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An Adaptive hate speech detection Approach Using Neutrosophic Neural Networks for Social Media Forensics

Computers, Materials & Continua 2024年第4期79卷 243-262页

作者： Yasmine M.Ibrahim Reem Essameldin Saad M.Darwish Department of Information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandria21526Egypt Faculty of Computers and Information Technology Egyptian E-Learning University(EELU)Giza12611Egypt Faculty of Computers and Data Science Alexandria UniversityAlexandria21554Egypt

Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due tothe complex nature of language used in such platforms. Currently, several methods exist for classifying hatespeech, but they still suffer from ambiguity when differentiating between hateful and offensive content and theyalso 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 employed to exploreand determine the optimal set of weights. The PSO algorithm adjusts the weights to optimize the performanceof the MLP as fine-tuning. Additionally, in this approach, two separate MLP models are employed. One MLPis dedicated to predicting degrees of truth membership, while the other MLP focuses on predicting degrees offalse membership. The difference between these memberships quantifies uncertainty, indicating the degree ofindeterminacy in predictions. The experimental results indicate the superior performance of our model comparedto previous work when evaluated on the Davidson dataset.

关键词： hate speech detection whale optimization neutrosophic sets social media forensics

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Comparing Fine-Tuning, Zero and Few-Shot Strategies with Large Language Models in hate speech detection in English

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2849-2868页

作者： Ronghao Pan JoséAntonio García-Díaz Rafael Valencia-García Departamento de Informática y Sistemas Universidad de MurciaCampus de EspinardoMurcia30100Spain

Large Language Models(LLMs)are increasingly demonstrating their ability to understand natural language and solve complex tasks,especially through text *** 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 *** recent years,the popularity of social networking has provided a medium through which some users can engage in offensive and harmful online *** this study,we investigate the ability of different LLMs,ranging from zero-shot and few-shot learning to *** experiments show that LLMs can identify sexist and hateful online texts using zero-shot and few-shot approaches through information ***,it is found that the encoder-decoder model called Zephyr achieves the best results with the fine-tuning approach,scoring 86.811%on the Explainable detection of Online Sexism(EDOS)test-set and 57.453%on the Multilingual detection of hate speech Against Immigrants and Women in Twitter(hateval)***,it is confirmed that the evaluated models perform well in hate text detection,as they beat the best result in the hateval task *** error analysis shows that contextual learning had difficulty distinguishing between types of hate speech and figurative ***,the fine-tuned approach tends to produce many false positives.

关键词： hate speech detection zero-shot few-shot fine-tuning natural language processing

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Chaotic Elephant Herd Optimization with Machine Learning for Arabic hate speech detection

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Intelligent Automation & Soft Computing 2024年第3期39卷 567-583页

作者： Badriyya B.Al-onazi Jaber S.Alzahrani Najm Alotaibi Hussain Alshahrani Mohamed Ahmed Elfaki Radwa Marzouk Heba Mohsen Abdelwahed Motwakel Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies Riyadh12735Saudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqra11911Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt New CairoNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipAl-Kharj16278Saudi Arabia

In recent years,the usage of social networking sites has considerably increased in the Arab *** has empowered individuals to express their opinions,especially in ***,various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different *** is attributed to business owners’understanding of social media’s importance for business ***,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 *** speech over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil *** this background,the current study develops a Chaotic Elephant Herd Optimization with Machine Learning for hate speech detection(CEHOML-HSD)model in the context of the Arabic *** presented CEHOML-HSD model majorly concentrates on identifying and categorising the Arabic text into hate speech and *** attain this,the CEHOML-HSD model follows different sub-processes as discussed *** the initial stage,the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF ***,the Support Vector Machine(SVM)model is utilized to detect and classify the hate speech texts made in the Arabic ***,the CEHO approach is employed for fine-tuning the parameters involved in *** CEHO approach is developed by combining the chaotic functions with the classical EHO *** design of the CEHO algorithm for parameter tuning shows the novelty of the work.A widespread experimental analysis was executed to validate the enhanced performance of the proposed CEHOML-HSD *** comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches.

关键词： Arabic language machine learning elephant herd optimization TF-IDF vectorizer hate speech detection

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hate speech detection in Twitter using hybrid embeddings and improved cuckoo search-based neural networks

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International Journal of Intelligent Computing and Cybernetics 2020年第4期13卷 485-525页

作者： Femi Emmanuel Ayo Olusegun Folorunso Friday Thomas Ibharalu Idowu Ademola Osinuga Computer Science College of Natural and Applied SciencesMcPherson UniversitySeriki SotayoNigeria Computer Science College of Natural SciencesFederal University of Agriculture AbeokutaAbeokutaNigeria Computer Science Federal University of Agriculture AbeokutaAbeokutaNigeria Mathematics Federal University of Agriculture AbeokutaAbeokutaNigeria

Purpose-hate speech is an expression of intense *** has become a popular analytical tool for the prediction and monitoring of abusive *** speech detection with social media data has witnessed special research attention in recent studies,hence,the need to design a generic metadata architecture and efficient feature extraction technique to enhance hate speech ***/methodology/approach-This study proposes a hybrid embeddings enhanced with a topic inference method and an improved cuckoo search neural network for hate speech detection in Twitter *** proposed method uses a hybrid embeddings technique that includes Term Frequency-Inverse Document Frequency(TF-IDF)for word-level feature extraction and Long Short Term Memory(LSTM)which is a variant of recurrent neural networks architecture for sentence-level feature *** extracted features from the hybrid embeddings then serve as input into the improved cuckoo search neural network for the prediction of a tweet as hate speech,offensive language or ***-The proposed method showed better results when tested on the collected Twitter datasets compared to other related *** order to validate the performances of the proposed method,t-test and post hoc multiple comparisons were used to compare the significance and means of the proposed method with other related methods for hate speech ***,Paired Sample t-Test was also conducted to validate the performances of the proposed method with other related *** limitations/implications-Finally,the evaluation results showed that the proposed method outperforms other related methods with mean F1-score of ***/value-The main novelty of this study is the use of an automatic topic spotting measure based on na€ıve Bayes model to improve features representation.

关键词： Twitter hate speech detection Embeddings Cuckoo search Neural networks

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