatically by analyzing the language signals expressed in social networks. To this end, the article categorizes radicalization drivers and factors following the micro, meso, and macro levels used in the social sciences. It identifies the corresponding language signals and available language resources. Then, a computational system is developed to monitor these language signals. In addition, this article proposes semantic technologies since they offer unique exploration, query, and discovery capabilities. The system was evaluated based on a set of competency questions that show the benefits of this approach.

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Section: Discussionmentioning

confidence: 99%

Making Sense of Language Signals for Monitoring Radicalization

et al. 2022

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“…In addition to the discussed papers and to understand the cutting edge in this area to track the latest approaches, resources, statistics, techniques, and methods, we will also consider recent survey papers. Specifically, narrative review papers ( Al-Hassan & Al-Dossari, 2019 ; Mishra, Yannakoudakis & Shutova, 2019 ; Schmidt & Wiegand, 2017 ), systematic review papers ( Fortuna & Nunes, 2018 ; Poletto et al, 2021 ) and more recent systematic review articles ( Jahan & Oussalah, 2021 ). In this section, we will discuss the proposed taxonomy, which covers five different aspects and tasks:…”

Section: Survey Methodologymentioning

confidence: 99%

“…On the other hand, there are some studies that investigate the available dataset for this area. For example, Jahan & Oussalah (2021) investigated 69 hate speech datasets and found that the existing efforts provided a variety of challenges in terms of dataset preparation. Generally, researchers begin by gathering and annotating new comments from social media or by referring to older datasets.…”

Section: Survey Methodologymentioning

confidence: 99%

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A review on abusive content automatic detection: approaches, challenges and opportunities

Alrashidi

Jamal²,

Khan

et al. 2022

PeerJ Computer Science

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The increasing use of social media has led to the emergence of a new challenge in the form of abusive content. There are many forms of abusive content such as hate speech, cyberbullying, offensive language, and abusive language. This article will present a review of abusive content automatic detection approaches. Specifically, we are focusing on the recent contributions that were using natural language processing (NLP) technologies to detect the abusive content in social media. Accordingly, we adopt PRISMA flow chart for selecting the related papers and filtering process with some of inclusion and exclusion criteria. Therefore, we select 25 papers for meta-analysis and another 87 papers were cited in this article during the span of 2017–2021. In addition, we searched for the available datasets that are related to abusive content categories in three repositories and we highlighted some points related to the obtained results. Moreover, after a comprehensive review this article propose a new taxonomy of abusive content automatic detection by covering five different aspects and tasks. The proposed taxonomy gives insights and a holistic view of the automatic detection process. Finally, this article discusses and highlights the challenges and opportunities for the abusive content automatic detection problem.

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“…with a growth in the number of people using social media sites such as Twitter to express themselves [ 3 ]. The lack of tools and benchmark datasets are the main challenges in this field [ 4 ]. The goal here is to construct an accurate method for detecting misogyny and sarcasm from Arabic text.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Approach for Misogyny and Sarcasm Detection from Arabic Texts

Muaad

Hanumanthappa

Benifa

et al. 2022

Computational Intelligence and Neuroscience

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Social media networking is a prominent topic in real life, particularly at the current moment. The impact of comments has been investigated in several studies. Twitter, Facebook, and Instagram are just a few of the social media networks that are used to broadcast different news worldwide. In this paper, a comprehensive AI-based study is presented to automatically detect the Arabic text misogyny and sarcasm in binary and multiclass scenarios. The key of the proposed AI approach is to distinguish various topics of misogyny and sarcasm from Arabic tweets in social media networks. A comprehensive study is achieved for detecting both misogyny and sarcasm via adopting seven state-of-the-art NLP classifiers: ARABERT, PAC, LRC, RFC, LSVC, DTC, and KNNC. To fine tune, validate, and evaluate all of these techniques, two Arabic tweets datasets (i.e., misogyny and Abu Farah datasets) are used. For the experimental study, two scenarios are proposed for each case study (misogyny or sarcasm): binary and multiclass problems. For misogyny detection, the best accuracy is achieved using the AraBERT classifier with 91.0% for binary classification scenario and 89.0% for the multiclass scenario. For sarcasm detection, the best accuracy is achieved using the AraBERT as well with 88% for binary classification scenario and 77.0% for the multiclass scenario. The proposed method appears to be effective in detecting misogyny and sarcasm in social media platforms with suggesting AraBERT as a superior state-of-the-art deep learning classifier.

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A systematic review of Hate Speech automatic detection using Natural Language Processing

Cited by 12 publications

References 68 publications

Making Sense of Language Signals for Monitoring Radicalization

Making Sense of Language Signals for Monitoring Radicalization

A review on abusive content automatic detection: approaches, challenges and opportunities

Artificial Intelligence-Based Approach for Misogyny and Sarcasm Detection from Arabic Texts

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