Exploring Cross-Cultural Differences in E nglish Hate Speech Annotations: From Dataset Construction to Analysis (2024)

Abstract: Most hate speech datasets neglect the cultural diversity within a single language, resulting in a critical shortcoming in hate speech detection. To address this, we introduce CREHate, a CRoss-cultural English Hate speech dataset. To construct CREHate, we follow a two-step procedure: 1) cultural post collection and 2) cross-cultural annotation. We sample posts from the SBIC dataset, which predominantly represents North America, and collect posts from four geographically diverse English-speaking countries (Australia, United Kingdom, Singapore, and South Africa) using culturally hateful keywords we retrieve from our survey. Annotations are collected from the four countries plus the United States to establish representative labels for each country. Our analysis highlights statistically significant disparities across countries in hate speech annotations. Only 56.2% of the posts in CREHate achieve consensus among all countries, with the highest pairwise label difference rate of 26%. Qualitative analysis shows that label disagreement occurs mostly due to different interpretations of sarcasm and the personal bias of annotators on divisive topics. Lastly, we evaluate large language models (LLMs) under a zero-shot setting and show that current LLMs tend to show higher accuracies on Anglosphere country labels in CREHate.Our dataset and codes are available at: https://github.com/nlee0212/CREHate

## introduction identifying hate speech is highly subjective and relies heavily on an annotator's understanding and knowledge of the cultural context @xcite . unfortunately, existing english hate speech datasets often overlook the cultural diversity within the posts and the annotators. they are predominantly collected from twitter ( our research aims to investigate the influence of cultural diversity on hate speech. to achieve this, we construct a dataset that reflects diversity and examine how cultural background affects the inter-pretation of hate speech by annotators. specifically, we align culture with nationality when exploring how cultural background influences annotators' interpretations of hate speech. we acknowledge that focusing only on cross-country differences may not fully encompass the multifaceted cultural dynamics within each country. however, it offers a starting point to understand how annotators' cultural background based on nationality affects language interpretation, particularly in sensitive areas like hate speech. this approach underlines the importance of further, more detailed studies into the complex interplay of cultural identities and their impact on language perception @xcite , especially for enhancing hate speech moderation on global platforms. to this end, we construct crehate-a crosscultural english hate speech dataset-comprising 1,580 online posts annotated by individuals from five english-speaking countries: australia (au), united kingdom (gb), singapore (sg), the united states (us), and south africa (za) foot_2 . construction of crehate is done in a 2-step procedure: 1) cultural post collection and 2) cross-cultural annotation (figure 1 ). for cultural post collection, we collect 600 posts from youtube and reddit using keywords gathered from surveys from four countries: au, gb, sg, and za. we also sample 980 posts from sbic @xcite , a toxic language dataset of social media posts including diverse target groups, primarily reflecting a north american perspective ( we show that cross-cultural annotations of cre-hate demonstrate significant differences across countries. only 56.2% of the entire posts receive unanimous label agreement across all five countries, and the average pairwise agreement between countries is 78.8%, with a maximum label disagreement of 26.0%. the pairwise label agreement distribution among countries exhibits a notable deviation from that of randomly selected annotator groups, with its average being 2.62σ lower than the average pairwise label agreement of the random groups. furthermore, by conducting a qualitative analysis of potential reasons for label disagreements, we show that the primary contributing factors are likely due to different understandings of sarcasm and the personal bias of annotators on divisive topics. finally, we show that current llms tend to show higher accuracy scores on core anglosphere country labels in crehate. we further identify the limitations of these models in culture-specific hate speech classification, in which they are asked to predict hate speech based on the target country. our main contributions are as follows: ## analysis on the annotations in this section, we show that varying cultural backgrounds of annotators and posts lead to a significant disparity in hate speech annotation. ## experiments this section evaluates the performance of current llms in hate speech classification on crehate, with a specific focus on analyzing their performance with respect to country-specific annotations. experimental settings. we conduct zero-shot experiments using a multiple-choice question format. we use five variations of the question prompt, each presented with two answer options: 'hate' and 'non-hate. @xcite we test both answer option orders for all prompts, resulting in 10 formats 14 . all 1,580 instances of the crehate corpus are used in the experiments, and all the prompts are generated by the authors. the experiments are divided into two parts. in §5.1, we ask the model to predict whether a given post is hate speech. in §5.2, we add a target country to the question prompt to examine the model's ability to make culture-specific predictions in hate speech detection. for all results, we present the average values for all variations and perform a wilcoxon rank-sum test to determine any statistically significant differences between each model's highest and lowest accuracy scores across different countries. the models evaluated include gpt-4 (gpt-4-1106-preview), gpt-3.5 (gpt-3.5-turbo-1106), orca 2 (7b) @xcite , flan-t5-xxl @xcite , and opt @xcite 15 . ## conclusion in this paper, we analyze how cultural differences across english-speaking countries affect hate speech annotations. to this end, we develop crehate, a cross-cultural english hate speech dataset comprising 1,580 posts from five englishspeaking countries-au, gb, sg, us, and za. our work shows that there are notable variations in hate speech interpretations between these countries through various statistical methods. the overall agreement on hate speech identification across all countries is only 56.2%, with an average pairwise country disagreement of 21.2%. qualitative analysis suggests these differences stem from varied understandings of sarcasm and annotators' biases on divisive topics. we also discover that gpt models display higher accuracies with labels from anglosphere cultures and fail to make culturally tailored predictions when the target country is given. this research establishes a foundational framework for continuously evaluating and adapting hate speech models and datasets. we suggest expanding crehate to include more countries and posts to create a comprehensive tool for assessing cultural biases in model predictions and enhancing culturally tailored hate speech detection. we urge collaborative efforts in constructing datasets with broad cultural references and contextual nuances. annotators with relevant cultural knowledge should be employed to construct a more representative cultural dataset. such a comprehensive approach is crucial for developing more effective, culturally sensitive hate speech classifiers and promoting safer and more inclusive online communication. current hate speech detection tools often struggle with cultural biases, particularly skewing toward us or western perspectives. this results in inadequate representation and understanding of sociocultural contexts from other english-speaking countries. for example, a phrase considered derogatory in one culture might be benign in another, leading to false positives or negatives in detection. interpretations of posts can vary significantly between cultures, influenced by factors like local idioms, societal norms, and historical contexts. therefore, a more nuanced and inclusive approach to dataset creation and algorithm development is needed to ensure broader cultural representation and sensitivity.