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hate speech detection</title> <meta name="description" content="Search results for: Afaan Oromo hate speech detection"> <meta name="keywords" content="Afaan Oromo hate speech detection"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Afaan Oromo hate speech detection"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 4222</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Afaan Oromo hate speech detection</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4222</span> Hate Speech Detection Using Machine Learning: A Survey</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Edemealem%20Desalegn%20Kingawa">Edemealem Desalegn Kingawa</a>, <a href="https://publications.waset.org/abstracts/search?q=Kafte%20Tasew%20Timkete"> Kafte Tasew Timkete</a>, <a href="https://publications.waset.org/abstracts/search?q=Mekashaw%20Girmaw%20Abebe"> Mekashaw Girmaw Abebe</a>, <a href="https://publications.waset.org/abstracts/search?q=Terefe%20Feyisa"> Terefe Feyisa</a>, <a href="https://publications.waset.org/abstracts/search?q=Abiyot%20Bitew%20Mihretie"> Abiyot Bitew Mihretie</a>, <a href="https://publications.waset.org/abstracts/search?q=Senait%20Teklemarkos%20Haile"> Senait Teklemarkos Haile</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Currently, hate speech is a growing challenge for society, individuals, policymakers, and researchers, as social media platforms make it easy to anonymously create and grow online friends and followers and provide an online forum for debate about specific issues of community life, culture, politics, and others. Despite this, research on identifying and detecting hate speech is not satisfactory performance, and this is why future research on this issue is constantly called for. This paper provides a systematic review of the literature in this field, with a focus on approaches like word embedding techniques, machine learning, deep learning technologies, hate speech terminology, and other state-of-the-art technologies with challenges. In this paper, we have made a systematic review of the last six years of literature from Research Gate and Google Scholar. Furthermore, limitations, along with algorithm selection and use challenges, data collection, and cleaning challenges, and future research directions, are discussed in detail. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amharic%20hate%20speech" title="Amharic hate speech">Amharic hate speech</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning%20approach" title=" deep learning approach"> deep learning approach</a>, <a href="https://publications.waset.org/abstracts/search?q=hate%20speech%20detection%20review" title=" hate speech detection review"> hate speech detection review</a>, <a href="https://publications.waset.org/abstracts/search?q=Afaan%20Oromo%20hate%20speech%20detection" title=" Afaan Oromo hate speech detection"> Afaan Oromo hate speech detection</a> </p> <a href="https://publications.waset.org/abstracts/163615/hate-speech-detection-using-machine-learning-a-survey" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163615.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">178</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4221</span> Hate Speech Detection Using Deep Learning and Machine Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nabil%20Shawkat">Nabil Shawkat</a>, <a href="https://publications.waset.org/abstracts/search?q=Jamil%20Saquer"> Jamil Saquer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social media has accelerated our ability to engage with others and eliminated many communication barriers. On the other hand, the widespread use of social media resulted in an increase in online hate speech. This has drastic impacts on vulnerable individuals and societies. Therefore, it is critical to detect hate speech to prevent innocent users and vulnerable communities from becoming victims of hate speech. We investigate the performance of different deep learning and machine learning algorithms on three different datasets. Our results show that the BERT model gives the best performance among all the models by achieving an F1-score of 90.6% on one of the datasets and F1-scores of 89.7% and 88.2% on the other two datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hate%20speech" title="hate speech">hate speech</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=abusive%20words" title=" abusive words"> abusive words</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20classification" title=" text classification"> text classification</a> </p> <a href="https://publications.waset.org/abstracts/164751/hate-speech-detection-using-deep-learning-and-machine-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164751.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4220</span> Hate Speech Detection in Tunisian Dialect</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Helmi%20Baazaoui">Helmi Baazaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Zrigui"> Mounir Zrigui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study addresses the challenge of hate speech detection in Tunisian Arabic text, a critical issue for online safety and moderation. Leveraging the strengths of the AraBERT model, we fine-tuned and evaluated its performance against the Bi-LSTM model across four distinct datasets: T-HSAB, TNHS, TUNIZI-Dataset, and a newly compiled dataset with diverse labels such as Offensive Language, Racism, and Religious Intolerance. Our experimental results demonstrate that AraBERT significantly outperforms Bi-LSTM in terms of Recall, Precision, F1-Score, and Accuracy across all datasets. The findings underline the robustness of AraBERT in capturing the nuanced features of Tunisian Arabic and its superior capability in classification tasks. This research not only advances the technology for hate speech detection but also provides practical implications for social media moderation and policy-making in Tunisia. Future work will focus on expanding the datasets and exploring more sophisticated architectures to further enhance detection accuracy, thus promoting safer online interactions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hate%20speech%20detection" title="hate speech detection">hate speech detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Tunisian%20Arabic" title=" Tunisian Arabic"> Tunisian Arabic</a>, <a href="https://publications.waset.org/abstracts/search?q=AraBERT" title=" AraBERT"> AraBERT</a>, <a href="https://publications.waset.org/abstracts/search?q=Bi-LSTM" title=" Bi-LSTM"> Bi-LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=Gemini%20annotation%20tool" title=" Gemini annotation tool"> Gemini annotation tool</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media%20moderation" title=" social media moderation"> social media moderation</a> </p> <a href="https://publications.waset.org/abstracts/193877/hate-speech-detection-in-tunisian-dialect" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193877.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">11</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4219</span> Hate Speech in Selected Nigerian Newspapers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Laurel%20Chikwado%20Madumere">Laurel Chikwado Madumere</a>, <a href="https://publications.waset.org/abstracts/search?q=Kevin%20O.%20Ugorji"> Kevin O. Ugorji</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A speech is said to be full of hate when it appropriates disparaging and vituperative locutions and/or appellations, which are riddled with prejudices and misconceptions about an antagonizing party on the grounds of gender, race, political orientation, religious affiliations, tribe, etc. Due largely to the dichotomies and polarities that exist in Nigeria across political ideological spectrum, tribal affiliations, and gender contradistinctions, there are possibilities for the existence of socioeconomic, religious and political conditions that would induce, provoke and catalyze hate speeches in Nigeria’s mainstream media. Therefore the aim of this paper is to investigate, using select daily newspapers in Nigeria, the extent and complexity of those likely hate speeches that emanate from the pluralism in Nigeria and to set in to relief, the discrepancies and contrariety in the interpretation of those hate words. To achieve the above, the paper shall be qualitative in orientation as it shall be using the Speech Act Theory of J. L. Austin and J. R. Searle to interpret and evaluate the hate speeches in the select Nigerian daily newspapers. Also this paper shall help to elucidate the conditions that generate hate, and inform the government and NGOs how best to approach those conditions and put an end to the possible violence and extremism that emanate from extreme cases of hate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extremism" title="extremism">extremism</a>, <a href="https://publications.waset.org/abstracts/search?q=gender" title=" gender"> gender</a>, <a href="https://publications.waset.org/abstracts/search?q=hate%20speech" title=" hate speech"> hate speech</a>, <a href="https://publications.waset.org/abstracts/search?q=pluralism" title=" pluralism"> pluralism</a>, <a href="https://publications.waset.org/abstracts/search?q=prejudice" title=" prejudice"> prejudice</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20act%20theory" title=" speech act theory"> speech act theory</a> </p> <a href="https://publications.waset.org/abstracts/124330/hate-speech-in-selected-nigerian-newspapers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124330.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4218</span> Formulating a Definition of Hate Speech: From Divergence to Convergence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Avitus%20A.%20Agbor">Avitus A. Agbor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Numerous incidents, ranging from trivial to catastrophic, do come to mind when one reflects on hate. The victims of these belong to specific identifiable groups within communities. These experiences evoke discussions on Islamophobia, xenophobia, homophobia, anti-Semitism, racism, ethnic hatred, atheism, and other brutal forms of bigotry. Common to all these is an invisible but portent force that drives all of them: hatred. Such hatred is usually fueled by a profound degree of intolerance (to diversity) and the zeal to impose on others their beliefs and practices which they consider to be the conventional norm. More importantly, the perpetuation of these hateful acts is the unfortunate outcome of an overplay of invectives and hate speech which, to a greater extent, cannot be divorced from hate. From a legal perspective, acknowledging the existence of an undeniable link between hate speech and hate is quite easy. However, both within and without legal scholarship, the notion of “hate speech” remains a conundrum: a phrase that is quite easily explained through experiences than propounding a watertight definition that captures the entire essence and nature of what it is. The problem is further compounded by a few factors: first, within the international human rights framework, the notion of hate speech is not used. In limiting the right to freedom of expression, the ICCPR simply excludes specific kinds of speeches (but does not refer to them as hate speech). Regional human rights instruments are not so different, except for the subsequent developments that took place in the European Union in which the notion has been carefully delineated, and now a much clearer picture of what constitutes hate speech is provided. The legal architecture in domestic legal systems clearly shows differences in approaches and regulation: making it more difficult. In short, what may be hate speech in one legal system may very well be acceptable legal speech in another legal system. Lastly, the cornucopia of academic voices on the issue of hate speech exude the divergence thereon. Yet, in the absence of a well-formulated and universally acceptable definition, it is important to consider how hate speech can be defined. Taking an evidence-based approach, this research looks into the issue of defining hate speech in legal scholarship and how and why such a formulation is of critical importance in the prohibition and prosecution of hate speech. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hate%20speech" title="hate speech">hate speech</a>, <a href="https://publications.waset.org/abstracts/search?q=international%20human%20rights%20law" title=" international human rights law"> international human rights law</a>, <a href="https://publications.waset.org/abstracts/search?q=international%20criminal%20law" title=" international criminal law"> international criminal law</a>, <a href="https://publications.waset.org/abstracts/search?q=freedom%20of%20expression" title=" freedom of expression"> freedom of expression</a> </p> <a href="https://publications.waset.org/abstracts/171646/formulating-a-definition-of-hate-speech-from-divergence-to-convergence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171646.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">76</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4217</span> Optimization of Hate Speech and Abusive Language Detection on Indonesian-language Twitter using Genetic Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rikson%20Gultom">Rikson Gultom</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=abusive%20language" title="abusive language">abusive language</a>, <a href="https://publications.waset.org/abstracts/search?q=hate%20speech" title=" hate speech"> hate speech</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a> </p> <a href="https://publications.waset.org/abstracts/146661/optimization-of-hate-speech-and-abusive-language-detection-on-indonesian-language-twitter-using-genetic-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146661.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">128</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4216</span> Detecting Hate Speech And Cyberbullying Using Natural Language Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N%C3%A1dia%20Pereira">Nádia Pereira</a>, <a href="https://publications.waset.org/abstracts/search?q=Paula%20Ferreira"> Paula Ferreira</a>, <a href="https://publications.waset.org/abstracts/search?q=Sofia%20Francisco"> Sofia Francisco</a>, <a href="https://publications.waset.org/abstracts/search?q=Sofia%20Oliveira"> Sofia Oliveira</a>, <a href="https://publications.waset.org/abstracts/search?q=Sidclay%20Souza"> Sidclay Souza</a>, <a href="https://publications.waset.org/abstracts/search?q=Paula%20Paulino"> Paula Paulino</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20Margarida%20Veiga%20Sim%C3%A3o"> Ana Margarida Veiga Simão</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social media has progressed into a platform for hate speech among its users, and thus, there is an increasing need to develop automatic detection classifiers of offense and conflicts to help decrease the prevalence of such incidents. Online communication can be used to intentionally harm someone, which is why such classifiers could be essential in social networks. A possible application of these classifiers is the automatic detection of cyberbullying. Even though identifying the aggressive language used in online interactions could be important to build cyberbullying datasets, there are other criteria that must be considered. Being able to capture the language, which is indicative of the intent to harm others in a specific context of online interaction is fundamental. Offense and hate speech may be the foundation of online conflicts, which have become commonly used in social media and are an emergent research focus in machine learning and natural language processing. This study presents two Portuguese language offense-related datasets which serve as examples for future research and extend the study of the topic. The first is similar to other offense detection related datasets and is entitled Aggressiveness dataset. The second is a novelty because of the use of the history of the interaction between users and is entitled the Conflicts/Attacks dataset. Both datasets were developed in different phases. Firstly, we performed a content analysis of verbal aggression witnessed by adolescents in situations of cyberbullying. Secondly, we computed frequency analyses from the previous phase to gather lexical and linguistic cues used to identify potentially aggressive conflicts and attacks which were posted on Twitter. Thirdly, thorough annotation of real tweets was performed byindependent postgraduate educational psychologists with experience in cyberbullying research. Lastly, we benchmarked these datasets with other machine learning classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aggression" title="aggression">aggression</a>, <a href="https://publications.waset.org/abstracts/search?q=classifiers" title=" classifiers"> classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=cyberbullying" title=" cyberbullying"> cyberbullying</a>, <a href="https://publications.waset.org/abstracts/search?q=datasets" title=" datasets"> datasets</a>, <a href="https://publications.waset.org/abstracts/search?q=hate%20speech" title=" hate speech"> hate speech</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/142382/detecting-hate-speech-and-cyberbullying-using-natural-language-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142382.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">228</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4215</span> Understanding the Motivations behind the Assassination of Turkish Armenian Journalist, Hrant Dink</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nusret%20Mesut%20Sahin">Nusret Mesut Sahin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hrant Dink, a prominent Turkish-Armenian journalist, and editor-in-chief of the bilingual Turkish-Armenian newspaper Agos was assassinated in Istanbul on January 19th, 2007 by a nationalist extremist, Ogun Samast. Dink had been voicing the atrocities against the Armenians between 1915 and 1922 during the Ottoman rule, and his comments on the issue appeared in the Turkish media many times before his assassination. It has been argued that the suffocating atmosphere created by the Turkish news media targeting Mr. Dink made him a target of an extremist Turkish juvenile. This study analyzes the media news to understand and explain why Hrant Dink became the target of a nationalist extremist. In this research, content analysis of news articles (N= 170) is conducted to identify whether there is a link between hate speech against Hrant Dink in the Turkish media and his assassination. The content of the newspaper articles is categorized and coded according to the hate language being used. The analysis suggested that Turkish media paved the way for Dink’s assassination. Hate speech against Hrant Dink on the media had risen gradually before the assassination. The study also found that the number of news stories covering hate speech and racist discourse against non-Muslim citizens of Turkey also increased dramatically before the assassination. Therefore, hate speech against minorities in media narratives and news reports should be monitored, and political figures or leaders of social groups who are targeted by some media outlets should be protected. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hrant%20Dink" title="Hrant Dink">Hrant Dink</a>, <a href="https://publications.waset.org/abstracts/search?q=assassination" title=" assassination"> assassination</a>, <a href="https://publications.waset.org/abstracts/search?q=Turkish%20Armenian%20journalist" title=" Turkish Armenian journalist"> Turkish Armenian journalist</a>, <a href="https://publications.waset.org/abstracts/search?q=media" title=" media"> media</a> </p> <a href="https://publications.waset.org/abstracts/91256/understanding-the-motivations-behind-the-assassination-of-turkish-armenian-journalist-hrant-dink" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91256.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">159</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4214</span> Self-Supervised Learning for Hate-Speech Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shrabani%20Ghosh">Shrabani Ghosh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automatic offensive language detection in social media has become a stirring task in today's NLP. Manual Offensive language detection is tedious and laborious work where automatic methods based on machine learning are only alternatives. Previous works have done sentiment analysis over social media in different ways such as supervised, semi-supervised, and unsupervised manner. Domain adaptation in a semi-supervised way has also been explored in NLP, where the source domain and the target domain are different. In domain adaptation, the source domain usually has a large amount of labeled data, while only a limited amount of labeled data is available in the target domain. Pretrained transformers like BERT, RoBERTa models are fine-tuned to perform text classification in an unsupervised manner to perform further pre-train masked language modeling (MLM) tasks. In previous work, hate speech detection has been explored in Gab.ai, which is a free speech platform described as a platform of extremist in varying degrees in online social media. In domain adaptation process, Twitter data is used as the source domain, and Gab data is used as the target domain. The performance of domain adaptation also depends on the cross-domain similarity. Different distance measure methods such as L2 distance, cosine distance, Maximum Mean Discrepancy (MMD), Fisher Linear Discriminant (FLD), and CORAL have been used to estimate domain similarity. Certainly, in-domain distances are small, and between-domain distances are expected to be large. The previous work finding shows that pretrain masked language model (MLM) fine-tuned with a mixture of posts of source and target domain gives higher accuracy. However, in-domain performance of the hate classifier on Twitter data accuracy is 71.78%, and out-of-domain performance of the hate classifier on Gab data goes down to 56.53%. Recently self-supervised learning got a lot of attention as it is more applicable when labeled data are scarce. Few works have already been explored to apply self-supervised learning on NLP tasks such as sentiment classification. Self-supervised language representation model ALBERTA focuses on modeling inter-sentence coherence and helps downstream tasks with multi-sentence inputs. Self-supervised attention learning approach shows better performance as it exploits extracted context word in the training process. In this work, a self-supervised attention mechanism has been proposed to detect hate speech on Gab.ai. This framework initially classifies the Gab dataset in an attention-based self-supervised manner. On the next step, a semi-supervised classifier trained on the combination of labeled data from the first step and unlabeled data. The performance of the proposed framework will be compared with the results described earlier and also with optimized outcomes obtained from different optimization techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attention%20learning" title="attention learning">attention learning</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20model" title=" language model"> language model</a>, <a href="https://publications.waset.org/abstracts/search?q=offensive%20language%20detection" title=" offensive language detection"> offensive language detection</a>, <a href="https://publications.waset.org/abstracts/search?q=self-supervised%20learning" title=" self-supervised learning"> self-supervised learning</a> </p> <a href="https://publications.waset.org/abstracts/147950/self-supervised-learning-for-hate-speech-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147950.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">106</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4213</span> Conspiracy Theory in Discussions of the Coronavirus Pandemic in the Gulf Region</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rasha%20Salameh">Rasha Salameh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In light of the tense relationship between Saudi Arabia and Iran, this research paper sheds some light on Al-Arabiya’s reporting of Coronavirus in the Gulf. Particularly because most of the cases, in the beginning, were coming from Iran, some programs of this Saudi channel embraced a conspiracy theory. Hate speech has been used in talking about the topic and discussing it. The results of these discussions will be detailed in this paper in percentages with regard to the research sample, which includes five programs on Al-Arabiya channel: ‘DNA’, ‘Marraya’ (Mirrors), ‘Panorama’, ‘Tafaolcom’ (Your Interaction) and the ‘Diplomatic Street’, in the period between January 19, that is, the date of the first case in Iran, and April 10, 2020. The research shows the use of a conspiracy theory in the programs, in addition to some professional violations. The surveyed sample also shows that the matter receded due to the Arab Gulf states' preoccupation with the successively increasing cases that have appeared there since the start of the pandemic. The results indicate that hate speech was present in the sample at a rate of 98.1% and that most of the programs that dealt with the Iranian issue under the Corona pandemic on Al Arabiya used the conspiracy theory at a rate of 75.5%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Al-Arabiya" title="Al-Arabiya">Al-Arabiya</a>, <a href="https://publications.waset.org/abstracts/search?q=Iran" title=" Iran"> Iran</a>, <a href="https://publications.waset.org/abstracts/search?q=Corona" title=" Corona"> Corona</a>, <a href="https://publications.waset.org/abstracts/search?q=hate%20speech" title=" hate speech"> hate speech</a>, <a href="https://publications.waset.org/abstracts/search?q=conspiracy%20theory" title=" conspiracy theory"> conspiracy theory</a>, <a href="https://publications.waset.org/abstracts/search?q=politicization%20of%20the%20pandemic" title=" politicization of the pandemic"> politicization of the pandemic</a> </p> <a href="https://publications.waset.org/abstracts/136292/conspiracy-theory-in-discussions-of-the-coronavirus-pandemic-in-the-gulf-region" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136292.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4212</span> A Comparative Analysis on the Impact of the Prevention and Combating of Hate Crimes and Hate Speech Bill of 2016 on the Rights to Human Dignity, Equality, and Freedom in South Africa</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tholaine%20Matadi">Tholaine Matadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> South Africa is a democratic country with a historical record of racially-motivated marginalisation and exclusion of the majority. During the apartheid era the country was run along pieces of legislation and policies based on racial segregation. The system held a tight clamp on interracial mixing which forced people to remain in segregated areas. For example, a citizen from the Indian community could not own property in an area allocated to white people. In this way, a great majority of people were denied basic human rights. Now, there is a supreme constitution with an entrenched justiciable Bill of Rights founded on democratic values of social justice, human dignity, equality and the advancement of human rights and freedoms. The Constitution also enshrines the values of non-racialism and non-sexism. The Constitutional Court has the power to declare unconstitutional any law or conduct considered to be inconsistent with it. Now, more than two decades down the line, despite the abolition of apartheid, there is evidence that South Africa still experiences hate crimes which violate the entrenched right of vulnerable groups not to be discriminated against on the basis of race, sexual orientation, gender, national origin, occupation, or disability. To remedy this mischief parliament has responded by drafting the Prevention and Combatting of Hate Crimes and Hate Speech Bill. The Bill has been disseminated for public comment and suggestions. It is intended to combat hate crimes and hate speech based on sheer prejudice. The other purpose of the Bill is to bring South Africa in line with international human rights instruments against racism, racial discrimination, xenophobia and related expressions of intolerance identified in several international instruments. It is against this backdrop that this paper intends to analyse the impact of the Bill on the rights to human dignity, equality, and freedom. This study is significant because the Bill was highly contested and creates a huge debate. This study relies on a qualitative evaluative approach based on desktop and library research. The article recurs to primary and secondary sources. For comparative purpose, the paper compares South Africa with countries such as Australia, Canada, Kenya, Cuba, and United Kingdom which have criminalised hate crimes and hate speech. The finding from this study is that despite the Bill’s expressed positive intentions, this draft legislation is problematic for several reasons. The main reason is that it generates considerable controversy mostly because it is considered to infringe the right to freedom of expression. Though the author suggests that the Bill should not be rejected in its entirety, she notes the brutal psychological effect of hate crimes on their direct victims and the writer emphasises that a legislature can succeed to combat hate-crimes only if it provides for them as a separate stand-alone category of offences. In view of these findings, the study recommended that since hate speech clauses have a negative impact on freedom of expression it can be promulgated, subject to the legislature enacting the Prevention and Combatting of Hate-Crimes Bill as a stand-alone law which criminalises hate crimes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=freedom%20of%20expression" title="freedom of expression">freedom of expression</a>, <a href="https://publications.waset.org/abstracts/search?q=hate%20crimes" title=" hate crimes"> hate crimes</a>, <a href="https://publications.waset.org/abstracts/search?q=hate%20speech" title=" hate speech"> hate speech</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20dignity" title=" human dignity"> human dignity</a> </p> <a href="https://publications.waset.org/abstracts/91019/a-comparative-analysis-on-the-impact-of-the-prevention-and-combating-of-hate-crimes-and-hate-speech-bill-of-2016-on-the-rights-to-human-dignity-equality-and-freedom-in-south-africa" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91019.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">173</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4211</span> Detection of Clipped Fragments in Speech Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sergei%20Aleinik">Sergei Aleinik</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuri%20Matveev"> Yuri Matveev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper a novel method for the detection of clipping in speech signals is described. It is shown that the new method has better performance than known clipping detection methods, is easy to implement, and is robust to changes in signal amplitude, size of data, etc. Statistical simulation results are presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clipping" title="clipping">clipping</a>, <a href="https://publications.waset.org/abstracts/search?q=clipped%20signal" title=" clipped signal"> clipped signal</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20signal%20processing" title=" speech signal processing"> speech signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20signal%20processing" title=" digital signal processing"> digital signal processing</a> </p> <a href="https://publications.waset.org/abstracts/4816/detection-of-clipped-fragments-in-speech-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4816.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">393</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4210</span> Speech Detection Model Based on Deep Neural Networks Classifier for Speech Emotions Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Shoiynbek">A. Shoiynbek</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Kozhakhmet"> K. Kozhakhmet</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Menezes"> P. Menezes</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Kuanyshbay"> D. Kuanyshbay</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Bayazitov"> D. Bayazitov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speech emotion recognition has received increasing research interest all through current years. There was used emotional speech that was collected under controlled conditions in most research work. Actors imitating and artificially producing emotions in front of a microphone noted those records. There are four issues related to that approach, namely, (1) emotions are not natural, and it means that machines are learning to recognize fake emotions. (2) Emotions are very limited by quantity and poor in their variety of speaking. (3) There is language dependency on SER. (4) Consequently, each time when researchers want to start work with SER, they need to find a good emotional database on their language. In this paper, we propose the approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describe the sequence of actions of the proposed approach. One of the first objectives of the sequence of actions is a speech detection issue. The paper gives a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian languages. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To illustrate the working capacity of the developed model, we have performed an analysis of speech detection and extraction from real tasks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20networks" title="deep neural networks">deep neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20detection" title=" speech detection"> speech detection</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20emotion%20recognition" title=" speech emotion recognition"> speech emotion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Mel-frequency%20cepstrum%20coefficients" title=" Mel-frequency cepstrum coefficients"> Mel-frequency cepstrum coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=collecting%20speech%20emotion%20corpus" title=" collecting speech emotion corpus"> collecting speech emotion corpus</a>, <a href="https://publications.waset.org/abstracts/search?q=collecting%20speech%20emotion%20dataset" title=" collecting speech emotion dataset"> collecting speech emotion dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazakh%20speech%20dataset" title=" Kazakh speech dataset"> Kazakh speech dataset</a> </p> <a href="https://publications.waset.org/abstracts/152814/speech-detection-model-based-on-deep-neural-networks-classifier-for-speech-emotions-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152814.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">101</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4209</span> Speech Detection Model Based on Deep Neural Networks Classifier for Speech Emotions Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aisultan%20Shoiynbek">Aisultan Shoiynbek</a>, <a href="https://publications.waset.org/abstracts/search?q=Darkhan%20Kuanyshbay"> Darkhan Kuanyshbay</a>, <a href="https://publications.waset.org/abstracts/search?q=Paulo%20Menezes"> Paulo Menezes</a>, <a href="https://publications.waset.org/abstracts/search?q=Akbayan%20Bekarystankyzy"> Akbayan Bekarystankyzy</a>, <a href="https://publications.waset.org/abstracts/search?q=Assylbek%20Mukhametzhanov"> Assylbek Mukhametzhanov</a>, <a href="https://publications.waset.org/abstracts/search?q=Temirlan%20Shoiynbek"> Temirlan Shoiynbek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speech emotion recognition (SER) has received increasing research interest in recent years. It is a common practice to utilize emotional speech collected under controlled conditions recorded by actors imitating and artificially producing emotions in front of a microphone. There are four issues related to that approach: emotions are not natural, meaning that machines are learning to recognize fake emotions; emotions are very limited in quantity and poor in variety of speaking; there is some language dependency in SER; consequently, each time researchers want to start work with SER, they need to find a good emotional database in their language. This paper proposes an approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describes the sequence of actions involved in the proposed approach. One of the first objectives in the sequence of actions is the speech detection issue. The paper provides a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To investigate the working capacity of the developed model, an analysis of speech detection and extraction from real tasks has been performed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20networks" title="deep neural networks">deep neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20detection" title=" speech detection"> speech detection</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20emotion%20recognition" title=" speech emotion recognition"> speech emotion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Mel-frequency%20cepstrum%20coefficients" title=" Mel-frequency cepstrum coefficients"> Mel-frequency cepstrum coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=collecting%20speech%20emotion%20corpus" title=" collecting speech emotion corpus"> collecting speech emotion corpus</a>, <a href="https://publications.waset.org/abstracts/search?q=collecting%20speech%20emotion%20dataset" title=" collecting speech emotion dataset"> collecting speech emotion dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazakh%20speech%20dataset" title=" Kazakh speech dataset"> Kazakh speech dataset</a> </p> <a href="https://publications.waset.org/abstracts/189328/speech-detection-model-based-on-deep-neural-networks-classifier-for-speech-emotions-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189328.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">26</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4208</span> Freedom of Speech and Involvement in Hatred Speech on Social Media Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sara%20Chinnasamy">Sara Chinnasamy</a>, <a href="https://publications.waset.org/abstracts/search?q=Michelle%20Gun"> Michelle Gun</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Adnan%20Hashim"> M. Adnan Hashim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Federal Constitution guarantees Malaysians the right to free speech and expression; yet hatred speech can be commonly found on social media platforms such as Facebook, Twitter, and Instagram. In Malaysia social media sphere, most hatred speech involves religion, race and politics. Recent cases of racial attacks on social media have created social tensions among Malaysians. Many Malaysians always argue on their rights to freedom of speech. However, there are laws that limit their expression to the public and protecting social media users from being a victim of hate speech. This paper aims to explore the attitude and involvement of Malaysian netizens towards freedom of speech and hatred speech on social media. It also examines the relationship between involvement in hatred speech among Malaysian netizens and attitude towards freedom of speech. For most Malaysians, practicing total freedom of speech in the open is unthinkable. As a result, the best channel to articulate their feelings and opinions liberally is the internet. With the advent of the internet medium, more and more Malaysians are conveying their viewpoints using the various internet channels although sensitivity of the audience is seldom taken into account. Consequently, this situation has led to pockets of social disharmony among the citizens. Although this unhealthy activity is denounced by the authority, netizens are generally of the view that they have the right to write anything they want. Using the quantitative method, survey was conducted among Malaysians aged between 18 and 50 years who are active social media users. Results from the survey reveal that despite a weak relationship level between hatred speech involvement on social media and attitude towards freedom of speech, the association is still considerably significant. As such, it can be safely presumed that hatred speech on social media occurs due to the freedom of speech that exists by way of social media channels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=freedom%20of%20speech" title="freedom of speech">freedom of speech</a>, <a href="https://publications.waset.org/abstracts/search?q=hatred%20speech" title=" hatred speech"> hatred speech</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a>, <a href="https://publications.waset.org/abstracts/search?q=Malaysia" title=" Malaysia"> Malaysia</a>, <a href="https://publications.waset.org/abstracts/search?q=netizens" title=" netizens"> netizens</a> </p> <a href="https://publications.waset.org/abstracts/72863/freedom-of-speech-and-involvement-in-hatred-speech-on-social-media-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72863.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">457</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4207</span> The Evolution of Online Hate: How Decades of Tactical and Technological Innovation Created a Hate Epidemic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kashvi%20Jain">Kashvi Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Adam%20Burston"> Adam Burston</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Right-wing social movements are a dominant force in American politics, as evidenced by the January 6th Insurrection, the prevalence of extremist conspiracy theories, and a nationwide surge in hate crime. Despite an abundance of scholarship on contemporary right-wing extremism, there is little scholarship that explains their rise. This paper examines how the white power movement developed through tactical innovation and strategic use of increasingly powerful digital technologies. Using qualitative content analysis of archived digital bulletin boards and websites, we examine right-wing extremists’ digital communication during three consequential time periods of tactical and technological innovation: pre-internet (1980s), web 1.0 (1990s), and web 2.0 (2000s). Our analysis suggests that right-wing activists innovatively exploited the features and affordances of digital technologies and their knowledge of free speech rights to spread supremacist collective identity and ideology. Beyond our empirical contribution, we offer policy advice that school administrators can employ to limit hate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=leaderless%20resistance" title="leaderless resistance">leaderless resistance</a>, <a href="https://publications.waset.org/abstracts/search?q=technological%20affordances" title=" technological affordances"> technological affordances</a>, <a href="https://publications.waset.org/abstracts/search?q=anti-defamation%20league" title=" anti-defamation league"> anti-defamation league</a>, <a href="https://publications.waset.org/abstracts/search?q=white%20power%20movement" title=" white power movement"> white power movement</a>, <a href="https://publications.waset.org/abstracts/search?q=tactical" title=" tactical"> tactical</a> </p> <a href="https://publications.waset.org/abstracts/171301/the-evolution-of-online-hate-how-decades-of-tactical-and-technological-innovation-created-a-hate-epidemic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171301.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">69</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4206</span> The Importance of Right Speech in Buddhism and Its Relevance Today</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gautam%20Sharda">Gautam Sharda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The concept of right speech is the third stage of the noble eightfold path as prescribed by the Buddha and followed by millions of practicing Buddhists. The Buddha lays a lot of importance on the notion of right speech (Samma Vacca). In the Angutara Nikaya, the Buddha mentioned what constitutes right speech, which is basically four kinds of abstentions; namely abstaining from false speech, abstaining from slanderous speech, abstaining from harsh or hateful speech and abstaining from idle chatter. The Buddha gives reasons in support of his view as to why abstaining from these four kinds of speeches is favourable not only for maintaining the peace and equanimity within an individual but also within a society. It is a known fact that when we say something harsh or slanderous to others, it eventually affects our individual peace of mind too. We also know about the many examples of hate speeches which have led to senseless cases of violence and which are well documented within our country and the world. Also, indulging in false speech is not a healthy sign for individuals within a group as this kind of a social group which is based on falsities and lies cannot really survive for long and will eventually lead to chaos. Buddha also told us to refrain from idle chatter or gossip as generally we have seen that idle chatter or gossip does more harm than any good to the individual and the society. Hence, if most of us actually inculcate this third stage (namely, right speech) of the noble eightfold path of the Buddha in our daily life, it would be highly beneficial both for the individual and for the harmony of the society. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Buddhism" title="Buddhism">Buddhism</a>, <a href="https://publications.waset.org/abstracts/search?q=speech" title=" speech"> speech</a>, <a href="https://publications.waset.org/abstracts/search?q=individual" title=" individual"> individual</a>, <a href="https://publications.waset.org/abstracts/search?q=society" title=" society"> society</a> </p> <a href="https://publications.waset.org/abstracts/111716/the-importance-of-right-speech-in-buddhism-and-its-relevance-today" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/111716.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">264</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4205</span> Towards a Conscious Design in AI by Overcoming Dark Patterns</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ayse%20Arslan">Ayse Arslan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the important elements underpinning a conscious design is the degree of toxicity in communication. This study explores the mechanisms and strategies for identifying toxic content by avoiding dark patterns. Given the breadth of hate and harassment attacks, this study explores a threat model and taxonomy to assist in reasoning about strategies for detection, prevention, mitigation, and recovery. In addition to identifying some relevant techniques such as nudges, automatic detection, or human-ranking, the study suggests the use of major metrics such as the overhead and friction of solutions on platforms and users or balancing false positives (e.g., incorrectly penalizing legitimate users) against false negatives (e.g., users exposed to hate and harassment) to maintain a conscious design towards fairness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AI" title="AI">AI</a>, <a href="https://publications.waset.org/abstracts/search?q=ML" title=" ML"> ML</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithms" title=" algorithms"> algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=policy" title=" policy"> policy</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20design" title=" system design"> system design</a> </p> <a href="https://publications.waset.org/abstracts/150465/towards-a-conscious-design-in-ai-by-overcoming-dark-patterns" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150465.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">121</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4204</span> Ethnic Conflict Dynamics in the Ethiopian Federation: Case of the Oromo-Somali Conflict</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takele%20Bekele%20Bayu">Takele Bekele Bayu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Though Ethiopia is an ancient country with ethnocultural and linguistic diversity, modern Ethiopia came into being in the second half of the 19th century under the military expansion of King Menelik II. Since then, the subsequent political system in the country failed to recognize and accommodate the country’s ethnolinguistic diversity. However, in 1991 the new government led by the Ethiopian People's Revolutionary Democratic Front (EPRDF) adopted federal-state structuring whereby constitutionally recognized and institutionally accommodated the country’s diversity. This investigation aimed to analyze drivers of ethnic conflict and its dynamism along the Eastern shared border of the Somali and Oromia regional administrations within the federal framework. The paper employed a comparative research design, adopted mixed research methods, and used survey questionnaires and focus group discussions (FGDs) for data collection. The study found that the Somali-Oromo conflict is complex and the dynamics and the sources of conflict in the study areas are similar. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ethiopia" title="Ethiopia">Ethiopia</a>, <a href="https://publications.waset.org/abstracts/search?q=Oromo" title=" Oromo"> Oromo</a>, <a href="https://publications.waset.org/abstracts/search?q=Somali" title=" Somali"> Somali</a>, <a href="https://publications.waset.org/abstracts/search?q=ethnic%20conflict" title=" ethnic conflict"> ethnic conflict</a>, <a href="https://publications.waset.org/abstracts/search?q=federalism" title=" federalism"> federalism</a> </p> <a href="https://publications.waset.org/abstracts/169006/ethnic-conflict-dynamics-in-the-ethiopian-federation-case-of-the-oromo-somali-conflict" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169006.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">73</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4203</span> The Challenges Involved in Investigating and Prosecuting Hate Crime Online</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mark%20Williams">Mark Williams</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The digital revolution has radically transformed our social environment creating vast opportunities for interconnectivity and social interaction. This revolution, however, has also changed the reach and impact of hate crime, with social media providing a new platform to victimize and harass users in their homes. In this way, developments in the information and communication technologies have exacerbated and facilitated the commission of hate crime, increasing its prevalence and impact. Unfortunately, legislators, policymakers and criminal justice professionals have struggled to keep pace with these technological developments, reducing their ability to intervene in, regulate and govern the commission of hate crimes online. This work is further complicated by the global nature of this crime due to the tendency for offenders and victims to reside in multiple different jurisdictions, as well as the need for criminal justice professionals to obtain the cooperation of private companies to access information required for prosecution. Drawing on in-depth interviews with key criminal justice professionals and policymakers with detailed knowledge in this area, this paper examines the specific challenges the police and prosecution services face as they attempt to intervene in and prosecute the commission of hate crimes online. It is argued that any attempt to reduce online othering, such as the commission of hate crimes online, must be multifaceted, collaborative and involve both innovative technological solutions as well as internationally agreed ethical and legal frameworks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cybercrime" title="cybercrime">cybercrime</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20policing" title=" digital policing"> digital policing</a>, <a href="https://publications.waset.org/abstracts/search?q=hate%20crime" title=" hate crime"> hate crime</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a> </p> <a href="https://publications.waset.org/abstracts/103720/the-challenges-involved-in-investigating-and-prosecuting-hate-crime-online" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103720.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">227</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4202</span> Automatic Segmentation of the Clean Speech Signal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Ben%20Messaoud">M. A. Ben Messaoud</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Bouzid"> A. Bouzid</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Ellouze"> N. Ellouze</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speech Segmentation is the measure of the change point detection for partitioning an input speech signal into regions each of which accords to only one speaker. In this paper, we apply two features based on multi-scale product (MP) of the clean speech, namely the spectral centroid of MP, and the zero crossings rate of MP. We focus on multi-scale product analysis as an important tool for segmentation extraction. The multi-scale product is based on making the product of the speech wavelet transform coefficients at three successive dyadic scales. We have evaluated our method on the Keele database. Experimental results show the effectiveness of our method presenting a good performance. It shows that the two simple features can find word boundaries, and extracted the segments of the clean speech. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multiscale%20product" title="multiscale product">multiscale product</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20centroid" title=" spectral centroid"> spectral centroid</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20segmentation" title=" speech segmentation"> speech segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=zero%20crossings%20rate" title=" zero crossings rate"> zero crossings rate</a> </p> <a href="https://publications.waset.org/abstracts/17566/automatic-segmentation-of-the-clean-speech-signal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17566.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">500</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4201</span> Ethical Challenges for Journalists in Times of Fake News and Hate Speech: A Survey with German Journalists</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Laura%20C.%20Solzbacher">Laura C. Solzbacher</a>, <a href="https://publications.waset.org/abstracts/search?q=Caja%20Thimm"> Caja Thimm</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Journalists worldwide have been confronted with a variety of ethical challenges over the last years. Because of massive changes in media technology and the public sphere, especially online journalism has trouble to uphold the fundamental values of journalism. In particular, the increasing amount of fake news and hate speech puts journalists under more and more pressure. In order to understand better how journalists judge this development and how they adapt in their daily work, a survey with journalists in Germany was carried out. 303 professional journalists participated in an online questionnaire. Results show that 65% underline that economic pressure grows and nearly the same number describe a change in the role of journalists in society. Furthermore, 61% agree that they put more time into research to secure their work against accusations of fabricating fake news. Interestingly, over 60% see a change in the role of journalists in society. The majority (85%) confirms that print journalism has to give way for online platforms and that the influence of social media for journalism grows (75%). Half of the surveyed advocate for more personalized public activism on part of journalists, such as appearance in talk shows and public talks. The results of the study will be discussed in light of the ongoing debate on ethical standards as a condition for a sustainable and trustworthy digital public sphere. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ethics" title="ethics">ethics</a>, <a href="https://publications.waset.org/abstracts/search?q=fake%20news" title=" fake news"> fake news</a>, <a href="https://publications.waset.org/abstracts/search?q=journalism" title=" journalism"> journalism</a>, <a href="https://publications.waset.org/abstracts/search?q=public%20sphere" title=" public sphere"> public sphere</a> </p> <a href="https://publications.waset.org/abstracts/92934/ethical-challenges-for-journalists-in-times-of-fake-news-and-hate-speech-a-survey-with-german-journalists" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92934.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">269</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4200</span> Subband Coding and Glottal Closure Instant (GCI) Using SEDREAMS Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Harisudha%20Kuresan">Harisudha Kuresan</a>, <a href="https://publications.waset.org/abstracts/search?q=Dhanalakshmi%20Samiappan"> Dhanalakshmi Samiappan</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Rama%20Rao"> T. Rama Rao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In modern telecommunication applications, Glottal Closure Instants location finding is important and is directly evaluated from the speech waveform. Here, we study the GCI using Speech Event Detection using Residual Excitation and the Mean Based Signal (SEDREAMS) algorithm. Speech coding uses parameter estimation using audio signal processing techniques to model the speech signal combined with generic data compression algorithms to represent the resulting modeled in a compact bit stream. This paper proposes a sub-band coder SBC, which is a type of transform coding and its performance for GCI detection using SEDREAMS are evaluated. In SBCs code in the speech signal is divided into two or more frequency bands and each of these sub-band signal is coded individually. The sub-bands after being processed are recombined to form the output signal, whose bandwidth covers the whole frequency spectrum. Then the signal is decomposed into low and high-frequency components and decimation and interpolation in frequency domain are performed. The proposed structure significantly reduces error, and precise locations of Glottal Closure Instants (GCIs) are found using SEDREAMS algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SEDREAMS" title="SEDREAMS">SEDREAMS</a>, <a href="https://publications.waset.org/abstracts/search?q=GCI" title=" GCI"> GCI</a>, <a href="https://publications.waset.org/abstracts/search?q=SBC" title=" SBC"> SBC</a>, <a href="https://publications.waset.org/abstracts/search?q=GOI" title=" GOI"> GOI</a> </p> <a href="https://publications.waset.org/abstracts/56336/subband-coding-and-glottal-closure-instant-gci-using-sedreams-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56336.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">356</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4199</span> “Divorced Women are Like Second-Hand Clothes” - Hate Language in Media Discourse</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sopio%20Totibadze">Sopio Totibadze</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Although the legal framework of Georgia reflects the main principles of gender equality and is in line with the international situation, Georgia remains a male-dominated society. This means that men prevail in many areas of social, economic, and political life, which frequently gives women a subordinate status in society and the family. According to the latest studies, “violence against women and girls in Georgia is also recognized as a public problem, and it is necessary to focus on it”. Moreover, the Public Defender's report (2019) reveals that “in the last five years, 151 women were killed in Georgia due to gender and family violence”. Unfortunately, there are frequent cases of crimes based on gender-based oppression in Georgia, which pose a threat not only to women but also to people of any gender whose desires and aspirations do not correspond to the gender norms and roles prevailing in society. It is well-known that language is often used as a tool for gender oppression. Therefore, feminist and gender studies in linguistics ultimately serve to represent the problem, reflect on it, and propose ways to solve it. Together with technical advancement in communication, a new form of discrimination has arisen- hate language against women in electronic media discourse. Due to the nature of social media and the internet, messages containing hate language can spread in seconds and reach millions of people. However, only a few know about the detrimental effects they may have on the addressee and society. This paper aims to analyse the hateful comments directed at women on various media platforms to determine the linguistic strategies used while attacking women and the reasons why women may fall victim to this type of hate language. The data have been collected over six months, and overall, 500 comments will be examined for the paper. Qualitative and quantitative analysis was chosen for the methodology of the study. The comments posted on various media platforms have been selected manually due to several reasons, the most important being the problem of identifying hate speech as it can disguise itself in different ways- humour, memes, etc. The comments on the articles, posts, pictures, and videos selected for sociolinguistic analysis depict a woman, a taboo topic, or a scandalous event centred on a woman that triggered hate language towards the person to whom the post/article was dedicated. The study has revealed that a woman can become a victim of hatred directed at them if they do something considered to be a deviation from a societal norm, namely, get a divorce, be sexually active, be vocal about feministic values, and talk about taboos. Interestingly, people who utilize hate language are not only men trying to “normalize” the prejudiced patriarchal values but also women who are equally active in bringing down a "strong" woman. The paper also aims to raise awareness about the hate language directed at women, as being knowledgeable about the issue at hand is the first step to tackling it. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=femicide" title="femicide">femicide</a>, <a href="https://publications.waset.org/abstracts/search?q=hate%20language" title=" hate language"> hate language</a>, <a href="https://publications.waset.org/abstracts/search?q=media%20discourse" title=" media discourse"> media discourse</a>, <a href="https://publications.waset.org/abstracts/search?q=sociolinguistics" title=" sociolinguistics"> sociolinguistics</a> </p> <a href="https://publications.waset.org/abstracts/161632/divorced-women-are-like-second-hand-clothes-hate-language-in-media-discourse" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161632.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">85</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4198</span> Robust Noisy Speech Identification Using Frame Classifier Derived Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Punnoose%20A.%20K.">Punnoose A. K.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an approach for identifying noisy speech recording using a multi-layer perception (MLP) trained to predict phonemes from acoustic features. Characteristics of the MLP posteriors are explored for clean speech and noisy speech at the frame level. Appropriate density functions are used to fit the softmax probability of the clean and noisy speech. A function that takes into account the ratio of the softmax probability density of noisy speech to clean speech is formulated. These phoneme independent scoring is weighted using a phoneme-specific weightage to make the scoring more robust. Simple thresholding is used to identify the noisy speech recording from the clean speech recordings. The approach is benchmarked on standard databases, with a focus on precision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=noisy%20speech%20identification" title="noisy speech identification">noisy speech identification</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20pre-processing" title=" speech pre-processing"> speech pre-processing</a>, <a href="https://publications.waset.org/abstracts/search?q=noise%20robustness" title=" noise robustness"> noise robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20engineering" title=" feature engineering"> feature engineering</a> </p> <a href="https://publications.waset.org/abstracts/144694/robust-noisy-speech-identification-using-frame-classifier-derived-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144694.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4197</span> An Analysis of Illocutioary Act in Martin Luther King Jr.'s Propaganda Speech Entitled 'I Have a Dream'</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahgfirah%20Firdaus%20Soberatta">Mahgfirah Firdaus Soberatta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Language cannot be separated from human life. Humans use language to convey ideas, thoughts, and feelings. We can use words for different things for example like asserted, advising, promise, give opinions, hopes, etc. Propaganda is an attempt which seeks to obtain stable behavior to adopt everyone to his everyday life. It also controls the thoughts and attitudes of individuals in social settings permanent. In this research, the writer will discuss about the speech act in a propaganda speech delivered by Martin Luther King Jr. in Washington at Lincoln Memorial on August 28, 1963. 'I Have a Dream' is a public speech delivered by American civil rights activist MLK, he calls from an end to racism in USA. In this research, the writer uses Searle theory to analyze the types of illocutionary speech act that used by Martin Luther King Jr. in his propaganda speech. In this research, the writer uses a qualitative method described in descriptive, because the research wants to describe and explain the types of illocutionary speech acts used by Martin Luther King Jr. in his propaganda speech. The findings indicate that there are five types of speech acts in Martin Luther King Jr. speech. MLK also used direct speech and indirect speech in his propaganda speech. However, direct speech is the dominant speech act that MLK used in his propaganda speech. It is hoped that this research is useful for the readers to enrich their knowledge in a particular field of pragmatic speech acts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20act" title="speech act">speech act</a>, <a href="https://publications.waset.org/abstracts/search?q=propaganda" title=" propaganda"> propaganda</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Luther%20King%20Jr." title=" Martin Luther King Jr."> Martin Luther King Jr.</a>, <a href="https://publications.waset.org/abstracts/search?q=speech" title=" speech"> speech</a> </p> <a href="https://publications.waset.org/abstracts/45649/an-analysis-of-illocutioary-act-in-martin-luther-king-jrs-propaganda-speech-entitled-i-have-a-dream" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45649.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">441</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4196</span> "Groomers, Pedos, and Perverts": Strategies for Queer People and Allies to Combat Discourses of Hate</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Todd%20G.%20Morrison">Todd G. Morrison</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20J.%20Bishop"> C. J. Bishop</a>, <a href="https://publications.waset.org/abstracts/search?q=Melanie%20A.%20Morrison"> Melanie A. Morrison</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An upsurge of hatred directed at sexual- and gender-marginalized persons (SGMPs) has been documented in numerous Western nations. The denial of gender-affirmative care for trans youth; the banning of books containing queer content (no matter how innocuous); the boycotting of products affiliated with queer influencers and with pride celebrations; and the silencing of sexual- and gender-marginalized teachers and academics (and their allies) constitute key ways in which this hatred now manifests itself. The health consequences for SGMPs living in environments characterized by hatred of queer people include elevated rates of depression, anxiety, suicidality, and substance misuse. Given these sequelae, in this paper, the authors outline the challenges that academics experience when adopting an advocacy role. The authors also provide an overview of specific strategies that SGMPs may find helpful when engaging with persons committed to harming queer people. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=queer%20people" title="queer people">queer people</a>, <a href="https://publications.waset.org/abstracts/search?q=resistance" title=" resistance"> resistance</a>, <a href="https://publications.waset.org/abstracts/search?q=minority%20rights" title=" minority rights"> minority rights</a>, <a href="https://publications.waset.org/abstracts/search?q=hate%20speech" title=" hate speech"> hate speech</a> </p> <a href="https://publications.waset.org/abstracts/176569/groomers-pedos-and-perverts-strategies-for-queer-people-and-allies-to-combat-discourses-of-hate" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176569.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">60</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4195</span> Automatic Vowel and Consonant's Target Formant Frequency Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Othmane%20Bouferroum">Othmane Bouferroum</a>, <a href="https://publications.waset.org/abstracts/search?q=Malika%20Boudraa"> Malika Boudraa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a dual exponential model for CV formant transition is derived from locus theory of speech perception. Then, an algorithm for automatic vowel and consonant’s target formant frequency detection is developed and tested on real speech. The results show that vowels and consonants are detected through transitions rather than their small stable portions. Also, vowel reduction is clearly observed in our data. These results are confirmed by the observations made in perceptual experiments in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acoustic%20invariance" title="acoustic invariance">acoustic invariance</a>, <a href="https://publications.waset.org/abstracts/search?q=coarticulation" title=" coarticulation"> coarticulation</a>, <a href="https://publications.waset.org/abstracts/search?q=formant%20transition" title=" formant transition"> formant transition</a>, <a href="https://publications.waset.org/abstracts/search?q=locus%20equation" title=" locus equation"> locus equation</a> </p> <a href="https://publications.waset.org/abstracts/58408/automatic-vowel-and-consonants-target-formant-frequency-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58408.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">271</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4194</span> The Visual Side of Islamophobia: A Social-Semiotic Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carmen%20Aguilera-Carnerero">Carmen Aguilera-Carnerero</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Islamophobia, the unfounded hostility towards Muslims and Islam, has been deeply studied in the last decades from different perspectives ranging from anthropology, sociology, media studies, and linguistics. In the past few years, we have witnessed how the birth of social media has transformed formerly passive audiences into an active group that not only receives and digests information but also creates and comments publicly on any event of their interest. In this way, average citizens now have been entitled with the power of becoming potential opinion leaders. This rise of social media in the last years gave way to a different way of Islamophobia, the so called ‘cyberIslamophobia’. Considerably less attention, however, has been given to the study of islamophobic images that accompany the texts in social media. This paper attempts to analyse a corpus of 300 images of islamophobic nature taken from social media (from Twitter and Facebook) from the years 2014-2017 to see: a) how hate speech is visually constructed, b) how cyberislamophobia is articulated through images and whether there are differences/similarities between the textual and the visual elements, c) the impact of those images in the audience and their reaction to it and d) whether visual cyberislamophobia has undergone any process of permeating popular culture (for example, through memes) and its real impact. To carry out this task, we have used Critical Discourse Analysis as the most suitable theoretical framework that analyses and criticizes the dominant discourses that affect inequality, injustice, and oppression. The analysis of images was studied according to the theoretical framework provided by the visual framing theory and the visual design grammar to conclude that memes are subtle but very powerful tools to spread Islamophobia and foster hate speech under the guise of humour within popular culture. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cyberIslamophobia" title="cyberIslamophobia">cyberIslamophobia</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20grammar" title=" visual grammar"> visual grammar</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a>, <a href="https://publications.waset.org/abstracts/search?q=popular%20culture" title=" popular culture"> popular culture</a> </p> <a href="https://publications.waset.org/abstracts/86980/the-visual-side-of-islamophobia-a-social-semiotic-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86980.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4193</span> A Comparative Study of Natural Language Processing Models for Detecting Obfuscated Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rub%C3%A9n%20Valcarce-%C3%81lvarez">Rubén Valcarce-Álvarez</a>, <a href="https://publications.waset.org/abstracts/search?q=Francisco%20J%C3%A1%C3%B1ez-Martino"> Francisco Jáñez-Martino</a>, <a href="https://publications.waset.org/abstracts/search?q=Roc%C3%ADo%20Alaiz-Rodr%C3%ADguez"> Rocío Alaiz-Rodríguez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cybersecurity challenges, including scams, drug sales, the distribution of child sexual abuse material, fake news, and hate speech on both the surface and deep web, have significantly increased over the past decade. Users who post such content often employ strategies to evade detection by automated filters. Among these tactics, text obfuscation plays an essential role in deceiving detection systems. This approach involves modifying words to make them more difficult for automated systems to interpret while remaining sufficiently readable for human users. In this work, we aim at spotting obfuscated words and the employed techniques, such as leetspeak, word inversion, punctuation changes, and mixed techniques. We benchmark Named Entity Recognition (NER) using models from the BERT family as well as two large language models (LLMs), Llama and Mistral, on XX_NER_WordCamouflage dataset. Our experiments evaluate these models by comparing their precision, recall, F1 scores, and accuracy, both overall and for each individual class. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing%20%28NLP%29" title="natural language processing (NLP)">natural language processing (NLP)</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20obfuscation" title=" text obfuscation"> text obfuscation</a>, <a href="https://publications.waset.org/abstracts/search?q=named%20entity%20recognition%20%28NER%29" title=" named entity recognition (NER)"> named entity recognition (NER)</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/195578/a-comparative-study-of-natural-language-processing-models-for-detecting-obfuscated-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/195578.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Afaan%20Oromo%20hate%20speech%20detection&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Afaan%20Oromo%20hate%20speech%20detection&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Afaan%20Oromo%20hate%20speech%20detection&page=4">4</a></li> <li class="page-item"><a class="page-link" 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