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.

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.

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.

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.

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.

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.

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。

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%.

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.

论反仇恨言论视阈下网络暴力的法律治理

对网络暴力进行法律治理,是新时代网络强国建设和网络法治工作的题中之义,而仇恨言论是网络暴力的主要表现形式之一,因此,反仇恨言论是溯源开展全面和深入的网络暴力法律治理的重要手段。我国一向重视网络暴力的法律治理且已取得一定成绩,然而,由于缺乏反仇恨言论视阈的法律治理意识,反网络暴力相关立法的体系性不强,很多网民“网络戾气”非常严重却不自知。加之我国并无反仇恨言论方面的立法,在涉仇恨言论网络暴力案件的处理上存在着取证难、网络平台应对难、受害者维权难等问题。未来有必要加强反仇恨言论视阈下的网络暴力法律治理,具体可采取“三步走”立法策略,在尚无相关立法时执法机关和司法机关能动履职,充分、准确适用现行法律法规,特别是“两高一部”《关于依法惩治网络暴力违法犯罪的指导意见》的规定,网络平台则可构建“政府—平台”双层监管格局。同时,在治理过程中应当注意依法保护公民的言论自由权利,通过社会共治推动形成清朗的网络空间,促进网络空间法律治理的现代化进程。

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.

战争罪中媒体仇视宣传的责任——纽伦堡后续审判和国际军事法庭三起相关案例之比较

世界反法西斯战争结束后同盟国组织的纽伦堡国际军事法庭审判了两起利用媒体仇视宣传的案件,目前学界一般认为对媒体言论的法律规制大体形成于这两个案例。而在纽伦堡国际军事法庭审判结束后,美国军事法庭的后续审判中对纳粹党新闻领袖奥托·迪特里希的判决几乎被学界所遗忘。面对两个法庭相似罪名的指控,三个案件却出现截然不同的判决。通过展现这两个法庭的判决思路和推理,发现这三起案例对定义危害人类罪、灭绝种族罪、媒体仇视宣传责任意义深刻,促进了国际刑法上间接犯罪责任认定的完善。

网络仇恨言论治理的平台责任研究

中国目前正面临日益严重的仇恨言论问题,突出表现在网络仇恨言论十分泛滥。虽然中国目前并没有针对仇恨言论的专门性法律,但是相关规定散见于宪法、刑法、民法和行政法等法律法规。至于网络仇恨言论的治理,内容方面,需要明确网络仇恨言论的边界与范畴;责任方面,网络平台需要承担一些特殊侵权责任与注意义务。

憎恨言论的网络传播及其规制

憎恨言论是一种针对具有群体身份特征的个人或群体的,带有偏见、攻击、憎恨意味的言论类型。网络传播具有主体低门槛、内容发布低门槛和传播低门槛等特征,使网络成为憎恨言论传播的天然平台。刻板成见是憎恨言论网络传播的心理机制,憎恨言论的伤害性表现在个人及社会的各个层面,包括对传播对象心理层面、身体层面的伤害以及对民族团结、社会稳定的伤害。憎恨言论在各国的表现形式各不相同,对憎恨言论进行严格的规制或许还存在一些难度。

自媒体语境下网络民族仇恨言论法律规制问题探析

民族仇恨言论具有破坏民族团结,阻碍少数民族权利保障,破坏社会秩序和推动极端民族主义、恐怖主义的负面作用。个体意见网络表达、信息定制、自媒体伦理缺失和后现代网络文化构成自媒体条件下网络民族仇恨言论肆虐的背景。仇恨言论冲击了传统表达自由的理论预设和保护价值,表达自由的积极权利面向和作为集体权利的公共利益,呼唤对网络民族仇恨言论的规制。针对我国治理网络民族仇恨言论的现状,应当从法律制度建设、公民品质培育和努力消除民族发展上的不均衡状态入手,逐步加以根除。