Improving Arabic Hate Speech Identification Using Online Machine Learning and Deep Learning Models

Elzayady, Hossam; Mohamed, Mohamed S.; Badran, Khaled; Salama, Gouda I.

doi:10.1007/978-981-19-1610-6_46

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…We use in this phase TF-IDF feature extraction technique. Today, 83% of text-based recommender systems in digital libraries employ TF-IDF as a term weighting method [25]. The usefulness of a word in the corpus is measured numerically, calculated by (1).…”

Section: Feature Extractionmentioning

confidence: 99%

A hybrid approach based on personality traits for hate speech detection in Arabic social media

Elzayady

Mohamed

Badran

et al. 2023

IJECE

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<p>In recent years, as social media has grown in popularity, people have gained the ability to freely share their views. However, this may lead to users' conflict and hostility, resulting in unattractive online environments. Hate speech relates to using expressions or phrases that are violent, offensive, or insulting to a minority of people. The number of Arab social media users is quickly rising, and this is being followed by an increase in the frequency of cyber hate speech in the area. Therefore, the automated detection of Arabic hate speech has become a major concern for many stakeholders. The intersection of personality learning and hate speech detection is a relatively less studied niche. We suggest a novel approach that is focused on extracting personality trait features and using these features to detect Arabic hate speech. The experimental results show that the proposed approach is superior in terms of the macro-F1 score by achieving 82.3% compared to previous work reported in the literature.</p>

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Section: Feature Extractionmentioning

confidence: 99%

A hybrid approach based on personality traits for hate speech detection in Arabic social media

Elzayady

Mohamed

Badran

et al. 2023

IJECE

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show abstract

“…Representing a form of supervised learning, Decision Trees create a framework that categorizes input data into specific output classes based on their statistical properties, effectively forming a tree of decisions [59]. These trees offer a highly interpretable model and adeptly manage non-linear relationships.…”

Section: ) Decision Treesmentioning

confidence: 99%

Offensive Language Detection on Online Social Networks using Hybrid Deep Learning Architecture

Kazbekova,

Ismagulova,

Kemelbekova

et al. 2023

IJACSA

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In the digital era, online social networks (OSNs) have revolutionized communication, creating spaces for vibrant public discourse. However, these platforms also harbor offensive language that can proliferates hate speech, cyberbullying, and discrimination, significantly undermining the quality of online interactions and posing severe social implications. This research paper introduces a sophisticated approach to offensive language detection on OSNs, employing a novel Hybrid Deep Learning Architecture (HDLA). The urgency of addressing offensive content is juxtaposed with the challenges inherent in accurately identifying nuanced communications, thus necessitating an advanced model that transcends the limitations of traditional natural language processing techniques. The proposed HDLA model synergistically integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks, capitalizing on the strengths of both methodologies. While the CNN component excels in the hierarchical extraction of spatial features within text data, identifying offensive patterns often concealed in the structural nuances, the LSTM network, adept in processing sequential data, captures the contextual dependencies in user posts over time. This duality ensures a comprehensive analysis of complex linguistic constructs, enhancing the detection accuracy for both overt and covert offensive content. Our research meticulously evaluates the HDLA model using extensive, multi-source datasets reflective of diverse OSN environments, establishing benchmarks against prevailing deep learning models. Results indicate a substantial improvement in precision, recall, and F1-score, demonstrating the model's efficacy in identifying offensive language amidst varying degrees of subtlety and complexity. Furthermore, the model maintains high interpretability, providing insights into the intricate mechanisms of offensive content propagation. Our findings underscore the potential of HDLA in fostering healthier online communities by efficiently curating digital content, thereby upholding the integrity of digital communication spaces.

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A comprehensive review on Arabic offensive language and hate speech detection on social media: methods, challenges and solutions

Abdelsamie,

Azab,

Hefny

2024

Soc. Netw. Anal. Min.

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Improving Arabic Hate Speech Identification Using Online Machine Learning and Deep Learning Models

Cited by 3 publications

References 18 publications

A hybrid approach based on personality traits for hate speech detection in Arabic social media

A hybrid approach based on personality traits for hate speech detection in Arabic social media

Offensive Language Detection on Online Social Networks using Hybrid Deep Learning Architecture

A comprehensive review on Arabic offensive language and hate speech detection on social media: methods, challenges and solutions

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