Hate Speech Identification Research Articles

Evaluating feature combination strategies for hate-speech detection in Spanish using linguistic features and transformers

The rise of social networks has allowed misogynistic, xenophobic, and homophobic people to spread their hate-speech to intimidate individuals or groups because of their gender, ethnicity or sexual orientation. The consequences of hate-speech are devastating, causing severe depression and even leading people to commit suicide. Hate-speech identification is challenging as the large amount of daily publications makes it impossible to review every comment by hand. Moreover, hate-speech is also spread by hoaxes that requires language and context understanding. With the aim of reducing the number of comments that should be reviewed by experts, or even for the development of autonomous systems, the automatic identification of hate-speech has gained academic relevance. However, the reliability of automatic approaches is still limited specifically in languages other than English, in which some of the state-of-the-art techniques have not been analyzed in detail. In this work, we examine which features are most effective in identifying hate-speech in Spanish and how these features can be combined to develop more accurate systems. In addition, we characterize the language present in each type of hate-speech by means of explainable linguistic features and compare our results with state-of-the-art approaches. Our research indicates that combining linguistic features and transformers by means of knowledge integration outperforms current solutions regarding hate-speech identification in Spanish.

Spam Detection and Hate Speech Identification

The utilization of the term “spam” to explain this sort of invasive blanket-messaging. These are messages sent or comments given to multiple recipients who failed to provoke them. The issues caused by spam are because of the mixture of the unsolicited and bulk aspects; the amount of unwanted messages swamps messaging systems and drowns out the messages that recipients do want. there's no legal definition of hate speech. It can be understood as any quite communication in speech, writing or behavior that attacks or discriminatory language with relevance an individual or a gaggle on the idea of who they're, in other words, supported their religion, ethnicity, nationality, race, color, descent, gender or other identity factor. During this paper, we aim to perform binary classification of spam and hate speech with the assistance of concepts regarding computing, language Processing and Machine Learning. We aim to supply the user with the flexibility to classify the message as fake or real. We have even incorporated the emoji feature further i.e., the user can enter messages with emoji and our model is ready to classify it.

A Novel Machine Learning-based Framework for Detecting Religious Arabic Hatred Speech in Social Networks

Social media platforms generate a huge amount of data every day. However, liberty of speech through these networks could easily help in spreading hatred. Hate speech is a severe concern endangering the cohesion and structure of civil societies. With the increase in hate and sarcasm among the people who contact others over the internet in this era, there is a dire need for utilizing artificial intelligence (AI) technology innovation that would face this problem. The rampant spread of hate can dangerously break society and severely damage marginalized people or groups. Thus, the identification of hate speech is essential and becoming more challenging, where the recognition of hate speech on time is crucial in stopping its dissemination. The capacity of the Arabic morphology and the scarcity of resources for the Arabic language makes the task of distinguishing hate speech even more demanding. For fast identification of Arabic hate speech in social network comments, this work presents a comprehensive framework with eight machine learning (ML) and deep learning (DL) algorithms, namely Gradient Boosting (GB), K-Nearest Neighbor (K-NN), Logistic Regression (LR), Naive Bayes (NB), Passive Aggressive Classifier (PAC), Support Vector Machine (SVM), Ara-BERT, and BERT-AJGT are implemented. Two representation techniques have been used in the proposed framework in order to extract features: a bag of words followed by BERT-based context text representations. Based on the result and discussion part, context text representation techniques with Ara-BERT and BERT-AJGT outperform all other ML models and related work with accuracy equal to 79% for both models.

PDHS: Pattern-Based Deep Hate Speech Detection With Improved Tweet Representation

Automatic hate speech identification in unstructured Twitter is significantly more difficult to analyze, posing a significant challenge. Existing models heavily depend on feature engineering, which increases the time complexity of detecting hate speech. This work aims to classify and detect hate speech using a linguistic pattern-based approach as pre-trained transformer language models. As a result, a novel Pattern-based Deep Hate Speech (PDHS) detection model was proposed to detect the presence of hate speech using a cross-attention encoder with a dual-level attention mechanism. Instead of concatenating the features, our model computes dot product attention for better representation by reducing the irrelevant features. The first level of Attention is extracting aspect terms using predefined parts-of-speech tagging. The second level of Attention is extracting the sentiment polarity to form a pattern. Our proposed model trains the extracted patterns with term frequency, parts-of-speech tag, and Sentiment Scores. The experimental results on Twitter Dataset can learn effective features to enhance the performance with minimum training time and attained 88%F1Score.

Psychosocial Features for Hate Speech Detection in Code-switched Texts

This study examines the problem of hate speech identification in codeswitched text from social media using a natural language processing approach. It explores different features in training nine models and empirically evaluates their predictiveness in identifying hate speech in a ~50k human-annotated dataset. The study espouses a novel approach to handle this challenge by introducing a hierarchical approach that employs Latent Dirichlet Analysis to generate topic models that help build a high-level Psychosocial feature set that we acronym PDC. PDC groups similar meaning words in word families, which is significant in capturing codeswitching during the preprocessing stage for supervised learning models. The high-level PDC features generated are based on a hate speech annotation framework [1] that is largely informed by the duplex theory of hate [2]. Results obtained from frequency-based models using the PDC feature on the dataset comprising of tweets generated during the 2012 and 2017 presidential elections in Kenya indicate an f-score of 83% (precision: 81%, recall: 85%) in identifying hate speech. The study is significant in that it publicly shares a unique codeswitched dataset for hate speech that is valuable for comparative studies. Secondly, it provides a methodology for building a novel PDC feature set to identify nuanced forms of hate speech, camouflaged in codeswitched data, which conventional methods could not adequately identify.

Building and Annotating a Codeswitched Hate Speech Corpora

Presidential campaign periods are a major trigger event for hate speech on social media in almost every country. A systematic review of previous studies indicates inadequate publicly available annotated datasets and hardly any evidence of theoretical underpinning for the annotation schemes used for hate speech identification. This situation stifles the development of empirically useful data for research, especially in supervised machine learning. This paper describes the methodology that was used to develop a multidimensional hate speech framework based on the duplex theory of hate [1] components that include distance, passion, commitment to hate, and hate as a story. Subsequently, an annotation scheme based on the framework was used to annotate a random sample of ~51k tweets from ~400k tweets that were collected during the August and October 2017 presidential campaign period in Kenya. This resulted in a goldstandard codeswitched dataset that could be used for comparative and empirical studies in supervised machine learning. The resulting classifiers trained on this dataset could be used to provide real-time monitoring of hate speech spikes on social media and inform data-driven decision-making by relevant security agencies in government.

Characterization and mechanical properties of offensive language taxonomy and detection techniques

Machine learning is the sub field of Artificial Intelligence which is responsible for developing a system that has the capability to learn and improve from past experience. There is an increasing demand for the offensive language detection because there is a large code-mixed text. Sometimes there is a possibility of code-mixed texts which are written using native scripts. If the system is getting trained on the data which is created using single language then it will leads to failure because of complexity. Using online communities and social media platforms has become increasing now. Recently many of the researchers have been investigating different ways of identifying the abusive content and developing systems to detect its different types like aggression identification, cyber bullying detection, hate speech identification, offensive language identification and toxic comments identification. Developing mechanisms to detect this is a challenging task. In the last few years, machine learning techniques and deep learning concept has been playing a significant role in the development of solutions for offensive language detection. So, considerable efforts have also been devoted to the development of machine learning techniques and deep learning models which is used to identify toxic content in recent years. First, this survey provides offensive language taxonomy and detection approaches. Then, the article focuses on the offensive language identification and toxic comments identification approaches. This article reviews various models used, type of learning model, languages for which the developed model is applied, other methods or approaches that were compared against this model, dataset used, offensive language detection type and performance metrics. The findings from the review are as well summarized and they urge that further effort is required to get improvement in the current state-of-the art approaches. This article also focuses on future work to improve the offensive language classification and toxic comments identification.

How well do hate speech, toxicity, abusive and offensive language classification models generalize across datasets?

A considerable body of research deals with the automatic identification of hate speech and related phenomena. However, cross-dataset model generalization remains a challenge. In this context, we address two still open central questions: (i) to what extent does the generalization depend on the model and the composition and annotation of the training data in terms of different categories?, and (ii) do specific features of the datasets or models influence the generalization potential? To answer (i), we experiment with BERT, ALBERT, fastText, and SVM models trained on nine common public English datasets, whose class (or category) labels are standardized (and thus made comparable), in intra- and cross-dataset setups. The experiments show that indeed the generalization varies from model to model and that some of the categories (e.g., ‘toxic’, ‘abusive’, or ‘offensive’) serve better as cross-dataset training categories than others (e.g., ‘hate speech’). To answer (ii), we use a Random Forest model for assessing the relevance of different model and dataset features during the prediction of the performance of 450 BERT, 450 ALBERT, 450 fastText, and 348 SVM binary abusive language classifiers (1698 in total). We find that in order to generalize well, a model already needs to perform well in an intra-dataset scenario. Furthermore, we find that some other parameters are equally decisive for the success of the generalization, including, e.g., the training and target categories and the percentage of the out-of-domain vocabulary.

Hate Speech as Part of Contemporary Public Discourse and Possible Solutions to Support Public Administration: European Parliament Elections in Croatia

This article deals with hate speech in public discourse and the media, emphasizing the importance of detecting it in a timely manner in order to remove it. This falls within the scope of the tasks of public administration according to the EU’s normative framework because language is one of the main ways that discrimination is enacted. To this end, the empirical research was carried out in two parts. The first part identifies and analyzes unacceptable public behavior (hate speech), defining types of occurrence as opposed to insults and slander, and identifying the advantages and disadvantages of using language technologies for timely identification. The second part of the research detects occurrences of hate speech in Croatian offline media using the example of the 2019 European Parliament elections, drawing attention to a number of methodological obstacles preventing timely identification of hate speech. The results of this investigation contribute to understanding the linguistic-discursive construction of offline and online hate speech in multicultural communities. It is hoped that regulatory authorities will use the results of this research to facilitate implementation of the EU normative framework.

Comparing pre-trained language models for Spanish hate speech detection

Nowadays, due to the great uncontrolled content posted daily on the Web, there has also been a huge increase in the dissemination of hate speech worldwide. Social media, blogs and community forums are examples where people are freely allowed to communicate. However, freedom of expression is not always respectful since offensive or insulting language is sometimes used. Social media companies often rely on users and content moderators to report on this type of content. Nevertheless, due to the large amount of content generated every day on the Web, automatic systems based on Natural Language Processing techniques are required for identifying abusive language online. To date, most of the systems developed to combat this problem are mainly focused on English content, but this issue is a worldwide concern and therefore other languages such as Spanish are involved. In this paper, we address the task of Spanish hate speech identification on social media and provide a deeper understanding of the capabilities of new techniques based on machine learning. In particular, we compare the performance of Deep Learning methods with recently pre-trained language models based on Transfer Learning as well as with traditional machine learning models. Our main contribution is the achievement of promising results in Spanish by applying multilingual and monolingual pre-trained language models such as BERT, XLM and BETO.

Translated vs Non-Translated Method for Multilingual Hate Speech Identification in Twitter

Nowadays social media is often misused to spread hate speech. Spreading hate speech is an act that needs to be handled in a special way because it can undermine or discriminate other people and cause conflict that leading to both material and immaterial losses. There are several challenges in building a hate speech identification system; one of them is identifying hate speech in multilingual scope. In this paper, we adapt and compare two methods in multilingual text classification which are translated (with and without language identification) and non-translated method for multilingual hate speech identification (including Hindi, English, and Indonesian language) using machine learning approach. We use some classification algorithms (classifiers) namely Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest Decision Tree (RFDT) with word n-grams and char n-grams (character n-grams) as feature extraction. Our experiment result shows that the non-translated method gives the best result. However, the use of non-translated method needs to be reconsidered because this method needs more cost for data collection and annotation. Meanwhile, translated without language identification method give a poor result. To address this problem, we combine translated method with monolingual hate speech identification, and the experiment result shows that this approach can increase the multilingual hate speech identification performance compared to translate without language identification. This paper discusses the advantages and disadvantages for all method and the future works to enhance the performance in multilingual hate speech identification.

Ensemble Method for Indonesian Twitter Hate Speech Detection

Due to the massive increase of user-generated web content, in particular on social media networks where anyone can give a statement freely without any limitations, the amount of hateful activities is also increasing. Social media and microblogging web services, such as Twitter, allowing to read and analyze user tweets in near real time. Twitter is a logical source of data for hate speech analysis since users of twitter are more likely to express their emotions of an event by posting some tweet. This analysis can help for early identification of hate speech so it can be prevented to be spread widely. The manual way of classifying out hateful contents in twitter is costly and not scalable. Therefore, the automatic way of hate speech detection is needed to be developed for tweets in Indonesian language. In this study, we used ensemble method for hate speech detection in Indonesian language. We employed five stand-alone classification algorithms, including Naïve Bayes, K-Nearest Neighbours, Maximum Entropy, Random Forest, and Support Vector Machines, and two ensemble methods, hard voting and soft voting, on Twitter hate speech dataset. The experiment results showed that using ensemble method can improve the classification performance. The best result is achieved when using soft voting with F1 measure 79.8% on unbalance dataset and 84.7% on balanced dataset. Although the improvement is not truly remarkable, using ensemble method can reduce the jeopardy of choosing a poor classifier to be used for detecting new tweets as hate speech or not.

In search of hate speech in Lithuanian public discourse: A corpus-assisted analysis of online comments

Abstract The present paper aims to report on the preliminary findings from the initial stages of ongoing research on hate speech in Lithuanian online comments. Comments are marked strongly by such phenomena as flaming and trolling; therefore, in this genre we can expect a high degree of hostility, obscenity, high incidence of insults and aggressive lexis, which can inflict harm to individuals or organizations. The goal of the current research is thus to make an attempt to identify some features of verbal aggression in Lithuanian by applying the principles and instruments of corpus linguistics, which proved to be a useful approach when dealing with such issues as trolling. It is expected that further analysis of those features will help to identify and define formal linguistic criteria that could facilitate identification of hate speech in public discourse. The data has been obtained from the Lithuanian corpus of user-generated comments collected from one major Lithuanian portal, www.delfi.lt. The corpus consists of all the comments posted in the year 2014 and in total includes 17,909 comments, which make up 1,160,109 words. For the initial data analysis, linguistic aspects, such as wordlists, collocations, and formulaic language, were analysed by using the AntConc software. The interpretations of the results are still very tentative, but what the initial findings show is that overt aggression does not feature among the most frequent and most salient features of comments. Aggression is, in our data, indirectly expressed through creative language use, which can mainly be studied through qualitative analysis.