Detection and computational analysis of internet hate speech

Hate speech is plaguing the cyberspace along with user-generated content. Adding counter speech has become an effective way to combat hate speech online. Existing datasets and models target either (a) hate speech or (b) hate and counter speech but disregard the context. This paper investigates the role of context in the annotation and detection of online hate and counter speech, where context is defined as the preceding comment in a conversation thread. We created a context-aware dataset for a 3-way classification task on Reddit comments: hate speech, counter speech, or neutral. Our analyses indicate that context is critical to identify hate and counter speech: human judgments change for most comments depending on whether we show annotators the context. A linguistic analysis draws insights into the language people use to express hate and counter speech. Experimental results show that neural networks obtain significantly better results if context is taken into account. We also present qualitative error analyses shedding light into (a) when and why context is beneficial and (b) the remaining errors made by our best model when context is taken into account.

The CASE@EACL2024 Shared Task addresses Climate Activism online through three subtasks that focus on hate speech detection (Subtask A), hate speech target classification (Subtask B), and stance detection (Subtask C) respectively.Our contribution examines the effect of finetuning on external data for each of these subtasks.For the two subtasks that focus on hate speech, we augment the training data with the OLID (Zampieri et al., 2019a) dataset, whereas for the stance subtask we harness the SemEval-2016 Stance dataset (Mohammad et al., 2016b).We fine-tune RoBERTa and DeBERTa models for each of the subtasks, with and without external training data.For the hate speech detection and stance detection subtasks, our RoBERTa models came up third and first on the leaderboard, respectively.While the use of external data was not relevant on those tasks, we found that it greatly improved the performance on the hate speech target categorization.

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.

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.

Finding hate speech with auxiliary emotion detection from self-training multi-label learning perspective

This article investigates the identification of hate speech on social media using machine learning and deep learning techniques. The research uses metrics such as F-measure, AUC-ROC, precision, accuracy, and recall assessing the effectiveness of various tactics. The findings indicate that deep learning models, particularly the bidirectional long short-term memory (BiLSTM) architecture, consistently outperform other methods in categorization tasks. The research emphasizes the importance of sophisticated neural network designs in identifying the intricacies of hostile and offensive content online. The study offers insights for promoting early identification and prevention of cyberbullying, improving secure and inclusive online environments. Future research may explore real-time detection systems, hybrid approaches, or the integration of complementary components to enhance and improve innovative technology in tackling this significant social issue. A sample tweet was annotated by specialists who categorize tweets as hate speech, offensive language, or neutral. The researchers applied shallow learning methodologies and integrated word embeddings like Word2Vec and GloVe to enhance the efficacy of deep learning models. The results indicate that BiLSTM surpasses shallow learning methods in detecting hate speech on Twitter, highlighting the efficacy of deep learning approaches in recognizing and tracking hate speech on social media platforms. When comparing different deep learning and machine learning models on different datasets, the results reveal that deep learning techniques are usually more effective. A reasonably high level of accuracy is achieved by KNN and SVM among classical algorithms, whereas Naïve Bayes performs the poorest. While deep learning approaches provide better results, tree-based models such as Random Forest and Decision Trees offer more consistent accuracy. Models based on neural networks, such as LSTM, CNN, and BI-LSTM, perform well, with LSTM-based methods excelling in particular. The most successful strategy for classification problems is the model presented, which obtains the greatest accuracy, precision, recall, F1-score of 95%. The research aids in the development of advanced tools and methodologies to mitigate hate speech on social media and foster positive online interactions. Future research may investigate alternative deep learning architectures, such as transformers, to enhance hate speech detection efficacy. The advancement of interpretable AI methodologies for identifying hate speech and delivering transparent forecasts might enhance user confidence and facilitate better content moderation decisions.

Online hate has emerged as a rapidly growing issue worldwide, often stemming from differences in opinion. It is crucial to use appropriate language and words on social media platforms, as inappropriate communication can negatively impact others. Consequently, detecting hate speech is of significant importance. While manual methods are commonly employed to identify hate and offensive content on social media, they are time-consuming, labor-intensive, and prone to errors. Therefore, AI-based approaches are increasingly being adopted for the effective classification of hate and offensive speech. The proposed model incorporates various text preprocessing techniques, such as removing extraneous elements like URLs, emojis, and blank spaces. Following preprocessing, tokenization is applied to break down the text into smaller components or tokens. The tokenization technique utilized in this study is TF-IDF (Term Frequency–Inverse Document Frequency). After tokenization, the model performs the classification of hate and offensive speech using the proposed BiLSTM-based SM-CJ (Scalable Multi-Channel Joint) framework. The BiLSTM-based SM-CJ model is particularly effective in detecting hate, offensive, and neutral tweets due to its ability to capture both forward and backward contexts within a given text. Detecting hate speech requires a comprehensive understanding of the text and the identification of patterns spanning across multiple words or phrases. To achieve this, the LSTM component of the BiLSTM model is designed to capture long-term dependencies by utilizing information from earlier parts of the text. The proposed SM-CJ framework further aligns the input sequence lengths fetched from the input layer, enabling the model to focus on specific segments of the input sequence that are most relevant for hate speech detection. This approach allows the model to accurately capture derogatory language, and subtle nuances present in hate speech. Finally, the performance of the proposed framework is evaluated using various metrics, including accuracy, recall, F1-score, and precision. The results are compared with state-of-the-art approaches, demonstrating the effectiveness of the proposed model. Doi: 10.28991/ESJ-2025-09-01-03 Full Text: PDF

Hate speech towards a group or an individual based on their perceived identity, such as ethnicity, religion, or nationality, is widely and rapidly spreading on social media platforms. This causes harmful impacts on users of these platforms and the quality of online shared content. Fortunately, researchers have developed different machine learning algorithms to automatically detect hate speech on social media platforms. However, most of these algorithms focus on the detection of hate speech that appears in English. There is a lack of studies on the detection of hate speech in Arabic due to the language’s complex nature. This paper aims to address this issue by proposing an effective approach for detecting Arabic hate speech on social media platforms, namely Twitter. Therefore, this paper introduces the Arabic BERT-Mini Model (ABMM) to identify hate speech on social media. More specifically, the bidirectional encoder representations from transformers (BERT) model was employed to analyze data collected from Twitter and classify the results into three categories: normal, abuse, and hate speech. In order to evaluate our model and state-of-the-art approaches, we conducted a series of experiments on Twitter data. In comparison with previous works on Arabic hate-speech detection, the ABMM model shows very promising results with an accuracy score of 0.986 compared to the other models.

The internet has become an everyday tool to communicate and network with people around the globe, but its perceived anonymity, availability, and instant access have made it an environment conducive to spreading hateful content and connecting to like-minded individuals with similar hateful ideologies. Hate speech and other prejudice-motivated behavior, however, need to be considered on a continuum of victimization, and "like other social processes, [be seen as] dynamic and in a state of constant movement and change, rather than static and fixed" (Bowling, 1993, p. 238). It is a social process that is marked by multiple, repeat, and constant victimization (Bowling, 1993), with victims no longer distinguishing between specific hateful events, and rather normalizing experiences of hateful conduct "as an everyday, unwanted but routine reality of being 'different'" (Chakraborti, 2016, p. 581). Understanding hateful behavior and victimization as a process allows us to connect "low-level" incidents of hateful behavior to the more serious and life-threatening incidents at the more extreme end of the spectrum (Bowling & Phillips, 2002). The Christchurch attacks in New Zealand and their link to hateful communication on the online platform 8chan is only one such example of how online hate speech and cyberhate can escalate to "in real life" attacks, leaving the online sphere and spilling into the offline world. As per Allport's (1954) scale of prejudice, more extreme forms of prejudice-motivated violence are founded on "lower level" acts of prejudice and bias, therefore, hateful content online should not be ignored. Intervening online to interrupt or counter hateful behavior already at the lower end of the scale of prejudice becomes important; online interventions which are to be identified and synthesized through this systematic review. Allport's (1954) scale of prejudice will be the basis for this systematic review. Early on, Allport (1954) asserted that individuals with negative attitudes toward groups are likely to act out on these prejudices "somehow, somewhere" (p. 14), and that the more intense such negative attitudes are, the more hostile the action will be. Allport (1954) put forward a scale of acts of prejudice to illustrate different degrees of acting out negative attitudes, a scale that starts with antilocution (or what we call hate speech), described as explicitly expressing prejudices through negative verbal remarks to either friends or strangers (Allport, 1954). Avoidance is the next level on the scale of prejudice, with people avoiding members of certain groups, followed by discrimination, where distinctions are made between people based on prejudices, which leads to the active exclusion of members from certain groups (Allport, 1954). This level of acting on prejudices is routed in institutional or systemic prejudices, for example, in the differential treatment of people within employment or education practices, but also within the criminal justice system, or through social exclusion of certain minority group members. Physical attack is the next level on the scale of prejudice, which includes violence against members of certain groups by physically acting on negative attitudes or prejudices. The last level is extermination, which is the ultimate act of violence against members of specific groups, an expression of prejudice that systematically eradicates an entire group of people (e.g., genocide or lynchings; Allport, 1954). Allport's (1954) scale of prejudice makes it clear how hate speech/cyberhate is connected to more extreme forms of violence motivated by specific prejudices and biases, with hate speech (or antilocutions) being only the starting point on a 5-point continuum (Bilewicz & Soral, 2020). The importance of this scale of prejudice is not only that it clearly illustrates a range of different ways and intensity levels to act out prejudices, but also the "progression from verbal aggression to physical violence or, in other words, the performative potential of hate speech" (Allport, 1954; Kopytowska & Baider, 2017, p. 138). This is where interventions at the lower level of the scale of prejudices, interventions targeting hate speech/cyberhate, become important. There is no universal definition of hateful conduct online, but there is some consensus that hate speech targets disadvantaged social groups (Jacobs & Potter, 1998). Bakalis (2018) more narrowly defines cyberhate as "any use of technology to express hatred towards a person or persons because of a protected characteristic—namely race, religion, gender, sexual orientation, disability and transgender identity" (p. 87). Another definition that also points out the ambiguity and challenges involved with identifying more subtle forms of hate speech, and also making reference to the potential threat of hate speech escalating to offline violence, is that put forward by Fortuna and Nunes (2018), who analyzed various definitions of hate speech "Hate speech is language that attacks or diminishes, that incites violence or hate against groups, based on specific characteristics such as physical appearance, religion, descent, national or ethnic origin, sexual orientation, gender identity or other, and it can occur with different linguistic styles, even in subtle forms or when humour is used" (p. 5). In this systematic review, we also distinguish hate speech/cyberhate specifically from other forms of harmful online activity, such as cyber-bullying, harassment, trolling or flaming, as perpetrators of such online behavior repeatedly and systematically target specific individuals to cause upset, to seek out negative reactions, or to create discord on the internet. In contrast, hate speech/cyberhate is more general and does not necessarily target a specific individual (Al-Hassan & Al-Dossari, 2019), instead hate speech/cyberhate heavily features prejudice, bias and intolerance toward certain groups within society. With the majority of hate speech happening online, interventions that take place online are an important way to challenge prejudice and bias, potentially reaching masses of people across the globe. The unique feature of the internet is that such individual negative attitudes toward minority groups and more extreme hateful ideology can find its way onto certain platforms and can instantly connect people sharing similar prejudices. By closing the social and spatial distance, the internet creates a form of collective identity (Perry, 2000, p. 123) and can convince individuals with even the most extreme ideologies that others out there share their views (Gerstenfeld et al., 2003). In addition, the enormous frequency of hate speech/cyberhate within online environments creates a sense of normativity to hatred and the potential for acts of intergroup violence or political radicalization (Bilewicz & Soral, 2020, p. 9). It is, therefore, important to challenge this hate speech epidemic (Bilewicz & Soral, 2020), especially since hate movements have increasingly crossed into the mainstream (Perry, 2000). With hate speech/cyberhate posing a threat to the social order by violating social norms (Soral et al., 2018), perceptions of social norms as either supporting or opposing prejudice has been found to have an influence on how individuals react online (Hsueh et al., 2015). Seeing other people post prejudiced (opposed to antiprejudiced) comments online can lead to the adoption of an online group's biases and can influence an individual's own perceptions and feelings toward the targeted stigmatized group (Hsueh et al., 2015). In addition, research around desensitization also suggests that being exposed to hate speech leads to desensitization, which further leads to an increase in outgroup prejudice toward groups targeted by such speech (Soral et al., 2018). With society increasingly recognizing that it is inappropriate to express prejudices in public settings, many interventions will include some form of social norms nudging to reduce such prejudices; interventions that "nudge behavior in the desired direction" (Titley et al., 2014, p. 60). Therefore, hate speech not only affects minority group members, but also has an influence on opinions of majority group members (Soral et al., 2018), which makes strategies that can elicit change in people's prejudice-related attitudes crucial (see, e.g., Zitek & Hebl, 2007). Governments around the world face increased demand for understanding and countering hateful ideology and violent extremism both online and offline (e.g., the Christchurch Call in New Zealand). The U.S. Government's 2011 CVE Strategy highlights the importance of ongoing research and analysis, the sharing of knowledge and best practices internationally, and the countering of hateful ideologies and propaganda (see also Department of Homeland Security, 2016, 2019). The goal of this systematic review is to use an integrated and interdisciplinary approach to examine the effectiveness of online campaigns and strategies for reducing hate speech and cyberhate. The internet also provides an opportunity to reach masses of people who have been exposed to hateful content and ideology online, therefore, this systematic review will focus on online interventions addressing online hate speech and cyberhate. The specific settings where we would expect to see the online interventions deployed will be on websites, text messaging applications, and online and social media platforms including, but not limited to, Facebook, Instagram, TikTok, WhatsApp, Google, YouTube, and Snapchat. As mentioned previously, many online interventions will be based on social norm nudges to reduce online hate. These interventions aim to change people's online behavior and encourage individuals or groups to conform to established social norms. The communication of social norms can happen through establishing community standards on online platforms themselves (e.g., Facebook, Twitter, etc.), through more formal online training courses, or through anti-hate speech/anti-cyberhate campaigns teaching people to recognize hate, embrace diversity, and stand up to bias. Such prevention campaigns are designed to challenge bias and build ally behaviors by supplying people with constructive responses to combat, for example, antisemitism racism, and homophobia, as well as provide resources to help people explore and critically reflect on current events. Other interventions may add messages to hateful online comments, counter hateful content or extremist ideology, or redirect people to more credible sources. Both peers and parents have been found to foster racial consciousness and identity development, define interracial relationships and cultivate ethnic heritage and culture (Hagerman, 2016). Socialization influences how children understand their group's social position and their membership within that group by providing an understanding of racial, religious, and sexual privilege (Bowman & Howard, 1985). Socialization often reflects peers' and parents' experiences with racism, discrimination, and their ideological perspectives about race, religion, or sexuality (Umaña-Taylor & Fine, 2004). This is important because peers and parents who feel discriminated against or believe that the "other" is a threat may impart their prejudices to their children or friends, which could lead them to interpret the social world with similar discriminatory views and/or behavior. Individuals who feel socially alienated or rejected are especially vulnerable to such socialization practices as they feel that adopting these views will provide them with a sense of acceptance and belonging (Leiken, 2012). Regardless of how an individual develops certain racial, religious, or sexual biases, the online interventions under review are expected to target and reduce the production of original hateful content such as antisemitic Tweets and/or homophobic blog posts as well as the consumption of hate speech material (e.g., watching or reading hate speech videos or blogs). For example, some interventions take a rather broad messaging approach by implementing racial sensitivity and diversity training through Public Service Announcements, peer-to-peer dialogue workshops, or films that provide opportunities for youth and adults to self-reflect and learn about historical oppression, people of color, women, and the LGBTQIA+ community from credible sources. The factual understanding of diverse groups is often supplemented by experiences with people within the group. These educational programs often identify a cultural guide who is willing to introduce youth to new experiences and who can aid in processing thoughts, feelings, and behaviors. These interventions intend to dispute and contradict negative stereotypes associated with specific cultures, people, and institutions by sharing different points of view based on human rights values such as openness, respect for difference, freedom, and equality (Gomes, 2017). Moreover, such interventions tend to involve blanket bans on specific behaviors enforced through the public promotion of norms or individual sanctions enforced by moderators. Other interventions, such as the "Redirect Method," are narrower in their messaging. These interventions generate curated playlists and collections of authentic content that challenge hate speech/cyberhate narratives and propaganda (Helmus & Klein, 2018). For instance, people who are directly searching for extremist content online may be linked to videos and written content that confronts such claims. These videos are designed to be objective in appearance instead of containing material that explicitly counters extremist propaganda. The underlying goal of this type of interventions is to provide credible content that effectively undermines extremist messaging but does not overtly attack the source of propaganda. In addition to confronting hate speech narratives, these interventions provide users with links to numerous social services such as anger management training, drug and alcohol treatment, and mental health resources. Online platforms, such as Twitter and Facebook, have also started to employ a similar method, redirecting people who comment on or share "fake news" or conspiracy theories, which often are fraught with prejudicial undertones and are harmful to minority groups, to more credible content and news sources. The aforementioned interventions are designed to counter-balance these biased perceptions (e.g., unsupported claims of the Black community as criminal or the LGBTQIA+ community as pathologized) Blacks as criminals, LGBTQIA+ as pathologized) by blunting the occurrence of racist discourse and reducing the likelihood these individuals will internalize and normalize racial, religious, and/or sexual prejudices (Qian et al., 2019). Being in new situations is uncomfortable and often awakens fears and apprehensions that can block our experiential development. Acquiring information or being exposed to minority-run businesses, poverty, and writings from minority authors allows a person to understand the thoughts, hopes, fears, and aspirations of the people outside their racial perspective rather than from the perspective of the majority society (Dunham et al., 2013; Lee et al., 2017). Doing so, counters racist programming by challenging hegemonic beliefs, which can lead to the acceptance of tolerant attitudes and the reduction of hateful expressions online. Findings from the proposed review will enhance our understanding of the effectiveness of online anti-hate speech/anti-hate interventions, will help ensure that programming funds are dedicated to the most-effective efforts, and will play a critical role in helping individual programs improve the quality of service provisions. It will inform governments and policymakers of the current state of such online efforts, what works and which modes of interventions to implement, and help guide economically viable investments in nation-state security. Our search of the scholarly literature identified one review, Blaya (2019), as similar to the proposed topic. Blaya's (2019) review, however, focused on the prevalence, type, and characteristics of existing interventions for counteracting cyberhate and did not include a meta-analysis. Two other similar reviews focused on exposure to extremist online content (Hassan et al., 2018) and communication channels associated with cyber-racism (Bliuc et al., 2018). A search of the Campbell Library using key terms (hate OR radical*) returned two protocols and one review identified for further inspection to assess potential overlap. The protocols include "Psychosocial processes and intervention strategies behind Islamist deradicalization: A scoping review" by de Carvalho et al. (2019) and "Police programs that seek to increase community connectedness for reducing violent extremism behavior, attitudes and beliefs" by Mazerolle et al. (2020). A further review on a similar topic is a recently completed Campbell review (January 2020), "Counter-narratives for the prevention of violent radicalization: A systematic review of targeted interventions" by Carthy et al. (2018) at the National University of Ireland, Galway. Our proposed review is distinguished from the de Carvalho et al. (2019) review in that we are focusing on hate speech and cyberhate generally without delimiting our approach to a specific type of radicalization (e.g., Islamist). Furthermore, we are electing to complete a systematic review and meta-analysis. Likewise, the protocol by Mazerolle et al. (2020) focuses on interventions involving police officers either as initiators, recipients, or implementers of community connectedness interventions. Our review will focus specifically on any online intervention, which may or may not involve police, but police will not be the focus nor be the basis of the online intervention strategy. Judging from Carthy et al. (2018) protocol, we anticipate our review will also capture counter-narrative interventions, but will differ based on setting, timing, and scope of interventions. Specifically, we are interested in online interventions that extend beyond counter-messaging campaigns to include a broad array of interventions outlined above and extend beyond radicalization to include everyday hate and prejudice. In addition to conducting a meta-analysis, the proposed review would build on Blaya's (2019) work by expanding the population parameters to include both adolescents as well as adults. Blaya (2019) limited her search to include interventions aimed toward youth, young people, children, young adults, adolescents, children, and teenagers and did not focus on extremism. The main objective of this review is to synthesize the available evidence on the effectiveness of online interventions aimed at reducing the creation and/or consumption of online hate speech/cyberhate material. To what extent are online interventions effective in reducing online hate speech/cyberhate? How is effectiveness related to the type of online hate speech/cyberhate intervention used? How is effectiveness related to the characteristics of individuals experiencing the online hate speech/cyberhate intervention (e.g., age, gender, race/ethnicity, offense history, childhood trauma)? Both experimental and quasi-experimental quantitative studies will be included. These study designs will address research questions #1 to #3. Eligible quantitative study designs include the following: Eligible experimental designs must involve random assignment of participants to distinct treatment and control group(s). Designs that involve quasi-random assignment of participants such as alternate case assignment are also eligible and will be coded as experimental designs. All eligible quasi-experimental designs must include a comparison group of participants compared to participants in the treatment condition. Eligible studies include those that report matching procedures (individual- or group-level) and statistical procedures employed to achieve equivalency between groups. Statistical procedures may but are not limited to, analysis, and Furthermore, in of a limited quantitative evidence we will also include quasi-experimental studies with comparison groups that provide of for both groups. will also be included. Eligible include designs with a control group and designs with or without a control group than quasi-experimental designs include studies that a comparison group of participants who either to in the study or who in a but out to the of a Eligible comparison include other online interventions or in which participants not or an online Both youth and participants of any gender sexual orientation, or will be eligible for this review. The eligible youth population will be study participants with a of through The eligible population will be study participants with a of and in which only a of the is eligible for example, a study in both online and offline hate speech be not anticipate studies based on as our will be and we will take to studies that only online interventions. will of the of a study for through and be we will elicit the of a the of and studies will be these studies will be they will be and be in the and any related Blaya's (2019) of intervention strategies to the potential of eligible interventions. The intervention is the of responses to hate speech/cyberhate, which includes the countering of violent extremism and to address online interventions that are eligible range from hateful content online specific (e.g., of social media to to online hate using targeted strategies (e.g., through hateful of studies focusing on online include the and of online and content online content and & 2018), hateful online comments to comments et al., 2018), and to users out of online are also interested in interventions such as the of 8chan this online platform linked to "in real life" attacks in New Zealand and the and interventions that further hateful online content and radicalization similar events. hateful content online such has up speech as well as around online users and hateful groups on to other online to hateful content online using targeted strategies therefore, been as an effective online include using the from & 2020), the use of to online responses to in online where hate speech has been (Qian et al., 2019), and redirecting online users to videos for example, Our systematic review will include a range of online interventions, many of which have only recently Two other strategies identified by Blaya (2019) are the and of hate speech/cyberhate using technology as well as the creation of online and These interventions include online counter-narrative the and/or use of online counter online interventions, online training, and online narrowly to address extremist ideologies and hate speech that incites targeted violence and In such interventions seek to or the occurrence of violent extremism or the of hate speech and extremist by channels and opportunities to such groups. The and intervention eligible for this systematic review educational programs for example, provide people with online and challenge 2019). will include online programs with an online (e.g., and and educational and online interventions. of these interventions may by individuals no longer in the creation and/or consumption of cyberhate and extremist material online. These online interventions may be by and internet service or or in the case of interventions. The comparison may be routine exposure and to hate speech/cyberhate or online The of is the creation and/or consumption of hateful content online. By we to the production and of original hateful content such as antisemitic racist and/or homophobic blog The consumption of hate speech material may include or being a of a hate watching or reading hate speech videos or being a target of online hate speech/cyberhate, or hate speech material. of include and of study participants such as and attitudes toward hate Eligible studies must report a or (or to be included. There will be no exclusion on the source of for the and can be from any institutional or completed by will include any of from strategies to increase the scale of of potentially effective anti-hate speech and interventions for These could include to or to the creation of and behaviors. can also include such as a of hate speech/cyberhate to other platforms instead of a reduction of hate All described in eligible studies will be in the will focus on the between and the current The starting with the when the internet to a and community et al., are for an approach in the lower end of our search to the may be it is hate speech/cyberhate online through or and some studies may capture Our population of studies will also be limited to studies in and but of studies completed in any as we are focused on online content that can be and across and nation-state The language parameters reflect the language of the review Our will where studies the of study in will be between the members of the review These will be and as a from the protocol in the review. 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Over the last two decades, social media platforms have grown dramatically. Twitter and Facebook are the two most popular social media platforms, with millions of active users posting billions of messages daily. These platforms allow users to have freedom of expression. However, some users exploit this facility by disseminating hate speeches. Manual detection and censorship of such hate speeches are impractical; thus, an automatic detection mechanism is required to detect and counter hate speeches in a real-time environment. Most research in hate speech detection has been carried out in the English language. Still, minimal work has been explored in other languages, mainly Urdu written in Roman Urdu script. A few research have attempted machine learning, and deep learning models for Roman Urdu hate speech detection; however, due to a scarcity of Roman Urdu resources, and a large corpus with defined annotation rules, a robust hate speech detection model is still required. With this motivation, this study contributes in the following manner: we developed annotation guidelines for Roman Urdu Hate Speech. Second, we constructed a new Roman Urdu Hate Speech Dataset (RU-HSD-30K) that was annotated by a team of experts using the annotation rules. To the best of our knowledge, the Bi-LSTM model with an attention layer for Roman-Urdu Hate Speech Detection has not been explored. Therefore, we developed a context-aware Roman Urdu Hate Speech detection model based on Bi-LSTM with an attention layer and used custom word2vec for word embeddings. Finally, we examined the effect of lexical normalization of Roman Urdu words on the performance of the proposed model. Different traditional as well as deep learning models, including LSTM and CNN models, were used as baseline models. The performance of the models was assessed in terms of evaluation matrices like accuracy, precision, recall, and F1-score. The generalization of each model is also evaluated on a cross-domain dataset. Experimental results revealed that Bi-LSTM with attention outperformed the traditional machine learning models and other deep learning models with an accuracy score of 0.875 and an F-Score of 0.885. In addition, the results demonstrated that our suggested model (Bi-LSTM with Attention Layer) is more general than previous models when applied to unseen data. The results confirmed that lexical normalization of Roman Urdu words enhanced the performance of the suggested model.

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.

Technological developments over the past few decades have changed the way people communicate, with platforms like social media and blogs becoming vital channels for international conversation. Even though hate speech is vigorously suppressed on social media, it is still a concern that needs to be constantly recognized and observed. The Arabic language poses particular difficulties in the detection of hate speech, despite the considerable efforts made in this area for English-language social media content. Arabic calls for particular consideration when it comes to hate speech detection because of its many dialects and linguistic nuances. Another degree of complication is added by the widespread practice of "code-mixing," in which users merge various languages smoothly. Recognizing this research vacuum, the study aims to close it by examining how well machine learning models containing variation features can detect hate speech, especially when it comes to Arabic tweets featuring code-mixing. Therefore, the objective of this study is to assess and compare the effectiveness of different features and machine learning models for hate speech detection on Arabic hate speech and code-mixing hate speech datasets. To achieve the objectives, the methodology used includes data collection, data pre-processing, feature extraction, the construction of classification models, and the evaluation of the constructed classification models. The findings from the analysis revealed that the TF-IDF feature, when employed with the SGD model, attained the highest accuracy, reaching 98.21%. Subsequently, these results were contrasted with outcomes from three existing studies, and the proposed method outperformed them, underscoring the significance of the proposed method. Consequently, our study carries practical implications and serves as a foundational exploration in the realm of automated hate speech detection in text.

Detecting hate speech is a significant task in understanding the contents published online, particularly in natural language processing. Strong automated systems that can identify and minimize harmful online communication are therefore becoming necessary. The aim of this study is to compare classical deep learning models against transformer-based architectures to find a technique for identifying hate speech written in English. Using a hate speech classification labeled dataset, the study determine the performance of each model across evaluation metrics like accuracy, precision, recall and F1-score. For addressing class imbalance and improve generalization, methods like focal loss and data augmentation were applied. Our findings indicate that the models based on transformers, particularly RoBERTa-large, significantly outperform traditional architectures in finding out subtle and context-dependent instances of hate speech. This research highlights emerging importance of large pre-trained language models and hybrid ways for enhancing automated hate speech detection systems.

Along with the increasing activity on social media, hate speech is getting out of control. Hate speech detection can be done by utilizing text mining technology. There have been many hate speech detection studies conducted. To identify and analyze research trends, data sources, methods and features used in hate speech detection, this systematic literature review was created. Until early 2020, the topics of hate speech were found, including hate speech against minorities, religion, women, the general election agenda, and politics. Sources of data that are widely used to be used as datasets come from twitter. Hate speech is not only classified into HS (hate speech) and Non-HS (non-hate speech) but can be further classified into racism, sexism, offensive, abusive, threats of violence and others. Of the 38 studies that meet inclusion and exclusion, there are 26 algorithms and 28 features that have been used to detect hate speech. However, these methods and features do not necessarily guarantee a good hate detection performance. Hate speech classification performance is also influenced by the dataset, the features chosen, the number of classes and mutually exclusive classes.

Recent research on Responsible AI, particularly in addressing algorithmic biases, has gained significant attention. Natural Language Processing (NLP) algorithms, which rely on human-generated and human-labeled data, often reflect these challenges. In this paper, we analyze inter-annotator agreement in the task of labeling hate speech data and examine how annotators’ backgrounds influence their labeling decisions. Specifically, we investigate differences in hate speech annotations that arise when annotators identify with the targeted groups. Our findings reveal substantial differences in agreement between a general pool of annotators and those who personally relate to the targets of the hate speech they label. Additionally, we evaluate the OpenAI GPT-4o model on the same dataset. Our results highlight the need to consider annotators’ backgrounds when assessing the performance of Large Language Models (LLMs) in hate speech detection.