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.

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.

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.

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。

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.

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.

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.

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.

规制仇恨言论的国际法规则与实践

许多国家都存在仇恨言论这一丑恶现象。尽管对于仇恨言论的概念并不存在普遍接受的定义,但国际社会一直高度重视对仇恨言论的规制,提供了若干规则,形成了相应实践。规制仇恨言论的国际法依据可分为四类,即限制表达自由的规则、禁止滥用权利的规则、禁止仇恨言论的规则和规定仇恨言论为国际罪行的规则。普遍性和区域性人权机构以及国际刑事司法机构适用这些规则,在规制仇恨言论方面形成了丰富的案例,且规则适用的重点均落脚于如何平衡对表达自由的保障和对仇恨言论的规制。规制仇恨言论的国内法律规则和实践与国际法律规则和实践相互影响,研究国际相关法律规则和实践对于理解各国相关法律规则和实践、形成规制仇恨言论的国际共识和标准具有重要意义。

青少年压力感知对执行功能的影响:自我厌恶和负性情绪的作用路径

背景青少年执行功能的发展受诸多环境因素的影响。压力感知与执行功能密切相关,但其对青少年执行功能影响的路径尚不明确。目的探究负性情绪和自我厌恶在青少年压力感知与执行功能之间的作用路径,以期为改善青少年的执行功能提供参考。方法于2022年5月1日—30日,选取山东省日照市5所高中和5所初中的7734名青少年进行问卷调查。使用自编调查表收集青少年的一般资料,采用压力感知量表(PSS)、执行功能行为评定量表自评版(BRIEF-SR)、患者健康问卷(PHQ-4)和自我厌恶量表(SHS)分别评定青少年的压力感知水平、执行功能、负性情绪以及自我厌恶水平。采用Spearman相关分析考查各量表评分之间的相关性。采用Bootstrap方法检验自我厌恶和负性情绪在青少年压力感知与执行功能之间的中介效应。结果共回收有效问卷7012份(90.66%)。青少年BRIEF-SR评分与PSS、PHQ-4、SHS评分均呈正相关(r=0.564、0.653、0.597,P均<0.01),PSS评分与PHQ-4和SHS评分均呈正相关(r=0.615、0.531,P均<0.01),PHQ-4评分与SHS评分呈正相关(r=0.566,P<0.01)。青少年压力感知对执行功能影响的总效应为0.574(95%CI:0.555~0.594)。青少年自我厌恶(间接效应值为0.160,95%CI:0.145~0.175)和负性情绪(间接效应值为0.143,95%CI:0.129~0.158)分别是压力感知与执行功能之间的作用路径,且自我厌恶-负性情绪是其链式作用路径(间接效应值为0.065,95%CI:0.058~0.073),分别占总效应的27.87%、24.91%、11.32%。结论青少年压力感知既可以直接影响执行功能,也可以通过负性情绪与自我厌恶的独立路径或链式路径影响执行功能。

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

Purpose-Hate speech is an expression of intense hatred.Twitter has become a popular analytical tool for the prediction and monitoring of abusive behaviors.Hate 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 detection.Design/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 data.The 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 extraction.The 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 neither.Findings-The proposed method showed better results when tested on the collected Twitter datasets compared to other related methods.In 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 detection.Furthermore,Paired Sample t-Test was also conducted to validate the performances of the proposed method with other related methods.Research limitations/implications-Finally,the evaluation results showed that the proposed method outperforms other related methods with mean F1-score of 91.3.Originality/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.

基于谐音干扰词替换的中文仇恨言论检测方法

社交网络中的仇恨言论常含有形式多变的谐音干扰词,使得现有方法难以适应此现象,不能满足即时检测的要求。针对此问题,提出一种基于谐音干扰词替换的中文仇恨言论检测方法,提取原义词替换谐音干扰词,解决原有方法处理相对滞后问题。首先,对文本预处理,通过N-gram提取干扰词候选项,并利用点间互信息和邻接熵进行过滤;然后,计算拼音相似度筛选出谐音干扰词及其对应的候选原义词,通过语法结构和上下文语义相似确定原义词并对相应谐音干扰词进行替换,将替换后的文本作为分类层输入;最后,使用RoBERTa-wmm-ext得到语义特征,并通过Softmax计算仇恨情感倾向以实现检测任务。在数据集上进行实验,结果表明提出的模型有效地提升中文仇恨言论的检测效果。

多任务学习在不良言论与个体特征检测中的应用

多任务学习在自然语言处理领域有广泛应用,但多任务模型往往对任务间的相关性比较敏感.如果任务相关性较低或信息传递不合理,可能会严重影响任务性能.本文提出了一种新的共享-私有结构的多任务学习模型BB-MTL(BERT-BiLSTM multi-task learning model),并借助元学习的思想为其设计了一种特殊的参数优化方式MLL-TM(meta-learning-like train methods).进一步引入一个新的信息融合门SoWLG(Softmax weighted linear gate),用于选择性地融合每项任务的共享特征与私有特征.实验验证所提出的多任务学习方法,考虑到用户在网络上的行为与其个体特征密切相关,文中结合了不良言论检测、人格检测和情绪检测任务进行了一系列实验.实验结果表明,BB-MTL能够有效学习相关任务中的特征信息,在3项任务上的准确率分别达到了81.56%、77.09%和70.82%.

Spatial prediction of sparse events using a discrete global grid system;a case study of hate crimes in the USA

Spatial prediction of any geographic phenomenon can be an intractable problem.Predicting sparse and uncertain spatial events related to many influencing factors necessitates the integration of multiple data sources.We present an innovative approach that combines data in a Discrete Global Grid System(DGGS)and uses machine learning for analysis.A DGGS provides a structured input for multiple types of spatial data,consistent over multiple scales.This data framework facilitates the training of an Artificial Neural Network(ANN)to map and predict a phenomenon.Spatial lag regression models(SLRM)are used to evaluate and rank the outputs of the ANN.In our case study,we predict hate crimes in the USA.Hate crimes get attention from mass media and the scientific community,but data on such events is sparse.We trained the ANN with data ingested in the DGGS based on a 50%sample of hate crimes as identified by the Southern Poverty Law Center(SPLC).Our spatial prediction is up to 78%accurate and verified at the state level against the independent FBI hate crime statistics with a fit of 80%.The derived risk maps are a guide to action for policy makers and law enforcement.

INTERNET INTERMEDIARIES＇ LIABILITY FOR ONLINE ILLEGAL HATE SPEECH

Considering the prevalence of online hate speech and its harm and risks to the targeted people, democratic discourse and public security, it is necessary to combat online hate speech. For this purpose, interact intermediaries play a crucial role as new governors of online speech. However, there is no universal definition of hate speech. Rules concerning this vary in different countries depending on their social, ethical, legal and religious backgrounds. The answer to the question of who can be liable for online hate speech also varies in different countries depending on the social, cultural, history, legal and political backgrounds. The First Amendment, cyberliberalism and the priority of promoting the emerging internet industry lead to the U.S. model, which offers intermediaries wide exemptions from liability for third-party illegal content. Conversely, the Chinese model of cyberpaternalism prefers to control online content on ideological, political and national security grounds through indirect methods, whereas the European Union （EU） and most European countries, including Germany, choose the middle ground to achieve balance between restricting online illegal hate speech and the freedom of speech as well as internet innovation. It is worth noting that there is a heated discussion on whether intermediary liability exemptions are still suitable for the world today, and there is a tendency in the EU to expand intermediary liability by imposing obligation on online platforms to tackle illegal hate speech. However, these reforms are again criticized as they could lead to erosion of the EU legal framework as well as privatization of law enforcement through algorithmic tools. Those critical issues relate to the central questions of whether intermediaries should be liable for user-generated illegal hate speech at all and, if so, how should they fulfill these liabilities？ Based on the analysis of the different basic standpoints of cyberliberalists and cyberpaternalists on the internet regulation as well as the arguments of proponents and opponents of the intermediary liability exemptions, especially the debates over factual impracticality and legal restraints, impact on internet innovation and the chilling effect on freedom of speech in the case that intermediaries bear liabilities for illegal third-party content, the paper argues that the arguments for intermediary liability exemptions are not any more tenable or plausible in the web 3.0 era. The outdated intermediary immunity doctrine needs to be reformed and amended.Furthermore, intermediaries are becoming the new governors of online speech and platforms now have the power to curtail online hate speech. Thus, the attention should turn to the appropriate design of legal responsibilities of intermediaries. The possible suggestions could be the following three points： Imposing liability on intermediaries for illegal hate speech requires national law and international human rights norms as the outer boundary; openness, transparency and accountability as internal constraints; balance of multi-interests and involvement of multi-stakeholders in internet governance regime.