Offensive Language and Hate Speech Detection Based on Transfer Learning

Social media has changed the world and play an important role in people lives. Social media platforms like Twitter, Facebook and YouTube create a new dimension of communication by providing channels to express and exchange ideas freely. Although the evolution brings numerous benefits, the dynamic environment and the allowable of anonymous posts could expose the uglier side of humanity. Irresponsible people would abuse the freedom of speech by aggressively express opinion or idea that incites hatred. This study performs hate speech and offensive language detection. The problem of this task is there is no clear boundary between hate speech and offensive language. In this study, a selected new features set is proposed for detecting hate speech and offensive language. Using Twitter dataset, the experiments are performed by considering the combination of word n-gram and enhanced syntactic n-gram. To reduce the feature set, filter-embedded combining feature selection is used. The experimental results indicate that the combination of word n-gram and enhanced syntactic n-gram with feature selection to classify the data into three classes: hate speech, offensive language or neither could give good performance. The result reaches 91% for accuracy and the averages of precision, recall and F1.

Social media sites facilitate users’ discussions, expression of opinions, sharing of information and news, and promotion of ideas and products, thus rapidly increasing the volume of hate speech and offensive content on online platforms. Consequently, hate speech and offensive content have turned out to be a widespread issue that negatively affects both individuals and society and needs to be controlled through detection and removal. This paper aims to provide a further understanding of the meaning of hate speech and offensive language and to provide a comprehensive discussion of the various techniques to detect hate speech and offensive language. A systematic literature review (SLR) of 90 research papers published between 2018 and 2024 was conducted to discover gaps within the literature. This review revealed challenges and possibilities for further development and improvement of previous findings. The results show that most of these works classified hate speech and offensive language using techniques from machine learning (ML) and deep learning (DL), and the most common performance metrics were Accuracy, Precision, Recall, and F‐measure. The benchmark datasets are also described. Twitter was the most commonly utilized social network for obtaining datasets, while Facebook is sometimes used. Moreover, the findings of this review offer insight into research trends in Arabic hate speech and offensive language, as well as new research directions. The most interesting finding is that until now most Social Media Network Developers have not included autodetection of hate speech or offensive language plugins. Finally, this study presents a guideline for choosing the best strategies and techniques to detect and predict Arabic offensive language and hate speech.

This paper examined the phenomenon of hate speech and foul language on social media platforms in Nigeria, and assessed their moral and legal consequences in the society and to journalism practice. It used both quantitative and qualitative methodology to investigate the phenomenon. In the first place, the paper employed the survey research methodology to sample 384 respondents using questionnaire and focus group discussion as instruments for data collection. Findings from the research indicate that promoting hate speech and foul language on social media have moral and legal consequences in the society and to journalism practice. Findings also show that although, the respondents understand that hate speech and foul language attract legal consequences, they do not know what obligations are created by law against perpetrators of hate speech and foul language in Nigeria. The paper therefore, adopted the qualitative, doctrinal and analytical methodology to discuss the legal consequences and obligations created against perpetrators of hate speech and foul language in Nigeria. The paper concluded based on the findings that hate speech and foul language is prevalent on social media platforms in Nigeria and that there are adequate legal provisions to curb the phenomenon in Nigeria. It recommends among others things that the Nigerian government and NGOs should sponsor monitoring projects like the UMATI in Kenya to better understand the use of hate speech and that monitoring agencies set up under the legal regime should adopt mechanisms to identify and remove hate speech content on social media platforms in Nigeria.

Hate speech detection is an important issue in sentiment analysis and natural language processing. This study aims to improve the effectiveness of hate speech detection in English text using the BERT model, along with modified preprocessing techniques to enhance the F1-score. The dataset, sourced from Kaggle, contains English text with hate speech content. Evaluation results show a significant improvement in the model's accuracy and overall text classification performance. The BERT model achieved 89.11% accuracy on test data, correctly predicting 85 out of 95 samples. While the model excels at classifying offensive text with around 95% accuracy, it struggles to distinguish between hate and offensive text, with some confusion between neither and offensive categories. The classification report shows F1-scores of 0.43 for the hate class, 0.94 for the offensive class, and 0.84 for the neither class, with a weighted average F1-score of 0.89 and a macro average of 0.73. These results indicate that the BERT model delivers solid performance in detecting hate speech, though there is room for improvement, particularly in distinguishing certain classes.

Social networks facilitate communication between people from all over the world. Unfortunately, the excessive use of social networks leads to the rise of antisocial behaviors such as the spread of online offensive language, cyberbullying (CB), and hate speech (HS). Therefore, abusive offensive and hate detection become a crucial part of cyberharassment. Manual detection of cyberharassment is cumbersome, slow, and not even feasible in rapidly growing data. In this study, we addressed the challenges of automatic detection of the offensive tweets in the Arabic language. The main contribution of this study is to design and implement an intelligent prediction system encompassing a two-stage optimization approach to identify and classify the offensive from the non-offensive text. In the first stage, the proposed approach fine-tuned the pre-trained word embedding models by training them for several epochs on the training dataset. The embeddings of the vocabularies in the new dataset are trained and added to the old embeddings. While in the second stage, it employed a hybrid approach of two classifiers, namely XGBoost and SVM, and a genetic algorithm (GA) to mitigate the drawback of the classifiers in finding the optimal hyperparameter values to run the proposed approach. We tested the proposed approach on Arabic Cyberbullying Corpus (ArCybC), which contains tweets collected from four Twitter domains: gaming, sports, news, and celebrities. The ArCybC dataset has four categories: sexual, racial, intelligence, and appearance. The proposed approach produced superior results, in which the SVM algorithm with the Aravec SkipGram word embedding model achieved an accuracy rate of 88.2% and an F1-score rate of 87.8%.

In recent times, the Internet and social media are very well-known and popular among people. Usage of social media is increased exponentially during the last few years globally, and it allows people to engage with one another and share ideas, thoughts, opinions, etc. Every day, massive amounts of data are disseminated at breakneck speed via social media platforms, reaching a massive audience. Furthermore, the ability to write anonymous posts and comments makes expressing and spreading hate speech even easier. To improve the users’ experience, social media sites are attempting to remove hateful remarks. In this research work, the main focus is on developing automated hate speech and offensive language detection models. Started with traditional hate speech and offensive language identification approaches, than reaches to advanced hate speech recognition methods for social media. This brings a need for integrated datasets and the hate speech prediction method. This paper describes the study on hate speech and offensive content identification in English language by using the various approaches based on machine learning algorithms (Support vector machine, decision tree, and so on) and NLP, along with the features used for the classification problem. The models were tested on the HASOC (2021) datasets and concluded that the ensemble model perform better than other algorithms with the test dataset for different tasks. Results and analysis part of this paper offers researchers a comprehensive picture of approaches.

This paper introduces a streamlined taxonomy for categorizing offensive language in Hebrew, addressing a gap in the literature that has, until now, largely focused on Indo-European languages. Our taxonomy divides offensive language into seven levels (six explicit and one implicit level). We based our work on the simplified offensive language (SOL) taxonomy introduced in (Lewandowska-Tomaszczyk et al. 2021a) hoping that our adjustment of SOL to the Hebrew language will be capable of reflecting the unique linguistic and cultural nuances of Hebrew. The study involves both linguistic and cultural analysis beyond Natural Language Processing (NLP). We employed manual linguistic analysis to understand the nuances of offensive language in Hebrew.An accompanying dataset, gathered on Twitter and manually curated by human annotators, is described in detail. This dataset was constructed to both validate the taxonomy and serve as a foundation for future research on offensive language detection and analysis in Hebrew. Preliminary analysis of the dataset reveals intriguing patterns and distributions, underscoring the complexity and specificity of offensive expressions in the Hebrew language.The aim of our work is to capture the complexity and specificity of offensive expressions in Hebrew beyond what automated NLP methods alone can provide. Our findings highlight the significance of considering linguistic and cultural variations when researching and correcting abusive language online. We believe that our streamlined taxonomy and associated dataset will be crucial in improving research in Hebrew language sociocultural studies, natural language processing, and offensive language detection. Our study also makes a substantial contribution to the study of low-resource languages and can be used as a model for future research on other languages.

Machine translation (MT) is widely used to translate content on social media platforms aiming to improve accessibility. A great part of the content circulated on social media is user-generated and often contains non-standard spelling, hashtags, and emojis that pose challenges to MT systems. This leads to many mistranslated instances that are presented to users of these platforms, hindering their understanding of content written in other languages. In this paper, we investigate the impact of MT on offensive language identification. We pose that MT and potential mistranslations have an important and mostly under-explored impact on social media tasks such as sentiment analysis and offensive language identification. We create MT-Offense, a novel dataset containing English originals and translations in Arabic, Hindi, Marathi, Sinhala, and Spanish produced by multiple open-access Neural Machine Translation systems. We evaluate the performance of various offensive language models on both original and MT content in different training and test set combinations. We report the F1 scores of the models. Our results show that (1) offensive language identification models perform better on original data than on MT data, and (2) the use of MT data in training helps models better identify offensive language in MT content compared to models trained exclusively on original data.

Distinguishing hate speech from non-hate offensive language is challenging, as hate speech not always includes offensive slurs and offensive language not always express hate. Here, four deep learners based on the Bidirectional Encoder Representations from Transformers (BERT), with either general or domain-specific language models, were tested against two datasets containing tweets labelled as either ‘Hateful’, ‘Normal’ or ‘Offensive’. The results indicate that the attention-based models profoundly confuse hate speech with offensive and normal language. However, the pre-trained models outperform state-of-the-art results in terms of accurately predicting the hateful instances.

Since cyberbullying impacts both individual victims and entire society, research on abusive language and its detection has attracted attention in recent years. Because social media sites like Facebook, Instagram, Twitter, and others are so widely accessible, hate speech, bullying, sexism, racism, aggressive material, harassment, poisonous comments, and other types of abuse have all substantially increased. Due to the critical requirement to detect, regulate, and limit the spread of harmful content on social networking sites, we conducted this study to automate the detection of offensive language or cyberbullying. We created a new Arabic balanced data set to be used in the offensive detection process because having a balanced data set for a model would result in improved accuracy models. Recently, the performance of single classifiers has been improved using ensemble machine learning. The purpose of this study is to examine the effectiveness of several single and ensemble machine learning algorithms in identifying Arabic text that contains foul language and cyberbullying. Applying them to three Arabic datasets, we have selected three machine learning classifiers and three ensemble models for this aim. Two of them are offensive datasets that are readily accessible in the public, while the third one was created. The results showed that the single learner machine learning strategy is inferior to the ensemble machine learning methodology. Voting performs is the best performing trained ensemble machine learning classifier, outperforming the best single learner classifier (65.1%, 76.2%, and 98%) for the same datasets with accuracy scores of (71.1%, 76.7%, and 98.5%) for each of the three datasets used. Finally, we improve the voting technique’s performance through hyperparameter tuning on the Arabic cyberbullying data set.

The task of detecting online textual hate speech focuses on various objectives with one of the most important being distinguishing between hate speech and generic offensive language. This distinction is very blurry since the two classes are strongly overlapped. Using only keywords for identification falls short because of this overlap, hence the context in which each textual instance exists in, needs to be extracted for increased effectiveness. To achieve this, we investigate the use of embeddings learned with syntactic dependency context (words that have a grammatical relationship with the target word) as opposed to linear context (words that precede and succeed the target word as determined by the window size) in various forms as features for the classification task on a multi-class dataset. Our results and analysis show that for the downstream task of hate speech detection, specifically for distinguishing between hateful and offensive language, the dependency-based embedding has a very comparable performance with its linear-based counterpart even outperforming it in some settings. Moreover, compared to the state-of-the-art (BERT), it demonstrates competitive performance especially when in an ensemble with linear-based embedding. Also, we observed that for a specialised task such as hate speech detection, a domain-specific embedding is probably more important than a large out-of-domain embedding with a larger vocabulary size.

The rise of social media has led to an increase in toxic language, hate speech, and offensive content. While extensive research exists for widely spoken languages like English, Slovak remains underrepresented due to the lack of high-quality datasets. This gap limits the development of effective models for toxicity detection and sentiment analysis in Slovak. To address this, we introduce three new annotated Slovak datasets focused on toxic language, offensive language, hate speech detection, and sentiment analysis. These native datasets provide a more reliable foundation for automated moderation compared to machine-translated alternatives. Our research also highlights the real-world impact of online toxicity, including social polarization and psychological distress, emphasizing the need for proactive detection systems on social media platforms. This paper reviews existing Slovak datasets, presents our newly developed resources, and provides a comparative analysis. Finally, we outline key contributions and suggest future directions for improving toxic language detection in Slovak.

Social media provides an inexpensive way to communicate with millions of users. These websites provide a platform to propagate your ideas to millions of people. The power of social media is often misused. Freedom of expressing opinions and beliefs on social media websites has been resulted in spread of hate speech. It has become a major challenge to check the dissemination of hate speech on websites of social media. We used Naive Bayes algorithm to classify text tweets into three classes i.e., tweets containing hate speech, tweets containing offensive language and tweets containing neither hate speech nor offensive language. It is a supervised machine learning based algorithm. We performed experiments with three variations of Naive Bayes i.e., Bernoulli's, multinomial and Gaussian Naive Bayes classifier. Bernoulli’s and Multinomial Naïve Bayes delivered the best accuracy values.

Hate speech and abusive language are easily found in written communications in social media like Twitter. They often cause a dispute between parties, the victims, and the first who write the tweet. However, it is also difficult to distinguish whether a tweet contains hate speech and/or abusive language for those who take sides. This research aims to develop a method to classify the tweets into abusive and/or contain hate speech classes. If hate speech is detected, then the system will measure the hardness level of hatred. The dataset includes 13,126 real tweets data. Word embeddings are used for featuring text input. For the tweets classification, we use a Decision Tree algorithm. Some engineering of features and parameters tuning has improved the classification of the three classes: hate speech class, abusive words, and hate speech level. The lexicon feature in the Decision Tree classification produces the highest accuracy for detecting the three classes rather than engineering special features and textual features. The average accuracy of the three classes increased from 69.77 % to 70.48 % for the training-testing composition of 90:10, and another 69.35 % to 69.54 % for 80:20 respectively.

The paper introduces a very current topic in the field of natural language processing oriented to the automatic detection of hate speech and offensive language performed in the Slovak language. In this work, we describe the creation and processing database of short texts composed of posts and comments written in Slovak and published on social media. The proposed approach is based on sentiment analysis and implementing a tool for detecting hate speech using a convolutional neural network with elements of a recursive neural network, applied to a created database of comments. We achieved 61.32% detection accuracy only on a small set of training data balanced in the number of positive, neutral, and negative sentiments.