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

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.

富脂微环境通过PPARγ/ABCG2途径促进三阴性乳腺癌细胞的多药耐药

目的:探讨富脂微环境(ARM)对三阴性乳腺癌(TNBC)细胞多药耐药(MDR)的影响及机制。方法:选择MDA-MB-231细胞作为研究模型,应用CCK-8检测过氧化物酶体增殖物激活受体(PPAR)γ拮抗剂GW9662、PPARγ激动剂曲格列酮和ABC转运蛋白G家族成员2(ABCG2)抑制剂KO143在时间和剂量效应下对细胞增殖的影响。应用油红O染色检测人脂肪组织提取液(HATES)对细胞脂滴积累的影响。利用HATES模拟ARM,应用顺铂(DDP)和紫杉醇(PTX)作为化疗药物,通过细胞形态、CCK-8及流式Annexin V-PE/7-AAD双染凋亡检测HATES对MDA-MB-231细胞MDR的影响。荧光素酶报告基因检测HATES、GW9662、曲格列酮对MDA-MB-231细胞ABCG2转录活性的影响。Western印迹检测HATES、GW9662对MDA-MB-231细胞PPARγ和ABCG2蛋白表达的影响。Western印迹、CCK-8及流式Annexin V-PE/7-AAD双染凋亡检测HATES对PPARγ-siRNA瞬时转染MDA-MB-231细胞PPARγ及ABCG2蛋白表达、化疗时细胞增殖和凋亡的影响。CCK-8及流式Annexin V-PE/7-AAD双染凋亡检测HATES、GW9662、KO143对MDA-MB-231化疗时细胞增殖和凋亡的影响。结果:25μmol/L GW9662、25μmol/L曲格列酮及10μmol/L KO143处理细胞2 h均不影响细胞增殖,在此基础上进行后续实验。应用的HATES浓度可促进MDA-MB-231细胞脂滴形成且不影响MDA-MB-231细胞的形态、增殖及凋亡(均P>0.05),但在化疗药物存在时可促进细胞增殖并减少细胞凋亡(均P<0.05)。HATES可提高MDA-MB-231细胞的ABCG2转录活性,而阻断PPARγ可抑制该效应(P<0.001)。HATES促进细胞的MDR,沉默PPARγ和使用GW9662及KO143减弱HATES对MDR的影响(均P<0.001)。结论:HATES通过PPARγ/ABCG2途径促进MDA-MB-231细胞的MDR。

A Review of Machine Learning Techniques in Cyberbullying Detection

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.

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

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.

Improved Attentive Recurrent Network for Applied Linguistics-Based Offensive Speech Detection

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.

Social Media and Hate Speech:A Twitter Example

The internet has brought together people from diverse cultures,backgrounds,and languages,forming a global community.However,this unstoppable growth in online presence and user numbers has introduced several new challenges.The structure of the cyberspace panopticon,the utilization of big data and its manipulation by interest groups,and the emergence of various ethical issues in digital media,such as deceptive content,deepfakes,and echo chambers,have become significant concerns.When combined with the characteristics of digital dissemination and rapid global interaction,these factors have paved the way for ethical problems related to the production,proliferation,and legitimization of hate speech.Moreover,certain images have gained widespread acceptance as though they were real,despite having no factual basis.This recent realization that much of the information and imagery considered to be true is,in fact,a virtual illusion,is a commonly discussed truth.The alarming increase and growing legitimacy of hate speech within the digital realm,made possible by social media,are leading us toward an unavoidable outcome.This study aims to investigate the reality of hate speech in this context.To achieve this goal,the research question is formulated as follows:“Does social media,particularly Twitter,contain content that includes hate speech,incendiary information,and news?”The study’s population is social media,with the sample consisting of hate speech content found on Twitter.Qualitative research methods are intended to be employed in this study.

Ubase数据库系统缓冲管理器的实现

缓冲区技术是减少系统对外存访问的重要方法之一。文章分析了Ubase数据库系统缓冲管理器的基本功能,对常用页面替换算法的性能进行了比较,最终选择Love/Hate算法代替LRU算法实现了Ubase数据库系统的缓冲管理,降低了系统的开销,提高了Ubase数据库系统的性能。

hate crime译名商榷

hate crime是警务英语中使用较多的一个术语,但其译名并不统一。通过追溯该术语的概念和内涵,分析比较既有译名,找出翻译过程中法律文化缺省和翻译方法不当的问题,笔者结合中外法律文化特点,采用仿译的方法,提出新的译名。

Economic research on application of electric heating system based on game theory

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.

An architecture for digital hate content reduction with mobile edge computing

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.

The State of White Supremacy in America:It Is Time to Stand Together

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.

Discourse Recognition and Analysis of Ethnic Hate Speech in Social Media

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.

“Emotional Dwarfism”Is a Failure to Thrive Emotionally-Whence the Childhood Roots of Hate,Psychosis,Violence,and Nucleargeddon-All Curable

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.

An Analysis on Heathcliff's Dual Personality

Wutering Heights is an exceptionally successful novel written by Emily Bronte. The novel is organized by the love between Heathcliff and Catherine. This thesis attempts to analyze the dual personality of Heathcliff. Through the journey of love, revenge, death and restoration of humanity, his character is influenced by both his own characteristics and social environment.

Love and Hate in Frankenstein

Frankenstein,as the first science fiction in the world,mainly talks about the life of a young scientist,Victor Frankenstein,how he created the monster and how the monster destroyed his life.In the novel,love existed in everyone’s heart including the monster.On the other side,hate also existed in the characters in the novel.Love and hate were described in the novel,and at last love was more powerful than hate and overcome hate.

On the Regulation of Racist Speech

The legal regulation of racist speech involves limitation on the freedom of speech, which requires prudent trade-offs between freedom and quality. The protection of the freedom of speech, although of great value to a democratic society, does not include the pro tection of racist speech. The justification for regulating racist speech lies in the impact of racial ly-charged speech, not in the content of the speech itself. The regulation of racist speech should take into consideration thecriteriaofsubjectivemalignancy,necessityandproportionality.Due to their social status and special role in a democratic society, the media and political figures should bear a more stringent duty of care because of the harm and consequences of their racist remarks, which are far greater than those of other groups. During the pandemic, legal regula tion is required regarding remarks of politicians and the media that imply discrimination linked to CoVID-19.

Students Response to Hate Speech

The general problem of this research was how students respond to hate speech.The purpose of the study was to obtain an overview of(1)perceptions;(2)attitudes;and(3)student actions/participation towards hate speech.The research approach used was quantitative and descriptive with survey method.The population of this study was all the administrators of the student executive board in UNTAN,IAIN,and IKIP PGRI Pontianak totaling 162 students.The number of research samples was 115 students determined by Slovin formula.The respondents were choosen randomly.Data collection used a questionnaire.Data analysis used percentage quantitative descriptive analysis techniques.The general conclusion of the study shows that student responses to hate speech are good.Specific conclusions of the study are:(1)student perceptions(knowledge)of hate speech are on average 78.26%know and 21.74%do not know about the utterances of hatred;(2)student attitudes towards hate speech are on average 78.14%students do not agree with hate speech and 21.86%agree;and(3)student actions or participation in hate speech are on average 78.51%students never take acts in hate speech and 21.49%ever.

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

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.

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

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.