With the rapid growth of the Internet and social media, information is widely disseminated in multimodal forms, such as text and images, where discriminatory content can manifest in various ways. Discrimination detect...With the rapid growth of the Internet and social media, information is widely disseminated in multimodal forms, such as text and images, where discriminatory content can manifest in various ways. Discrimination detection techniques for multilingual and multimodal data can identify potential discriminatory behavior and help foster a more equitable and inclusive cyberspace. However, existing methods often struggle in complex contexts and multilingual environments. To address these challenges, this paper proposes an innovative detection method, using image and multilingual text encoders to separately extract features from different modalities. It continuously updates a historical feature memory bank, aggregates the Top-K most similar samples, and utilizes a Gated Recurrent Unit (GRU) to integrate current and historical features, generating enhanced feature representations with stronger semantic expressiveness to improve the model’s ability to capture discriminatory signals. Experimental results demonstrate that the proposed method exhibits superior discriminative power and detection accuracy in multilingual and multimodal contexts, offering a reliable and effective solution for identifying discriminatory content.展开更多
The legal protection of human dignity can be explored from the perspective of regulating“hate speech.”The practices of most countries worldwide demonstrate that human dignity serves as a fundamental value limiting t...The legal protection of human dignity can be explored from the perspective of regulating“hate speech.”The practices of most countries worldwide demonstrate that human dignity serves as a fundamental value limiting the freedom of expression.Legally protected human dignity encompasses three levels of meaning:the dignity of life as an inherent aspect of human existence,the dignity of individuals as members of specific groups,and the personal dignity of individuals as unique beings.These three levels collectively emphasize the principle that human beings are ends in themselves,underscoring that individuals must not be degraded to mere means or subjected to harm.The inherent nature of human dignity necessitates its protection by both the state and societal entities.Traditionally,the safeguarding of human dignity has primarily depended on state intervention.However,with the advent of the digital age,this responsibility has increasingly extended to social entities,imposing changes of enhanced and expanded obligations of respect.Consequently,the key to protecting human dignity lies in adjusting the allocation of responsibilities between the state and society in accordance with the development of the times.Under the guidance of human dignity as a constitutional value,China should focus on establishing a comprehensive protection system by improving legislation,law enforcement,and judicial practices.This includes specifying the obligations of social entities and constructing multi-level regulatory mechanisms to form an effective system of protection by the state and society.展开更多
Hateful meme is a multimodal medium that combines images and texts.The potential hate content of hateful memes has caused serious problems for social media security.The current hateful memes classification task faces ...Hateful meme is a multimodal medium that combines images and texts.The potential hate content of hateful memes has caused serious problems for social media security.The current hateful memes classification task faces significant data scarcity challenges,and direct fine-tuning of large-scale pre-trained models often leads to severe overfitting issues.In addition,it is a challenge to understand the underlying relationship between text and images in the hateful memes.To address these issues,we propose a multimodal hateful memes classification model named LABF,which is based on low-rank adapter layers and bidirectional gated feature fusion.Firstly,low-rank adapter layers are adopted to learn the feature representation of the new dataset.This is achieved by introducing a small number of additional parameters while retaining prior knowledge of the CLIP model,which effectively alleviates the overfitting phenomenon.Secondly,a bidirectional gated feature fusion mechanism is designed to dynamically adjust the interaction weights of text and image features to achieve finer cross-modal fusion.Experimental results show that the method significantly outperforms existing methods on two public datasets,verifying its effectiveness and robustness.展开更多
“civil discourse”amongst multiple individuals with diverse viewpoints is necessary to move toward truth,to maintain democratic buoyancy,and to get the most accurate read on how best to move forward toward our collec...“civil discourse”amongst multiple individuals with diverse viewpoints is necessary to move toward truth,to maintain democratic buoyancy,and to get the most accurate read on how best to move forward toward our collective good,civil discourse is nonetheless under catastrophic threat by contemporary forces that include the sloppy use of the term“hate speech”;the“libelling by labeling”(aka“cancelling”)in the public square of social media;technologically powered disinformation campaigns;and the growth of“safetyism”in academia.In light of these threats,the goal must be to convince educators,particularly philosophical educators,of the need to adopt a whole new focus in education,namely one that puts a spotlight on the fact that the utilization of the freedom of speech to destroy the freedom of speech of others utterly undermines the positive value of freedom of speech.In order to motivate individuals to turn their back on the dopamine rush of shutting someone down,educators must also spend a great deal of time showcasing the merits of“civil discourse”by providing young people with extensive experience in engaging in facilitated“civil discourse”(aka Communities of Philosophical Inquiry)so that its value can be woven into a personal commitment.展开更多
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...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 and few-shot learning to fine-tuning.Our experiments show that LLMs can identify sexist and hateful online texts using zero-shot and few-shot approaches through information retrieval.Furthermore,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)test-set.Finally,it is confirmed that the evaluated models perform well in hate text detection,as they beat the best result in the HatEval task leaderboard.The error analysis shows that contextual learning had difficulty distinguishing between types of hate speech and figurative language.However,the fine-tuned approach tends to produce many false positives.展开更多
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 hate...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.展开更多
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 op...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 over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil societies.In 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 language.The presented CEHOML-HSD model majorly concentrates on identifying and categorising the Arabic text into hate speech and normal.To attain this,the CEHOML-HSD model follows different sub-processes as discussed herewith.At the initial stage,the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF vectorizer.Secondly,the Support Vector Machine(SVM)model is utilized to detect and classify the hate speech texts made in the Arabic language.Lastly,the CEHO approach is employed for fine-tuning the parameters involved in SVM.This CEHO approach is developed by combining the chaotic functions with the classical EHO algorithm.The 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 approach.The comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches.展开更多
The use of clean electricity to replace fossil energy burning for heating is an important emission reduction way to achieve carbon neutrality.Without a good business model,it is very difficult to promote electric heat...The use of clean electricity to replace fossil energy burning for heating is an important emission reduction way to achieve carbon neutrality.Without a good business model,it is very difficult to promote electric heating as a replacement for coal-fired systems in some areas with abundant coal resources.This study proposed a new business model for electric heating with stakeholders that included the government,the power grid company,and heat users.Based on this model,a specific tri-objective optimization model was proposed with the electric heating promotion power,heating electricity price,and government subsidy as variables to characterize the game relationship of the stakeholders,and the components of the stakeholder benefit function were analyzed in detail.A classical multi-objective genetic algorithm was used to solve the model.Finally,an"electrical heating"project for a typical area in China was analyzed,and four promotion cases were examined.The results showed that the power grid company and users had an antagonistic relationship in relation to the electricity price,but the best solution was found for all three stakeholders due to the high financial subsidies provided by the government.展开更多
Automatic identification of cyberbullying is a problem that is gaining traction,especially in the Machine Learning areas.Not only is it complicated,but it has also become a pressing necessity,considering how social me...Automatic identification of cyberbullying is a problem that is gaining traction,especially in the Machine Learning areas.Not only is it complicated,but it has also become a pressing necessity,considering how social media has become an integral part of adolescents’lives and how serious the impacts of cyberbullying and online harassment can be,particularly among teenagers.This paper contains a systematic literature review of modern strategies,machine learning methods,and technical means for detecting cyberbullying and the aggressive command of an individual in the information space of the Internet.We undertake an in-depth review of 13 papers from four scientific databases.The article provides an overview of scientific literature to analyze the problem of cyberbullying detection from the point of view of machine learning and natural language processing.In this review,we consider a cyberbullying detection framework on social media platforms,which includes data collection,data processing,feature selection,feature extraction,and the application ofmachine learning to classify whether texts contain cyberbullying or not.This article seeks to guide future research on this topic toward a more consistent perspective with the phenomenon’s description and depiction,allowing future solutions to be more practical and effective.展开更多
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 ver...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 Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection(IALODL-OHSD)on Arabic Cross-Corpora.The presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social media.In the IALODL-OHSD model,a threestage process is performed,namely pre-processing,word embedding,and classification.Primarily,data pre-processing is performed to transform the Arabic social media text into a useful format.In addition,the word2vec word embedding process is utilized to produce word embeddings.The attentionbased cascaded long short-term memory(ACLSTM)model is utilized for the classification process.Finally,the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier results.To illustrate a brief result analysis of the IALODL-OHSD model,a detailed set of simulations were performed.The extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.展开更多
VIOLENCE IS NOT ONLY WRONG,IT’S DISEASED-it’s always painful and too often fatal-as with Martin Luther King,no less.When emotions run high,doctors have difficulty making progress-calm,dispassionate review of the obv...VIOLENCE IS NOT ONLY WRONG,IT’S DISEASED-it’s always painful and too often fatal-as with Martin Luther King,no less.When emotions run high,doctors have difficulty making progress-calm,dispassionate review of the obvious evidence is vital,if you aim for less pain and fewer deaths.This paper is based on the self-evident precept that violated children unmistakeably predate violent adults.The remedy,highlighted here,is unusual in all psychiatry,in that it is backed by solid,irrefutable,objective,scientific evidence-from brainscans,no less-at least it is,for those willing to look.The paper has 6 parts:1.Introduction;2.Un-memorising Terror;3.Nutritious Emotions;4.Tyrannical Revenge;5.The Way to Cure Nucleargeddon Is Paved With Good Intentions;6.Conclusion.Parenting is a troubled skill,largely because mis-parenting perpetuates itself.As the poet Philip Larkin says of parents-“they fill you with the faults they had,and add some extra just for you”.Larkin moderates his criticism with“they may not mean to,but they do”.Sadly his“solution”-“don’t have any kids yourself”can extinguish the human race as reliably as ever revengeful Emotional Dwarfism will.展开更多
Mobile devices with social media applications are the prevalent user equipment to generate and consume digital hate content.The objective of this paper is to propose a mobile edge computing architecture for regulating...Mobile devices with social media applications are the prevalent user equipment to generate and consume digital hate content.The objective of this paper is to propose a mobile edge computing architecture for regulating and reducing hate content at the user's level.In this regard,the profiling of hate content is obtained from the results of multiple studies by quantitative and qualitative analyses.Profiling resulted in different categories of hate content caused by gender,religion,race,and disability.Based on this information,an architectural framework is developed to regulate and reduce hate content at the user's level in the mobile computing environment.The proposed architecture will be a novel idea to reduce hate content generation and its impact.展开更多
Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-...Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate speech.Amongst the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the Internet.Based on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and profanity.Amongst these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or violence.Machine Learning(ML)techniques have recently been applied to recognize hate speech-related content.The current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social media.The GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate speech.In the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding processes.Then,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and classification.At last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition process.To depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was conducted.The comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.展开更多
An important part of human communication--verbal abuse--shows signs of specifically national features. So far the Russian language of law is sadly lacking a few terms necessary in legal procedures concerning cases of ...An important part of human communication--verbal abuse--shows signs of specifically national features. So far the Russian language of law is sadly lacking a few terms necessary in legal procedures concerning cases of human rights violation. The ＂Westernization＂ of Russian collectivist mentality makes the solution of the problem still more urgent.展开更多
The United States has numerous social-cultural issues affecting its population.One of these problems is white supremacy.This concept refers to a racist perception that white persons are naturally superior to individua...The United States has numerous social-cultural issues affecting its population.One of these problems is white supremacy.This concept refers to a racist perception that white persons are naturally superior to individuals of other races and should thus dominate them.While white supremacy in previous centuries galvanized a lot of support among white people,the concept is now regarded vicious by white,African American,Latino as well as other races.The modern American society is structured under cultural sensitiveness,racial equality,and religious tolerance.Unlike the years of slavery or reconstruction,all citizens in the United States are afforded equal rights irrespective of their cultural identities.Nevertheless,white supremacist notions persist in a nation that prides itself for its democratic model,cultural diversity,religious tolerance and socioeconomic transcendence.The state of white supremacy is that America can be manifested by violence,imperialistic tendencies,discrimination in the society,unfair immigration policies,poor political leadership,and the deplorable role of the media.With the growing number of atrocities being committed around the United States by both members of identified and non-identified white supremacist group,it is time to address this issue together.This paper,is a constructive discussion venting the many ills of racial discrimination in every aspect that affects minorities at all spheres of the society and the deplorable state of white supremacy in America.展开更多
Hate crimes are a culture phenomenon which is perceived by most as an occurrence that should be uprooted from the society. Yet, to date, we have been unable to do so. Hate crimes are the subject of research and commen...Hate crimes are a culture phenomenon which is perceived by most as an occurrence that should be uprooted from the society. Yet, to date, we have been unable to do so. Hate crimes are the subject of research and comments by experts in various fields. In this regard, most scholars agree that a hate based crime is distinguished from a ＂regular＂ criminal offence by the motive--the attack is aimed at a victim who is part of a differentiated minority group. However, when reading the relevant documents in the area, it seems that the differences between the experts start at the most basic point--what constitutes hate crimes？ This article analyses the concept of ＂hate crimes＂ via an interdisciplinary approach aimed at flashing out the fundamental gaps in the research. We have found that the problems include, inter alia, discrepancies in the definition of hate crimes, methodological difficulties regarding validity and legitimacy （mainly due to the absence of information based on the attacker＇s point of view） and the lack of agreement on the appropriate legal methods required to deal with the ramifications of hate crimes. While part I of this paper revolves around the theoretical aspects of the questions put forth at the centre of this article, part II looks at the same questions from a legal viewpoint. The correlation between the two chapters shows the impact the methodological difficulties have on enforcement endeavors. This relation is further advanced through the examination of test cases from different countries, among them--lsrael. Finally, the article concludes by suggesting a few thoughts on the way to overcome the theoretical problems and making the enforcement efforts more efficient.展开更多
Based on the proposal of freedom of speech,hate speech has become more and more widespread,especially in the past decade.Generally,the constituent elements of hate speech are mainly manifested in four aspects(Jiang,20...Based on the proposal of freedom of speech,hate speech has become more and more widespread,especially in the past decade.Generally,the constituent elements of hate speech are mainly manifested in four aspects(Jiang,2015):the way of expression,the object,the intention of expression,and the harmful consequences.Through these four aspects,hate speech can give a heavy blow to the stability and security of the whole society with the help of social media.Hence,this paper puts forward an analysis method of the recognition and resistance to hate speech from different conditions.展开更多
基金funded by the Open Foundation of Key Laboratory of Cyberspace Security,Ministry of Education[KLCS20240210].
文摘With the rapid growth of the Internet and social media, information is widely disseminated in multimodal forms, such as text and images, where discriminatory content can manifest in various ways. Discrimination detection techniques for multilingual and multimodal data can identify potential discriminatory behavior and help foster a more equitable and inclusive cyberspace. However, existing methods often struggle in complex contexts and multilingual environments. To address these challenges, this paper proposes an innovative detection method, using image and multilingual text encoders to separately extract features from different modalities. It continuously updates a historical feature memory bank, aggregates the Top-K most similar samples, and utilizes a Gated Recurrent Unit (GRU) to integrate current and historical features, generating enhanced feature representations with stronger semantic expressiveness to improve the model’s ability to capture discriminatory signals. Experimental results demonstrate that the proposed method exhibits superior discriminative power and detection accuracy in multilingual and multimodal contexts, offering a reliable and effective solution for identifying discriminatory content.
文摘The legal protection of human dignity can be explored from the perspective of regulating“hate speech.”The practices of most countries worldwide demonstrate that human dignity serves as a fundamental value limiting the freedom of expression.Legally protected human dignity encompasses three levels of meaning:the dignity of life as an inherent aspect of human existence,the dignity of individuals as members of specific groups,and the personal dignity of individuals as unique beings.These three levels collectively emphasize the principle that human beings are ends in themselves,underscoring that individuals must not be degraded to mere means or subjected to harm.The inherent nature of human dignity necessitates its protection by both the state and societal entities.Traditionally,the safeguarding of human dignity has primarily depended on state intervention.However,with the advent of the digital age,this responsibility has increasingly extended to social entities,imposing changes of enhanced and expanded obligations of respect.Consequently,the key to protecting human dignity lies in adjusting the allocation of responsibilities between the state and society in accordance with the development of the times.Under the guidance of human dignity as a constitutional value,China should focus on establishing a comprehensive protection system by improving legislation,law enforcement,and judicial practices.This includes specifying the obligations of social entities and constructing multi-level regulatory mechanisms to form an effective system of protection by the state and society.
基金supported by the Funding for Research on the Evolution of Cyberbullying Incidents and Intervention Strategies(24BSH033)Discipline Innovation and Talent Introduction Bases in Higher Education Institutions(B20087).
文摘Hateful meme is a multimodal medium that combines images and texts.The potential hate content of hateful memes has caused serious problems for social media security.The current hateful memes classification task faces significant data scarcity challenges,and direct fine-tuning of large-scale pre-trained models often leads to severe overfitting issues.In addition,it is a challenge to understand the underlying relationship between text and images in the hateful memes.To address these issues,we propose a multimodal hateful memes classification model named LABF,which is based on low-rank adapter layers and bidirectional gated feature fusion.Firstly,low-rank adapter layers are adopted to learn the feature representation of the new dataset.This is achieved by introducing a small number of additional parameters while retaining prior knowledge of the CLIP model,which effectively alleviates the overfitting phenomenon.Secondly,a bidirectional gated feature fusion mechanism is designed to dynamically adjust the interaction weights of text and image features to achieve finer cross-modal fusion.Experimental results show that the method significantly outperforms existing methods on two public datasets,verifying its effectiveness and robustness.
文摘“civil discourse”amongst multiple individuals with diverse viewpoints is necessary to move toward truth,to maintain democratic buoyancy,and to get the most accurate read on how best to move forward toward our collective good,civil discourse is nonetheless under catastrophic threat by contemporary forces that include the sloppy use of the term“hate speech”;the“libelling by labeling”(aka“cancelling”)in the public square of social media;technologically powered disinformation campaigns;and the growth of“safetyism”in academia.In light of these threats,the goal must be to convince educators,particularly philosophical educators,of the need to adopt a whole new focus in education,namely one that puts a spotlight on the fact that the utilization of the freedom of speech to destroy the freedom of speech of others utterly undermines the positive value of freedom of speech.In order to motivate individuals to turn their back on the dopamine rush of shutting someone down,educators must also spend a great deal of time showcasing the merits of“civil discourse”by providing young people with extensive experience in engaging in facilitated“civil discourse”(aka Communities of Philosophical Inquiry)so that its value can be woven into a personal commitment.
基金This work is part of the research projects LaTe4PoliticES(PID2022-138099OBI00)funded by MICIU/AEI/10.13039/501100011033the European Regional Development Fund(ERDF)-A Way of Making Europe and LT-SWM(TED2021-131167B-I00)funded by MICIU/AEI/10.13039/501100011033the European Union NextGenerationEU/PRTR.Mr.Ronghao Pan is supported by the Programa Investigo grant,funded by the Region of Murcia,the Spanish Ministry of Labour and Social Economy and the European Union-NextGenerationEU under the“Plan de Recuperación,Transformación y Resiliencia(PRTR).”。
文摘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 and few-shot learning to fine-tuning.Our experiments show that LLMs can identify sexist and hateful online texts using zero-shot and few-shot approaches through information retrieval.Furthermore,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)test-set.Finally,it is confirmed that the evaluated models perform well in hate text detection,as they beat the best result in the HatEval task leaderboard.The error analysis shows that contextual learning had difficulty distinguishing between types of hate speech and figurative language.However,the fine-tuned approach tends to produce many false positives.
文摘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.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R263)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.This study is supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2024/R/1445).
文摘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 over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil societies.In 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 language.The presented CEHOML-HSD model majorly concentrates on identifying and categorising the Arabic text into hate speech and normal.To attain this,the CEHOML-HSD model follows different sub-processes as discussed herewith.At the initial stage,the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF vectorizer.Secondly,the Support Vector Machine(SVM)model is utilized to detect and classify the hate speech texts made in the Arabic language.Lastly,the CEHO approach is employed for fine-tuning the parameters involved in SVM.This CEHO approach is developed by combining the chaotic functions with the classical EHO algorithm.The 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 approach.The comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches.
基金supported by Central Universities Basic Scientific Research Business Project of Ministry of Education(Talent Special Project,DUT20RC(5)021)State Grid Science and Technology Project of China(SGXJCJ00YJJS1800384)。
文摘The use of clean electricity to replace fossil energy burning for heating is an important emission reduction way to achieve carbon neutrality.Without a good business model,it is very difficult to promote electric heating as a replacement for coal-fired systems in some areas with abundant coal resources.This study proposed a new business model for electric heating with stakeholders that included the government,the power grid company,and heat users.Based on this model,a specific tri-objective optimization model was proposed with the electric heating promotion power,heating electricity price,and government subsidy as variables to characterize the game relationship of the stakeholders,and the components of the stakeholder benefit function were analyzed in detail.A classical multi-objective genetic algorithm was used to solve the model.Finally,an"electrical heating"project for a typical area in China was analyzed,and four promotion cases were examined.The results showed that the power grid company and users had an antagonistic relationship in relation to the electricity price,but the best solution was found for all three stakeholders due to the high financial subsidies provided by the government.
文摘Automatic identification of cyberbullying is a problem that is gaining traction,especially in the Machine Learning areas.Not only is it complicated,but it has also become a pressing necessity,considering how social media has become an integral part of adolescents’lives and how serious the impacts of cyberbullying and online harassment can be,particularly among teenagers.This paper contains a systematic literature review of modern strategies,machine learning methods,and technical means for detecting cyberbullying and the aggressive command of an individual in the information space of the Internet.We undertake an in-depth review of 13 papers from four scientific databases.The article provides an overview of scientific literature to analyze the problem of cyberbullying detection from the point of view of machine learning and natural language processing.In this review,we consider a cyberbullying detection framework on social media platforms,which includes data collection,data processing,feature selection,feature extraction,and the application ofmachine learning to classify whether texts contain cyberbullying or not.This article seeks to guide future research on this topic toward a more consistent perspective with the phenomenon’s description and depiction,allowing future solutions to be more practical and effective.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R263)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR43.
文摘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 Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection(IALODL-OHSD)on Arabic Cross-Corpora.The presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social media.In the IALODL-OHSD model,a threestage process is performed,namely pre-processing,word embedding,and classification.Primarily,data pre-processing is performed to transform the Arabic social media text into a useful format.In addition,the word2vec word embedding process is utilized to produce word embeddings.The attentionbased cascaded long short-term memory(ACLSTM)model is utilized for the classification process.Finally,the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier results.To illustrate a brief result analysis of the IALODL-OHSD model,a detailed set of simulations were performed.The extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.
文摘VIOLENCE IS NOT ONLY WRONG,IT’S DISEASED-it’s always painful and too often fatal-as with Martin Luther King,no less.When emotions run high,doctors have difficulty making progress-calm,dispassionate review of the obvious evidence is vital,if you aim for less pain and fewer deaths.This paper is based on the self-evident precept that violated children unmistakeably predate violent adults.The remedy,highlighted here,is unusual in all psychiatry,in that it is backed by solid,irrefutable,objective,scientific evidence-from brainscans,no less-at least it is,for those willing to look.The paper has 6 parts:1.Introduction;2.Un-memorising Terror;3.Nutritious Emotions;4.Tyrannical Revenge;5.The Way to Cure Nucleargeddon Is Paved With Good Intentions;6.Conclusion.Parenting is a troubled skill,largely because mis-parenting perpetuates itself.As the poet Philip Larkin says of parents-“they fill you with the faults they had,and add some extra just for you”.Larkin moderates his criticism with“they may not mean to,but they do”.Sadly his“solution”-“don’t have any kids yourself”can extinguish the human race as reliably as ever revengeful Emotional Dwarfism will.
文摘Mobile devices with social media applications are the prevalent user equipment to generate and consume digital hate content.The objective of this paper is to propose a mobile edge computing architecture for regulating and reducing hate content at the user's level.In this regard,the profiling of hate content is obtained from the results of multiple studies by quantitative and qualitative analyses.Profiling resulted in different categories of hate content caused by gender,religion,race,and disability.Based on this information,an architectural framework is developed to regulate and reduce hate content at the user's level in the mobile computing environment.The proposed architecture will be a novel idea to reduce hate content generation and its impact.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia+1 种基金Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22UQU4331004DSR031)supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444).
文摘Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate speech.Amongst the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the Internet.Based on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and profanity.Amongst these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or violence.Machine Learning(ML)techniques have recently been applied to recognize hate speech-related content.The current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social media.The GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate speech.In the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding processes.Then,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and classification.At last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition process.To depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was conducted.The comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.
文摘An important part of human communication--verbal abuse--shows signs of specifically national features. So far the Russian language of law is sadly lacking a few terms necessary in legal procedures concerning cases of human rights violation. The ＂Westernization＂ of Russian collectivist mentality makes the solution of the problem still more urgent.
文摘The United States has numerous social-cultural issues affecting its population.One of these problems is white supremacy.This concept refers to a racist perception that white persons are naturally superior to individuals of other races and should thus dominate them.While white supremacy in previous centuries galvanized a lot of support among white people,the concept is now regarded vicious by white,African American,Latino as well as other races.The modern American society is structured under cultural sensitiveness,racial equality,and religious tolerance.Unlike the years of slavery or reconstruction,all citizens in the United States are afforded equal rights irrespective of their cultural identities.Nevertheless,white supremacist notions persist in a nation that prides itself for its democratic model,cultural diversity,religious tolerance and socioeconomic transcendence.The state of white supremacy is that America can be manifested by violence,imperialistic tendencies,discrimination in the society,unfair immigration policies,poor political leadership,and the deplorable role of the media.With the growing number of atrocities being committed around the United States by both members of identified and non-identified white supremacist group,it is time to address this issue together.This paper,is a constructive discussion venting the many ills of racial discrimination in every aspect that affects minorities at all spheres of the society and the deplorable state of white supremacy in America.
文摘Hate crimes are a culture phenomenon which is perceived by most as an occurrence that should be uprooted from the society. Yet, to date, we have been unable to do so. Hate crimes are the subject of research and comments by experts in various fields. In this regard, most scholars agree that a hate based crime is distinguished from a ＂regular＂ criminal offence by the motive--the attack is aimed at a victim who is part of a differentiated minority group. However, when reading the relevant documents in the area, it seems that the differences between the experts start at the most basic point--what constitutes hate crimes？ This article analyses the concept of ＂hate crimes＂ via an interdisciplinary approach aimed at flashing out the fundamental gaps in the research. We have found that the problems include, inter alia, discrepancies in the definition of hate crimes, methodological difficulties regarding validity and legitimacy （mainly due to the absence of information based on the attacker＇s point of view） and the lack of agreement on the appropriate legal methods required to deal with the ramifications of hate crimes. While part I of this paper revolves around the theoretical aspects of the questions put forth at the centre of this article, part II looks at the same questions from a legal viewpoint. The correlation between the two chapters shows the impact the methodological difficulties have on enforcement endeavors. This relation is further advanced through the examination of test cases from different countries, among them--lsrael. Finally, the article concludes by suggesting a few thoughts on the way to overcome the theoretical problems and making the enforcement efforts more efficient.
文摘Based on the proposal of freedom of speech,hate speech has become more and more widespread,especially in the past decade.Generally,the constituent elements of hate speech are mainly manifested in four aspects(Jiang,2015):the way of expression,the object,the intention of expression,and the harmful consequences.Through these four aspects,hate speech can give a heavy blow to the stability and security of the whole society with the help of social media.Hence,this paper puts forward an analysis method of the recognition and resistance to hate speech from different conditions.
