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

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

doi:10.11591/ijece.v13i2.pp1979-1988

Cited by 5 publications

(3 citation statements)

References 31 publications

(46 reference statements)

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“…While previous research has explored various methodologies for hate speech detection, there still needs to be more consensus on the most efficient and accurate algorithmic combinations. Individual methods, such as support vector machines (SVM) (e.g., Elzayady et al, 2023) or recurrent neural networks (RNN) (e.g., Mazari & Kheddar, 2023), have been studied in previous research. However, a comprehensive comparative analysis of multiple algorithms is necessary to determine the optimal combination that balances accuracy, simplicity, computational efficiency, and ease of implementation.…”

Section: Research Articlementioning

confidence: 99%

A comparative analysis of machine learning algorithms for hate speech detection in social media

Omran,

Al Tararwah,

Al Qundus

2023

ONLINE J COMMUN MEDIA TECHNOL

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A<b> </b>detecting and mitigating hate speech in social media, particularly on platforms like Twitter, is a crucial task with significant societal impact. This research study presents a comprehensive comparative analysis of machine learning algorithms for hate speech detection, with the primary goal of identifying an optimal algorithmic combination that is simple, easy to implement, efficient, and yields high detection performance. Through meticulous pre-processing and rigorous evaluation, the study explores various algorithms to determine their suitability for hate speech detection. The focus is finding a combination that balances simplicity, ease of implementation, computational efficiency, and strong performance metrics. The findings reveal that the combination of naïve Bayes and decision tree algorithms achieves a high accuracy of 0.887 and an F1-score of 0.885, demonstrating its effectiveness in hate speech detection. This research contributes to identifying a reliable algorithmic combination that meets the criteria of simplicity, ease of implementation, quick processing, and strong performance, providing valuable guidance for researchers and practitioners in hate speech detection in social media. By elucidating the strengths and limitations of various algorithmic combinations, this research enhances the understanding of hate speech detection. It paves the way for developing robust solutions, creating a safer, more inclusive digital environment.

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Section: Research Articlementioning

confidence: 99%

A comparative analysis of machine learning algorithms for hate speech detection in social media

Omran,

Al Tararwah,

Al Qundus

2023

ONLINE J COMMUN MEDIA TECHNOL

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“…Challenges in hate speech detection on social media have been acknowledged, emphasizing the complexity of identifying and addressing hate speech online (Kovács et al, 2021). The offensive and insulting nature of hate speech has been linked to personality traits, suggesting a hybrid approach for hate speech detection in Arabic social media (Elzayady et al, 2023). In summary, hate speech on social media, particularly in the context of Indonesia, presents a multifaceted challenge that requires a combination of technological solutions, legal frameworks, and societal interventions to effectively combat its spread and impact.…”

Section: Introductionmentioning

confidence: 99%

Empathy and Hate Speech in Social Media: The Case of Indonesia

Rahmi

2024

IJSSHR

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Hate speech on social media in Indonesia 10 years ago has become a very interesting topic of discussion. Many of these incidents occur and are perpetrated by teenagers, with implications for violence and their psychological well-being. Hate speech on social media, particularly in the Indonesian context, presents a multifaceted challenge that requires a combination of technological solutions, legal frameworks, and community interventions to effectively combat its spread and impact. By utilizing insights from research on hate speech detection, empathy promotion, and regulatory frameworks, stakeholders can work towards creating a safer and more inclusive online environment in Indonesia. Strengthening digital literacy, as suggested in the references, is essential to empower individuals to identify and respond effectively to hate speech. Legal measures, as emphasized in the references, play an important role in combating hate speech, with the establishment of specialized units such as the Indonesian Virtual Police contributing to monitoring and handling hate speech incidents. Community engagement and educational programs, as discussed in the references, are crucial in promoting dialogue, tolerance, and understanding to foster a culture of mutual respect and empathy.

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“…Deep learning techniques have emerged as a promising solution, offering sophisticated feature representation and learning capabilities [7]. Studies leveraging Convolutional Neural Networks (CNNs) have demonstrated success in text classification and offensive content detection, owing to their ability to capture hierarchical text features [8]. Separately, Long Short-Term Memory (LSTM) networks, a form of recurrent neural networks (RNNs), have proven effective in understanding sequential data, thereby interpreting the context within the text efficiently [9].…”

Section: Introductionmentioning

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

Cited by 5 publications

References 31 publications

A comparative analysis of machine learning algorithms for hate speech detection in social media

A comparative analysis of machine learning algorithms for hate speech detection in social media

Empathy and Hate Speech in Social Media: The Case of Indonesia

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

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