A survey on multi-lingual offensive language detection

Motivation and impact

Based on the latest research, there are various compelling reasons for investigating and scrutinizing hateful content on social media platforms, with the ultimate goal of fostering a secure, considerate, and diverse online community. Such motivations include (i) detection and the mitigation of harmful behavioural patterns by analysing the trends of hateful content (Al-Hassan & Al-Dossari, 2019), (ii) preserving a safe online community by analysing and diminishing offensive, insincere and unsafe content from the social media platforms (d’Sa, Illina & Fohr, 2020), (iii) analyzing and safeguarding marginalized user communities that are targeted by hate speech and abusive content due to their race, ethnicity, gender, sexuality, religion, or other identifiable traits (Al-Hassan & Al-Dossari, 2019), and most importantly (iv) ensuring adherence to legal and regulatory frameworks in multiple countries that prohibit the propagation of hate speech and other forms of harmful content (Bakalis & Hornle, 2021). As a result, analyzing hateful and abusive content can contribute to a more positive user experience on social media platforms by providing a safer and more respectful online community, as well as support efforts to combat online hate speech and provide insight into real user behaviours.

Definition of hate speech and offensive language

One primary challenge in recognizing content as offensive or not is that, up to date there is no widely acknowledged unique definition of offensive and hate speech. This is primarily due to the ambiguous, subjective, and personal interpretations of whether a speech is “hatred” or expresses “offensive” (Fortuna, Soler & Wanner, 2020). Unspecified definitions can result in increased subjectivity in annotations which then facilitates generating a biased model (Davidson, Bhattacharya & Weber, 2019).

As shown in Fig. 1, due to the shared characteristics and effects, hate speech and aggressive content can be categorized as types of offensive content (Poletto et al., 2021). Offensive language can encompass a broader range of content that may cause discomfort or offense, while hate speech is specifically targeted at marginalized communities or individuals, aiming to spread prejudice, hostility, or discrimination, and subject to legal and social consequences. Researchers primarily focus on analyzing hate speech rather than general offensive content detection due to its severity of harm and the significant legal and policy implications it carries in many countries. Compared with the law-enforcing based hate-speech definitions such as the descriptions provided by, the Europe union commission (Wigand & Voin, 2017), the European Union Agency for Fundamental Rights (https://fra.europa.eu) and the UN Human Rights Office (https://www.ohchr.org), social media platforms should consider a broader definition for the hatred and offensive content.

Generally, hate speech in the context of social media commonly refers to any content that discriminates against or attacks individuals or groups based on several characteristics such as race, ethnicity, religion, sexual orientation, gender, or other identifiable features. This definition often aligns with community policies and content moderation guidelines of various social media platforms, drawing from assigned legal frameworks, academic research, and societal norms concerning discriminative behavior and hateful content online.

Multilingual vs. cross-lingual

In NLP, both multi-lingual and cross-lingual approaches deal with processing and understanding languages, which usually are different from English. However, there are differences in the way of using these terms:

Multi-lingual NLP: Multi-lingual NLP refers to the development and application of NLP models and techniques that can handle multiple languages simultaneously. This involves creating models that can process and understand different languages without the need for language-specific models. Multi-lingual models are trained on data from multiple languages and learn to capture shared linguistic patterns and representations across languages. These models can perform tasks such as text classification, named entity recognition, or machine translation across multiple languages. They are designed to provide a generalized solution for various languages, but they may not achieve the same level of performance as language-specific models.

Cross-lingual NLP: Cross-lingual NLP focuses on enabling communication and understanding between different languages. It involves developing techniques to transfer knowledge, resources, or models from a resource-rich language (often referred to as a source language) to resource-poor languages (target languages). The goal is to leverage the knowledge and resources available for one language to enhance NLP tasks in another language. Cross-lingual approaches can include tasks such as cross-lingual document classification, cross-lingual information retrieval, or cross-lingual word embeddings. Techniques used in cross-lingual NLP include machine translation, word alignment, parallel corpora, and bilingual dictionaries (Pikuliak, Simko & Bieliková, 2021).

Structure of this survey

Our survey incorporates an extensive exploration in existing approaches and datasets of multilingual hate speech and offensive language detection. The structure of our review is as follows: In ‘Background on Multilingual Hate Speech Phenomena’, we provide an examination of the previous surveys on hate speech detection. We focus on the studies on multilingual aspect of this task, where we carefully study their deficiencies in order to fill this gap in our survey. Next, “Approaches on Multilingual Hate Speech Detection” presents a thorough review of the existing approaches used for multilingual and cross-lingual offensive language detection. We give interpretations from our findings and summarize these studies in a comprehensive table. Following that, we examined the available resources in this field, starting in “Datasets on Multilingual Hate Speech Detection” with a detailed review of available multilingual datasets by a deep analysis of these corpora introducing their languages and main topics. Next, in “Resources for Multilingual Hate Speech Detection” we illustrate the different international collaborative projects provided for multilingual hate speech detection. We also present community challenges and competitions that focused this task. We, then, introduce a variety of publicly available source codes and APIs. Lastly, in “Challenges and limitations”, we present the challenges encountered in the field of multilingual hate speech detection. We also emphasize the limitations, as well as a set of future directions that, we believe, will help to overcome these obstacles and to progress further in the task of multilingual hate speech detection.

Who can benefit from this survey? This survey aims to serve as a pivotal roadmap for both the research community and business sector, delving into the current landscape and future direction of multilingual offensive language detection. For researchers in the academic field, it serves as a comprehensive synthesis, describing the ongoing advancements, evolving approaches, and available resources, including datasets, within this domain. It carefully discusses challenges, and limitations, and proposes promising directions for future exploration. In the business sector, this survey offers valuable understanding, serving as a guide for decision-makers. It helps in the assessment of the implementation of multilingual offensive language detection moderators for online textual content, particularly on social media platforms.

In order to give an in depth understanding of the state of the multilingual offensive language detection field, we provide an extensive review in this article. We carefully explore a number of aspects: mainly current approaches and a wide range of datasets that include low-resource languages, and also, available resources like collaborative projects and tools, challenges faced and possible recommendations for future development. As a result, academics as well as professionals can benefit considerably from our survey. This study gives significant recommendations and defines solutions for future directions, especially proposing getting benefit from the recently released LLMs and generative pre-trained models, therefore, contributing to the compilation of existing knowledge and supporting ongoing progress in reducing hate speech across languages and groups.

Previous surveys

Several previous studies have given comprehensive analysis of hate speech detection in different aspects, focusing on presenting to the community the related data, approaches and multilingual methods, and existing products. In this section, we aim to explore these surveys paying more attention to the multilingual aspect of the field. As displayed in Table 1, some of the existing survey studies focus on presenting definitions and notions related to the domain, as well as an examination of current approaches like in Poletto et al. (2021), Pamungkas, Basile & Patti (2021b), Chhabra & Vishwakarma (2023). Meanwhile, other surveys focus on introducing the available sources of the topic, such as data and available source-code (Vidgen & Derczynski, 2020; Poletto et al., 2021; Schmidt & Wiegand, 2017).

Table 1 illustrates previous literature reviews in the field of hate speech detection in multilingual and in general settings. We have carefully studied and analyzed these surveys, which enabled us to construct a comprehensive narrative review in order to cover the deficiencies in these surveys. More specifically, we aim to give an overview of the existing multilingual and cross-lingual approaches, similar to Schmidt & Wiegand (2017), Yin & Zubiaga (2021), Fortuna & Nunes (2018) (on English data), to Pamungkas, Basile & Patti (2021b) focusing on cross-lingual methods, as well as Chhabra & Vishwakarma (2023), where they basically displayed monolingual approaches in some specific languages. Adding to that, some previous surveys have presented existing corpora in the domain, in some specific languages as in Poletto et al. (2021), Jahan & Oussalah (2021), Chhabra & Vishwakarma (2023), and more widely in Pamungkas, Basile & Patti (2021b) presenting datasets in 18 different languages, and in Vidgen & Derczynski (2020), providing an open source website to 63 datasets in multiple languages (https://hatespeechdata.com/). One metric aspect, to take into consideration to define our survey type is based on the forms of literature reviews: Narrative and Systematic reviews. In fact, when writing a narrative review from the literature findings, authors are utilizing a more subjective and qualitative research technique. However, they employ methodical/systematic research methodology using a more quantitative and objective approach when working on systematic reviews. These reviews are considered as a link between practice or policy-making and research. Narrative reviews are also noticed to have this linking function; however, they are usually utilized in order to handle more general and complex subjects (Hammersley, 2001).

Surveys on hate speech—from general perspective

Several surveys have studied hate speech and offensive language detection. In 2017, Schmidt & Wiegand (2017) summarized the primary NLP aspects of automated hate speech detection: illustrating the various types of feature representation and, the existing supervised and semi-supervised techniques and their limitations. The authors, proposed as future direction, the urge to analyze hate speech detection from a multilingual viewpoint. Adding to that, Yin & Zubiaga (2021) described the most commonly implemented approaches in the field, such as dictionaries, bag-of-words, N-grams, among others. Moreover, Fortuna & Nunes (2018) presented a systematic overview. They recap the various approaches and resources available in this domain. They also critically examined valuable resources like datasets, illustrating some of the existings in different languages (English, Dutch and German). Another systematic survey has been conducted in 2021 (then updated in 2023), where Jahan & Oussalah (2021) addressed thorough aspects including the language used, the pipeline processing, and the techniques used, focusing primarily on deep learning approaches. They also presented many sources in some languages (data and GitHub projects).

Surveys on multilingual hate speech—multilingual perspective

Multilinguality is getting more popular in the task of hate speech detection and there are recently some surveys on this aspect, covering existing approaches and resources. These studies pay more attention to the considerable variations in the existing studies that aim to cover other languages (other than English), as well as more concepts related to offensive language (Racism, sexism, among others). This is required to build more generalized approaches. In 2021, Poletto et al. (2021) comprehensively studied the annotated datasets of hate speech, taking into account the creation process, subject case, language coverage, and other pertinent factors about the existing lexica and benchmark datasets in different languages. Moreover, few overviews have been conducted on the topic of multilingual offensive language detection, where the survey of Pamungkas, Basile & Patti (2021b) presented the approaches and the available corpora employed in cross-domain and cross-language techniques. Moreover, the survey of Chhabra & Vishwakarma (2023) provided a comprehensive review of hate speech definitions, exploring the essential textual analysis procedures used. The survey also described the advantages and disadvantages of multimodal and cross-lingual approaches.

Existing gaps in the previous surveys

Although several review studies have been written on the task of hate speech detection, but still there are several aspects that are not covered in those studies especially when it comes to presenting an in-depth comprehension of the multilingual aspect of this area. Our study is a narrative review that seeks to close this gap by exploring larger number of characteristics of multilingual offensive language detection: from existing approaches and available datasets to related collaborative projects and resource products that include community challenges, source codes, and APIs. Adding to that, this study will also analyze the associated challenges and limitations in the field. Furthermore, we aim to get into future research directions providing a roadmap for the research progress in multilingual hate speech detection.

Existing approaches

The value of studying multilingual offensive language detection has earned attention in recent years. This increasing interest is a consequence of the linguistic variety within social media platforms. The availability of multilingual datasets, especially from social media platforms that are used worldwide, has made it possible to develop algorithms to detect this content in various languages. Some research studies used to focus on creating monolingual models, working on languages other than English, but they have later evolved into cross-lingual and multilingual approaches, utilizing rich resource languages in order to detect the offensive language in low-resource ones totally unseen using zero-shot learning, or, using few-shot learning (Goodfellow, Bengio & Courville, 2016).

This section aims to provide an overview of existing approaches for this task. To that end, we organize the existing studies into eight distinct groups (as shown in the first column of Table 2): Traditional Machine Learning (where we found logistic regression (LR) models), deep neural networks (DNN), transfer learning (TL), machine translation (MT), ensemble learning (EL), meta learning (Meta-L), multitask learning (Multitask-L), and unsupervised learning (UL). By categorizing these approaches, we make it possible to analyze them carefully and present an overview of the evolution of methods used to tackle multilingual and cross-lingual offensive language detection, as described in the next subsection ‘Analysis of the existing approaches’.

As for languages presentation, we will use ISO 639-2 codes (https://www.loc.gov/standards/iso639-2/php/code_list.php).

Methodology of research: Our research of these existing approaches is based on information from earlier pertinent surveys (mentioned in the previous section). We also used specific keywords, such as “multilingual/cross-lingual offensive language detection”, “multilingual/cross-lingual hate speech detection”, “multilingual/cross-lingual abusive language detection”, among others, to find studies that were published in IEEE Xplore, ACM Digital Library, Google Scholar, among others. By focusing on publications that were released in 2019 and beyond (until July 2023), we ensured that our survey included the most cutting-edge approaches for multilingual and cross-lingual offensive language detection. Moreover, we won’t cover monolingual approahces in our study about existing approaches since our focus is basically directed to multilingual approaches, however, we mention some of the most relevant ones we found in ‘Other Technologies’ subsection. A summary of the identified approaches are presented in Table 2 and each of the eight techniques are detailed in the following part.

Analysis of the existing approaches

Table 2 provides an overview of the previous research studies, we collected during our research analysis. It illustrate the different approaches implemented in multilingual and cross-lingual offensive language detection, we can see how these approaches have evolved over the years as well as the complexity, and the diversity of the languages studied in the field.

Around 2020, several approaches like Vashistha & Zubiaga (2021) and Aluru et al. (2020) concentrated on word embeddings for feature representation and they managed to use small-sized datasets in different languages such as English, Hindi, and some code-mixed languages. There is a progression towards more complex deep learning architectures like CNN-LSTM in Vashistha & Zubiaga (2021), and diverse language sets in Aluru et al. (2020), that were using more low resource languages like Arabic, Indonesian, Italian, German, Portuguese, Polish, French, and Spanish. Later on, 2021 witnessed the use of more complex approaches in research in this domain, such as the use of bi-directional pre-trained transformers (like mBERT) and the use of XLM-R, which displays more direction toward implementing pre-trained language models (especially the ones trained on significant volumes of multilingual data). More recently, since 2022, we observe more use of several sophisticated approaches, such as multitask learning, meta-learning, and ensemble learning. In particular, ensemble learning and meta-learning are gaining more attention, combining the predictions of multiple models or building and training a meta-learner able to adapt to new tasks with a few training examples, which could be mainly practical to use in the low-resource language data. Starting with the feature extraction techniques, word embeddings were among the widely used techniques, especially with the frequent implementation of several transformers like BERT and XLM-R, along with the use of other methods such as character-level representation and FastText but less often.

English language was the mostly learned compared to other languages, along with some other European languages such as Spanish, German, and French. Other languages were very little studied such as Japanese (Ja) and Norwegian (No), which reveals a serious challenge in analyzing offensive language and hate speech in these languages along with other non accessible ones (languages used by small communities that don’t have ready data to work on). Therefore, we urge the need to conduct more research in order to be able to create performant services and tools for the detection of such content in these low-resource languages.

As shown in Table 2, the period of publication considered for the existing studies, ranges from 2019 and till the time of this study (July 2023). This implies that researching in multilingual text classification task is still relatively new area of study, with a lot of ongoing research. Moreover, for the multilingual approaches, the analyses cover a wide scope of languages, such as English, Bengali, Arabic, Danish, Croatian, French and more. As for the cross-lingual approaches, the focus was mostly on fewer languages, typically, with English being the most studied as a source language (used for training), then some other rich-resource languages like Spanish, Italian, German, among others. Which indicates that multilingual text classification still requires much research, especially in low-resource languages. Lastly, we discovered an increasing tendency toward employing pretrained large language models (LLMs) like BERT and XLM-R and their variations (mBERT, XLM-T, among others). These LLMs prove their ability to remarkably enhance the performance of multilingual hate speech detection tasks.

Collaborative international projects

A summary of the projects we are mentioning in this section is presented briefly in Table 5.

European project: Several European projects have been undertaken to address the detection and mitigation of online hate speech. The DARE project, https://cordis.europa.eu/project/id/725349 (2017–2021), funded under the EU Horizon 2020 Framework Programme, aims to develop technologies and methodologies for combating hate speech, including radicalization and extremist content. It involves partners from 13 countries, including Belgium, Croatia, Germany, Greece, France, Malta, Poland, Russia, Turkey, Tunisia, The Netherlands, and the UK. Another project, Hatemeter (2018–2020), focuses on monitoring, analyzing, and tackling anti-Muslim hatred online at the EU level. It takes a multidimensional approach to identify red flags of hate speech, understand patterns of Islamophobia, develop tactical and strategic responses, and produce counter-narratives. The project partners include Italy, France, and the UK. In the realm of cybersecurity, the PANACEA (2019–2022) project aims to improve cybersecurity and privacy/data protection in hospital and health infrastructures. It provides toolkits to enhance security and data protection for various stakeholders in the healthcare sector, including hospitals, software/system developers, medical device manufacturers, and digital service providers. The project involves partners from Italy, UK, Greece, France, Belgium, Netherlands, Germany, and Ireland. Moreover, the sCAN project (2018–2020), coordinated by LICRA (International League against Racism and Antisemitism), focuses on gathering expertise, tools, methodology, and knowledge to identify, analyze, report, and counteract online hate speech. It involves partners from France, Germany, Italy, Belgium, Czech Republic, Austria, Slovenia, Croatia, and Latvia.

While explicit information about language support may not be mentioned for some of the mentioned projects above, it is reasonable to assume that their deliverables and solutions would likely focus on the languages spoken in the countries of their partner organizations. Given the diverse range of partner countries involved in these projects, it is possible that they would consider the languages relevant to those countries. This would imply that their solutions and products could potentially support languages beyond English, depending on the specific project’s objectives and target regions.

Other projects are explicitly mentioning their focus on various languages. Among them, we found Detect Then Act (DTCT) (2019–2021), a European collaboration that aims to monitor and tackle online hate speech. It utilizes Explainable AI to assist users in deflating toxic discussions. The project reports illegal hate speech cases in accordance with the EU’s Code of Conduct and local legislation. Moreover, it provides master training for hate speech detection in multiple languages, including English, French, Dutch, German, and Hungarian. The project partners involved are from Belgium, Germany, the UK, and the Netherlands. The project Stand By Me (2020) was created to moderate online violence against women in Europe. The project aims to help addressing this issue by enhancing individuals’ awareness and capability to recognize such content. The project utilizes a diverse approach, including a combined learning program and educational resources. In addition, The European Observatory of Online Hate (EOOH) project (2021) was released by Textgain (in the lead), as a Multi-platform for monitoring hate speech in more than 20 social media platforms and covering 24 different languages. This project has made significant progress in understanding the complexities of online hate speech. This incorporates comprehending its various forms, the relationships among corresponding users, and the strategies involving disinformation. Lastly, the MANDOLA project (2017) aims to improve our understanding of online hate speech prevalence and empower ordinary citizens to report it. It utilizes big-data approaches to monitor this content, provide policymakers with actionable information, and transfer best practices among Member States. The project partners include Greece, Ireland, France, Spain, Bulgaria, and Cyprus. While the specific languages supported by the project may not be explicitly mentioned in the provided information, the project’s objectives display that it seeks to monitor and analyze hate-related speech across multiple languages, including languages other than English.

On the other hand, several “industrial projects” aim to tackle the issue of hate speech and polarization in society. One such project is PRO2HATERS: PROactive PROfiling of HATE speech spreadeRs (2017) by Symanto, (in Germany). PRO2HATERS focuses on addressing hate speech and polarization, with a particular emphasis on languages beyond English, such as German and Spanish, and their dialects. The project proposes language resources, network analysis, methods, and tools as key components to combat hate speech. It envisions various application scenarios, including cyber-security, where government agencies can detect and counter hate speech, and social media companies can automate hate speech detection. Another project in this domain is Identrics (2023, Bulgaria). Identrics offers a cutting-edge Hate Speech Detection service that helps eliminate hate speech in the comments sections of websites. Leveraging machine learning models, this project continuously learns to identify and flag hate speech, providing alerts to potential occurrences. By utilizing Identrics’ service, platforms can foster meaningful conversations without the concern of offensive or abusive language spreading throughout their community.

Non-European projects: Fighting hate speech is undoubtedly an international effort that cuts across national boundaries. The multifaceted projects use a wide range of tactics, including constructing machine learning models, crafting legislation, starting public education programs, and starting awareness campaigns. Despite the complexity of the problem, these international initiatives show a shared dedication to creating safer and more inclusive online and physical settings. The Online Hate Index (OHI), a tool employing machine learning to identify and quantify hate speech targeting marginalized groups on digital platforms in English, it was created by the Anti-Defamation League ADLs’s Center for Technology and Society (CTS). The program is made to identify linguistic trends and continuously advance in antisemitic content detection, giving an objective way to gauge the incidence of hate speech and assess the success of digital businesses’ anti-hate measures. This project is made by the USA and released in 2018 (the project was developed domestically but has international implications to be used worldwide). Moreover, ProPublica’s Documenting Hate project, a well-known American endeavor that was started in 2017, aimed to compile an extensive database of hate crimes committed throughout the nation. The project teamed up with newsrooms, educational institutions, and independent journalists to assist in reporting and documenting instances of bias and hatred in response to the dearth of accurate statistics on hate crimes.

Available products

We examine, in this part, various facets of the resources available in hate speech detection, more specifically in multilingual hate speech detection. We’ll start by highlighting the community challenges and competitions. We will next move on to talking about the accessible open-source codes. These represent concrete instruments that are open to learning and discovering the developed solutions. In order to wrap off this analysis, we will look at the APIs, including multilingual APIs.

Community Challenges & Datasets Provided: Detecting multilingual hate speech and offensive language is a paramount challenge, especially for social media platforms where a myriad of cultures and languages interact daily. This complexity arises due to the nuanced, context-specific, and often indirect nature of hate speech and offensive language. Over the years, several competitions and hackathons have been aimed at addressing this issue. In 2018, the TRAC-1: Aggression Identification (Kumar et al., 2018a), the first Workshop on Trolling, Aggression, and Cyberbullying, honed in on identifying aggression in social media posts in English and Hindi, both in Roman and Devanagari scripts. Then, 2019 saw many important challenges, such as: the SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (Basile et al., 2019). It centered around hate speech detection in a multilingual setting, focusing on English and Spanish tweets. In 2020, several key events occurred. Kaggle’s Jigsaw Multilingual Toxic Comment Classification competition encouraged participants to build models that identify rudeness, disrespect, or any conversation-derailing toxicity in multilingual online discussions using English-only training data. The same year, the Hate Speech Detection (HASOC) Competition, hosted by FIRE (Forum for Information Retrieval Evaluation), focused on identifying hate speech and offensive content in English, German, and Hindi. Additionally, the TRAC 2020, the second Workshop on Trolling, Aggression, and Cyberbullying, provided two shared tasks, one on Aggression Identification and another on Misogynistic Aggression Identification in Bangla (in both Roman and Bangla script), Hindi (in both Roman and Devanagari script) and English. Adding to that, in 2021, two significant challenges emerged: the Kaggle IIIT-D Multilingual Abusive Comment Identification focused on identifying abusive comments across various Indic languages, and the PAN shared task of Profiling Hate Speech Spreaders on Twitter 2021 involved profiling hate speech spreaders on Twitter in English and Spanish. Apart from these targeted events, numerous other hackathons and competitions contribute indirectly to the field of multilingual hate speech detection. Events centered around cross-lingual or multilingual text classification, sentiment analysis, or broad NLP problems offer valuable platforms for devising innovative solutions to detect hate speech and offensive language across languages.

Datasets provided of the community challenges: Overall, the above-mentioned challenges are presented in Table 6, they were instrumental in providing a broad spectrum of datasets that cater to different languages and aspects of hate speech detection. They are sources of rich and diverse information, accessible to researchers worldwide. By compiling and making these datasets available, they have fundamentally contributed to the field. These datasets can be accessed by registering or filling out the appropriate forms. One prominent resource comes from the SemEval 2019 Task 5, a Shared Task focused on the Multilingual Detection of Hate. Furthermore, the IIT-D Multilingual Abusive Comment Identification challenge has provided a dataset unique in its capacity for massively multilingual abusive comment identification across a variety of Indic languages. Another dataset worth mentioning revolves around Profiling Hate Speech Spreaders on Twitter, and is available in both English and Spanish. Complementing this, the HASOC 2020 challenge provides a dataset for Hate Speech and Offensive Content Identification. Additionally, the dataset from the TRAC—2020, caters to Bangla, Hindi, and English.

Available source codes: Table 7 provides a general overview of different solutions that have been developed in multilingual hate speech and offensive language detection. The source codes presented here are across 2020 to 2023, dealing with a variety of languages. Recently, Cohen et al. (2023) offered a source code along with live demonstrations to execute it. This code especially helps to further study and implement ensemble models based on RoBERTa or DeBERTa. It also gives a practical tool for researchers studying back translation and GPT-3 data augmentation techniques. Also, Deshpande, Farris & Kumar (2022) provided the source code of a model for detecting hate speech across ten languages. Another GitHub project is the “Multilingual-Abuse-Comment-Detection”, which focuses on identifying abusive comments in seventeen Indian languages using MuRIL-based models (BERT based Multilingual Representations for Indian Languages). Moreover, Röttger et al. (2021) evaluated the performance of hate speech detection models across ten languages, giving code to test across multilingual provided datasets. Adding to that, Aluru et al. (2021) worked across nine languages on sixteen datasets for the classification of hate speech data, presenting a source code to train and fine-tune several models, including mBERT-based, Translation+BERT, CNN+GRU and LASER+LR. Besides, each of Aluru et al. (2021) and Sharif, Hossain & Hoque (2021) gave solutions for offensive language detection in three different Dravidian languages. In 2020, the “Detoxify” project by Hanu & Unitary Team (2020), established on three Jigsaw challenges, studied multilingual toxic comment classification across seventeen languages using XLM-R based models, as well as Ahn et al. (2020b) which deals with offensive language detection in five languages, within Semeval 2020 task, being among the first ten places in each of Greek, Danish, and Turkish languages datasets. Overall, these source codes describe the recent research actions toward producing more practical and effective solutions for multilingual hate speech and offensive language detection. They underscore the increasing direction toward low-resource languages. Even though some of these codes didn’t have research study associated, they present detailed descriptions of their source codes in GitHub repositories.

Available APIs: The landscape of hate speech detection is rich with an array of tools that harness the power of AI to identify and counteract such harmful discourse. A comprehensive collection of tools and services designed to counteract and analyze hate speech is available online. Among the many prominent tools, illustrated in Table 8, we have the HateLab, an international center for studying hate speech founded by the Economic and Social Research Council (ESRC), the RapidAPI Hate Speech Detection that enables effective detection of offensive language, and iSpotHate, a freely available API dedicated to eradicating hate speech. Moreover, the Python library HateSonar (2020) offers simple and efficient hate speech detection without any need for user training, and Profanity-check (2019), another one, swiftly checks for profanity or offensive language in strings. Furthermore, StopPropagHate by INESC TEC, utilizes machine learning techniques to help news organizations automatically identify hate speech. Cohere offers a text moderation API that can efficiently filter out harmful or inappropriate content in real-time, while Hive.ai is a high-speed content moderation API with extensive training data (with results returned in under 200 ms). Additionally, MODERATION API can detect and hide a wide range of data entities, including sensitive information and inappropriate content. Similarly, Openai contributes to comprehend linguistic context, precisely identifying subtle instances of abusive language, including hate speech, cyberbullying, and content that promotes self-harm, as well as detecting probable instances of misinformation or disinformation.

Multilingual APIs: The relevance of multilingual tools in hate speech detection is paramount, as they aid in breaking language barriers to ensure the internet remains a safe space for all. Among these powerful tools, Sightengine stands out with its capability to detect hateful, sexual and toxic content across multiple languages, which include not just English, but also Chinese, French, Italian, Dutch, German, Portuguese, Swedish, Turkey, Filipino, and Spanish. Similarly, Spectrum Labs’ Guardian elevates the standard of multilingual content moderation. Unlike conventional tools that largely depend on keyword-based filters, Guardian employs true Natural Language Understanding (NLU) AI. This advanced technique allows the system to detect harmful behaviors, such as bullying, hate speech, spam, extremism, among others, across languages including Arabic, French, Hindi, Korean, among many others. Microsoft Azure is an API developed within Cognitive Services, it helps in detecting profanity in more than 110 languages. Also, Membrace can filter out various types of content, including spam, clickbait, offensiveness, among others. It covers multiple languages: English, German, French, Polish, Spanish, Turkish, Italian, Dutch, Swedish, Chinese, Arabic, Chinese, Russian and Japanese. Moreover, Alibaba Cloud’s Text Moderation 2.0 API, which supports up to 20 languages, offers a potent suite of features. These include content review, and custom configurations. Adding to that, Huawei Cloud contributes to this language-inclusive trend with its content moderation API. Although it currently supports only Chinese, its presence underlines the importance of multilingual tools and the continuous strides being made towards expanding language support in the field of hate speech detection.

Challenges

Limitations

Computational Limitations: Multilingual text classification task requires extensive computational resources due to large data volume from many languages and the complexity of the models employed (since multilingual models are usually bigger in size with more weights). In fact, multilingual embeddings or pre-trained transformers (mBERT, XLM-R, mT5, among others.) require more computing resources. Moreover, the process of fine-tuning these models usually needs extensive training. While some resources provided, like Google Colab, VastAI, and cloud platforms such as AWS, Google Cloud, Microsoft Azure, and Baidu offer essential computational resources, they may be costly, especially for users and researchers with limited budgets. For further details, a brief description of the resource providers, mentioned above, is presented in Table 9, we illustrated the most affordable offers delivered.

Multilingual Pre-trained Large Language models (Multilingual LLMs): Implementing and training pre-trained language models is not an easy task due to their limitations (Nozza, 2021). An example of these crucial limitations, presented by Conneau et al. (2020), is the “curse of multilinguality”. They indicate that training multilingual LLMs in more languages shows declines in performance despite keeping the number of update steps. Furthermore, performance usually declines when supporting more languages and providing optimal performance on a more limited language set. This ’curse’ basically involves determining whether to work on a small number of languages for more accurate performance or to distribute resources across multiple languages but with reduced performance (Pfeiffer et al., 2022).

Limitations on machine translation tools: The performance of offensive language detection models can be impacted by the quality and precision of the machine translation tools. Although multilingual Neural Machine Translation (multilingual NMT) displays significant performance, the degree to which it can handle many languages remains limited (Aharoni, Johnson & Firat, 2019). Recently, these tools have made important results in bilingual translation (Cho et al., 2014; Vaswani et al., 2017). However, there remain considerable barriers when it comes to implementing NMT in low-resource languages (Dabre, Chu & Kunchukuttan, 2020; Wang et al., 2021). Therefore, recent research studies have emphasized enclosing many translation data inside a single model to improve their performance (Aharoni, Johnson & Firat, 2019). However, it has been observed that these models frequently give low performance compared to the bilingual ones (Arivazhagan et al., 2019; Pham et al., 2019). Moreover, the majority of earlier studies have been focused on English language translation, which lead to non-English ones to be low performing (Aharoni, Johnson & Firat, 2019; Zhang et al., 2020).

Future directions

The field of multilingual and cross-lingual offensive language detection offers many promising recommendations for future research. especially in low resource languages, as well as in different topics of offensive language. For instance, since social media users generate one-third of the poor-quality Arabic content, Koshiry et al. (2023) built and annotated a standardized toxic Arabic dataset from Twitter, which would facilitate and improve toxicity analysis in Arabic language.