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  • Open Access

    ARTICLE

    Chaotic Elephant Herd Optimization with Machine Learning for Arabic Hate Speech Detection

    Badriyya B. Al-onazi1, Jaber S. Alzahrani2, Najm Alotaibi3, Hussain Alshahrani4, Mohamed Ahmed Elfaki4, Radwa Marzouk5, Heba Mohsen6, Abdelwahed Motwakel7,*

    Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 567-583, 2024, DOI:10.32604/iasc.2023.033835

    Abstract In recent years, the usage of social networking sites has considerably increased in the Arab world. It has empowered individuals to express their opinions, especially in politics. Furthermore, various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different scales. This is attributed to business owners’ understanding of social media’s importance for business development. However, the Arabic morphology is too complicated to understand due to the availability of nearly 10,000 roots and more than 900 patterns that act as the basis for verbs and nouns. Hate speech… More >

  • Open Access

    ARTICLE

    Comparing Fine-Tuning, Zero and Few-Shot Strategies with Large Language Models in Hate Speech Detection in English

    Ronghao Pan, José Antonio García-Díaz*, Rafael Valencia-García

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2849-2868, 2024, DOI:10.32604/cmes.2024.049631

    Abstract Large Language Models (LLMs) are increasingly demonstrating their ability to understand natural language and solve complex tasks, especially through text generation. One of the relevant capabilities is contextual learning, which involves the ability to receive instructions in natural language or task demonstrations to generate expected outputs for test instances without the need for additional training or gradient updates. In recent years, the popularity of social networking has provided a medium through which some users can engage in offensive and harmful online behavior. In this study, we investigate the ability of different LLMs, ranging from zero-shot… More >

  • Open Access

    ARTICLE

    An Adaptive Hate Speech Detection Approach Using Neutrosophic Neural Networks for Social Media Forensics

    Yasmine M. Ibrahim1,2, Reem Essameldin3, Saad M. Darwish1,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 243-262, 2024, DOI:10.32604/cmc.2024.047840

    Abstract Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due to the complex nature of language used in such platforms. Currently, several methods exist for classifying hate speech, but they still suffer from ambiguity when differentiating between hateful and offensive content and they also lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs) for neutrosophic sets classification. During the training process of the MLP, the WOA is More >

  • Open Access

    ARTICLE

    Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection

    Abdelwahed Motwakel1,*, Badriyya B. Al-onazi2, Jaber S. Alzahrani3, Sana Alazwari4, Mahmoud Othman5, Abu Sarwar Zamani1, Ishfaq Yaseen1, Amgad Atta Abdelmageed1

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3321-3338, 2023, DOI:10.32604/csse.2023.033901

    Abstract Arabic is the world’s first language, categorized by its rich and complicated grammatical formats. Furthermore, the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns. The Arabic language consists of distinct variations utilized in a community and particular situations. Social media sites are a medium for expressing opinions and social phenomena like racism, hatred, offensive language, and all kinds of verbal violence. Such conduct does not impact particular nations, communities, or groups only, extending beyond such areas into people’s everyday lives. This study introduces an… More >

  • Open Access

    ARTICLE

    Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

    Daniyar Sultan1,2, Aigerim Toktarova3,*, Ainur Zhumadillayeva4, Sapargali Aldeshov5,6, Shynar Mussiraliyeva1, Gulbakhram Beissenova6,7, Abay Tursynbayev8, Gulmira Baenova4, Aigul Imanbayeva6

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2115-2131, 2023, DOI:10.32604/cmc.2023.032993

    Abstract Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology. The latest advancements in communication technology have significantly surpassed the conventional constraints for communication with regards to time and location. These new platforms have ushered in a new age of user-generated content, online chats, social network and comprehensive data on individual behavior. However, the abuse of communication software such as social media websites, online communities, and chats has resulted in a new kind of online hostility and aggressive actions. Due to widespread use of the social networking platforms… More >

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