Hate Speech Detection Research Articles

TurkishBERTweet: Fast and reliable large language model for social media analysis

Turkish is one of the most spoken languages in the world; however, it is still among the low-resource languages. Wide us of this language on social media platforms such as Twitter, Instagram, or Tiktok and strategic position of the country in the world politics makes it appealing for the social network researchers and industry. To address this need, we introduce TurkishBERTweet, the first large scale pre-trained language model for Turkish social media built using over 894 million Turkish tweets. The model shares the same architecture as RoBERTa-base model with smaller input length, making TurkishBERTweet lighter than the most used model, called BERTurk, and can have significantly lower inference time. We trained our model using the same approach for RoBERTa model and evaluated on two tasks: Sentiment Classification and Hate Speech Detection. We demonstrate that TurkishBERTweet outperforms the other available alternatives on generalizability and its lower inference time gives significant advantage to process large-scale datasets. We also show custom preprocessors for social media can acquire information from platform specific entities. We also conduct comparison with the commercial solutions like OpenAI and Gemini, and other available Turkish LLMs in terms of cost and performance to demonstrate TurkishBERTweet is scalable and cost-effective.

Semantic Safeguards: Harnessing BERT and Advanced Deep Learning Models Outperforming in the Detection of Hate Speech on Social Networks

Semantic Safeguards: Harnessing BERT and Advanced Deep Learning Models Outperforming in the Detection of Hate Speech on Social Networks

Comparing Pre-trained Language Model for Arabic Hate Speech Detection

Comparing Pre-trained Language Model for Arabic Hate Speech Detection

HA-GCEN: Hyperedge-abundant graph convolutional enhanced network for hate speech detection

The proliferation of online social networks (OSNs) has led to the rampant spread of hate speech. However, traditional detection methods often struggle to effectively detect various forms of hate speech with satisfactory performance, primarily because these methods typically rely on graph-based models that tend to focus on pairwise relationships, thus failing to fully exploit the contextual and user-specific information that could unveil more subtle forms of hate speech. However, establishing a complete graph inevitably introduces considerable computational overhead and redundant information. To overcome these limitations, this study introduces a hyperedge-abundant graph convolutional enhanced network (HA-GCEN) learning framework for hate speech detection (HSD) in OSNs. The proposed hypergraph construction method with a hypergraph convolutional enhanced network primarily consists of three content-, relation-, and semanteme-hyperedge components. These components were designed to enhance context sensitivity, to comprehensively improve the understanding of group relationships and detect latent hate speech. Furthermore, the HA-GCEN was carefully designed to extract high-level correlations from the constructed hypergraph through hypergraph convolutional layers. The efficiency of the proposed method was validated on two benchmark datasets, SemEval2019 task 5 and FUNC, achieving significant improvements over state-of-the-art methods with increases of 5.74% and 2.56% in the F1 score, 5.43% and 1.47% in precision, and 5.98% and 3.47% in recall, respectively. These results attest to HA-GCEN’s advanced feature mining and learning capabilities, demonstrating its potential for more effective HSD within OSNs.

Telugu language hate speech detection using deep learning transformer models: Corpus generation and evaluation

In today's digital era, social media has become a new tool for communication and sharing information, with the availability of high-speed internet it tends to reach the masses much faster. Lack of regulations and ethics have made advancement in the proliferation of abusive language and hate speech has become a growing concern on social media platforms in the form of posts, replies, and comments towards individuals, groups, religions, and communities. However, the process of classification of hate speech manually on online platforms is cumbersome and impractical due to the excessive amount of data being generated. Therefore, it is crucial to automatically filter online content to identify and eliminate hate speech from social media. Widely spoken resource-rich languages like English have driven the research and achieved the desired result due to the accessibility of large corpora, annotated datasets, and tools. Resource-constrained languages are not able to achieve the benefits of advancement due to a lack of data corpus and annotated datasets. India has diverse languages that change with demographics and languages that have limited data availability and semantic differences. Telugu is one of the low-resource Dravidian languages spoken in the southern part of India.In this paper, we present a monolingual Telugu corpus consisting of tweets posted on Twitter annotated with hate and non-hate labels and experiments to provide a comparison of state-of-the-art fine-tuned deep learning models (mBERT, DistilBERT, IndicBERT, NLLB, Muril, RNN+LSTM, XLM-RoBERTa, and Indic-Bart). Through transfer learning and hyperparameter tuning, the models are compared for their effectiveness in classifying hate speech in Telugu text. The fine-tuned mBERT model outperformed all other fine-tuned models achieving an accuracy of 98.2. The authors also propose a deployment model for social media accounts.

Detection of Hate Speech in Marathi Using Language Specific Pre-Processing

Detection of Hate Speech in Marathi Using Language Specific Pre-Processing

Hate Speech Detection by Using Rationales for Judging Sarcasm

The growing number of social media users has impacted the rise in hate comments and posts. While extensive research in hate speech detection attempts to combat this phenomenon by developing new datasets and detection models, reconciling classification accuracy with broader decision-making metrics like plausibility and faithfulness remains challenging. As restrictions on social media tighten to stop the spread of hate and offensive content, users have adapted by finding new approaches, often camouflaged in the form of sarcasm. Therefore, dealing with new trends such as the increased use of emoticons (negative emoticons in positive sentences) and sarcastic comments is necessary. This paper introduces sarcasm-based rationale (emoticons or portions of text that indicate sarcasm) combined with hate/offensive rationale for better detection of hidden hate comments/posts. A dataset was created by labeling texts and selecting rationale based on sarcasm from the existing benchmark hate dataset, HateXplain. The newly formed dataset was then applied in the existing state-of-the-art model. The model’s F1-score increased by 0.01 when using sarcasm rationale with hate/offensive rationale in a newly formed attention proposed in the data’s preprocessing step. Also, with the new data, a significant improvement was observed in explainability metrics such as plausibility and faithfulness.

Analysis of Machine Learning Models for Hate Speech Detection in Online Content

The internet has become a vital platform for people to express their views and beliefs. The users on social media platforms and blogging services are free to publish anything they like. But occasionally, information that targets a particular group of people intending to promote hate or discrimination rises causing trouble in the community. We refer to such material as hate speech. Hate speech has the potential to significantly damage social peace and harmony. Extremism and societal instability have occasionally resulted from hate speech. The several forms of hate speech like racism, sexism, hate speech based on religion, etc.—as well as the approaches put out to combat them are covered. Additionally, we list the problems and provide fixes for issues with hate speech identification on the open internet. Therefore, it is necessary to monitor hate speech on the internet. We analyze relevant research in the field of hate speech detection in this paper. Our proposed system not only identify the Hate Speech on internet but also label them into categories like (Offensive Speech, Hate Speech, fair Speech etc.) The gathered information can be processed to provide Hate speech reports, which will make the internet more user-friendly for anyone using it.

Fine-grained hate speech detection in Arabic using transformer-based models

With the proliferation of social media platforms, characterized by features such as anonymity, user-friendly access, and the facilitation of online community building and discourse, the matter of detecting and monitoring hate speech has emerged as an increasingly formidable challenge for society, individuals, and researchers. Despite the crucial importance of hate speech detection task, the majority of work in this field has been conducted in English, with insufficient focus on other languages, particularly Arabic. Furthermore, most existing studies on Arabic hate speech detection have addressed this task as a binary classification problem, which is unreliable. Therefore, the aim of this study is to provide an enhanced model for detecting fine-grained hate speech in Arabic. To this end, three transformer-based models were evaluated to generate contextualized word embeddings from input sequence. Additionally, these models were combined with a bidirectional gated recurrent unit (BiGRU) layer to further improve the extracted semantic and context features. The experiments were conducted on an Arabic reference dataset provided by the open-source Arabic corpora and processing tools (OSACT-5) shared task. A comparative analysis indicates the efficiency of the proposed model over the baseline and related work models by achieving a macro F1-score of 61.68%.

Towards Optimal NLP Solutions: Analyzing GPT and LLaMA-2 Models Across Model Scale, Dataset Size, and Task Diversity

This study carries out a comprehensive comparison of fine-tuned GPT models (GPT-2, GPT-3, GPT-3.5) and LLaMA-2 models (LLaMA-2 7B, LLaMA-2 13B, LLaMA-2 70B) in text classification, addressing dataset sizes, model scales, and task diversity. Since its inception in 2018, the GPT series has been pivotal in advancing NLP, with each iteration introducing substantial enhancements. Despite its progress, detailed analyses, especially against competitive open-source models like the LLaMA-2 series in text classification, remain scarce. The current study fills this gap by fine-tuning these models across varied datasets, focusing on enhancing task-specific performance in hate speech and offensive language detection, fake news classification, and sentiment analysis. The learning efficacy and efficiency of the GPT and LLaMA-2 models were evaluated, providing a nuanced guide to choosing optimal models for NLP tasks based on architectural benefits and adaptation efficiency with limited data and resources. In particular, even with datasets as small as 1,000 rows per class, the F1 scores for the GPT-3.5 and LLaMA-2 models exceeded 0.9, reaching 0.99 with complete datasets. Additionally, the LLaMA-2 13B and 70B models outperformed GPT-3, demonstrating their superior efficiency and effectiveness in text classification. Both the GPT and LLaMA-2 series showed commendable performance on all three tasks, underscoring their ability to handle a diversity of tasks. Based on the size, performance, and resources required for fine-tuning the model, this study identifies LLaMA-2 13B as the most optimal model for NLP tasks.

Domain adaptation-based method for improving generalization of hate speech detection models

Domain adaptation-based method for improving generalization of hate speech detection models

Automatic hate speech detection in audio using machine learning algorithms

Automatic hate speech detection in audio using machine learning algorithms

Hate Speech Detection Using Deep Learning

Abstract: Hate speech is a form of verbal abuse that targetsindividuals depending on their race, religion, ethnicity, gender, sexual orientation, or other personal characteristics. It can be spoken, written, or displayed, and can be found in a variety of contexts, including online, in person, and in the media. Hate Speech has the potential to have a devastating impact on victims, causing emotional distress, social isolation, and even physical harm. Recognizing hate speech is a challenging task, especially in multilingual contexts. This is because hate speech can be expressed in many ways, and can be difficult todistinguish from other forms of speech. However, Artificial Intelligence (AI) has advanced recently, and have made it possible to develop effective hate speech detection systems. Hate speech detection is a challenging task, especially in multilingual contexts. This survey paper reviews the recent advances in hate speech detection using BERT and CNN models. We explored the various approaches that have been proposed, the challengesfaced, and the paths that research will take in the future

Robustness of models addressing Information Disorder: A comprehensive review and benchmarking study

Machine learning and deep learning models are increasingly susceptible to adversarial attacks, particularly in critical areas like cybersecurity and Information Disorder. This study provides a comprehensive evaluation of model Robustness against such attacks across key tasks well-assessed in Information Disorder literature: Toxic Speech Detection, Sentiment Analysis, Propaganda Detection, and Hate Speech Detection. Rigorous experiments conducted across 13 models and 12 diverse datasets highlight significant vulnerabilities. The methodological framework implements adversarial attacks that strategically manipulates model inputs based on keyword significance, identified using the LIME method, an advanced explainable AI technique. The evaluation measures Robustness primarily through accuracy of the models and attack success rates. The experiments reveal that current models display inconsistent resistance to adversarial manipulations, underscoring an urgent need for developing more sophisticated defensive strategies. The study sheds light on the critical weaknesses in existing models and charts a course for future research to fortify AI resilience against evolving cyber threats. The findings advocate for a paradigm shift in model training and development to prioritize adversarial Robustness, ensuring that AI systems are equipped to handle real-world adversarial scenarios effectively.

A comprehensive review on Arabic offensive language and hate speech detection on social media: methods, challenges and solutions

A comprehensive review on Arabic offensive language and hate speech detection on social media: methods, challenges and solutions

AUTOMATED HATE SPEECH DETECTION USING RECURRENT NEURAL NETWORK

The spread of online entertainment and information sharing has indeed brought significant advances to humanity. However, it has also created various challenges, such as the spread of derogatory content through means such as voicemail. Addressing this emerging problem requires continued consideration, especially in virtual environments, where innovative approaches combining design methods and artificial intelligence are used to identify and filter such content from various data sets. While efforts are being made to combat this problem, there is still a gap in design strategies for that purpose and a comprehensive overview of artificial intelligence algorithms. This includes the need for transparent evaluation criteria and publicly available datasets to benchmark performance and promote collaboration within the research community. Keywords — Deep Learning, Recurrent Neural Network(RNN), Hate Speech Detection.

MetaHate: A Dataset for Unifying Efforts on Hate Speech Detection

MetaHate: A Dataset for Unifying Efforts on Hate Speech Detection

Automatic detection of hate speech in code-mixed Indian languages in twitter social media interaction using DConvBLSTM-MuRIL ensemble method

Automatic detection of hate speech in code-mixed Indian languages in twitter social media interaction using DConvBLSTM-MuRIL ensemble method

StereoHate: Toward identifying stereotypical bias and target group in hate speech detection

Abstract Though social media helps spread knowledge more effectively, it also stimulates the propagation of online abuse and harassment, including hate speech. It is crucial to prevent hate speech since it may have serious adverse effects on both society and individuals. Therefore, it is not only important for models to detect these speeches but to also output explanations of why a given text is toxic. While plenty of research is going on to detect online hate speech in English, there is very little research on low-resource languages like Hindi and the explainability aspect of hate speech. Recent laws like the “right to explanations” of the General Data Protection Regulation have spurred research in developing interpretable models rather than only focusing on performance. Motivated by this, we create the first interpretable benchmark hate speech corpus hate speech explanation (HHES) in the Hindi language, where each hate post has its stereotypical bias and target group category. Providing descriptions of internal stereotypical bias as an explanation of hate posts makes a hate speech detection model more trustworthy. Current work proposes a commonsense-aware unified generative framework, CGenEx, by reframing the multitask problem as a text-to-text generation task. The novelty of this framework is it can solve two different categories of tasks (generation and classification) simultaneously. We establish the efficacy of our proposed model (CGenEx-fuse) on various evaluation metrics over other baselines when applied to the Hindi HHES dataset. Disclaimer The article contains profanity, an inevitable situation for the nature of the work involved. These in no way reflect the opinion of authors.

Deep Learning Approaches for Hate Speech Detection

The main goals of this project are to develop reliable hate speech identification models that can recognize derogatory terminology, prejudiced attitudes, and damaging stereotypes on a variety of internet platforms. To help the models train and generalize efficiently, the study focuses on using big datasets with a variety of content types, including both hate speech and non-hate speech. The findings of this study suggest that machine learning has the potential to mitigate the negative consequences of hate speech by using automated filtering and flagging tools. The study also highlights the need for continued research and development to improve the accuracy, uniformity, and transparency of hate speech detection systems, and ultimately to foster a safer online environment to encourage all people.