Venue: NAACL 2025

# Multi\({}^{3}\)_Hate:_ Multimodal, Multilingual, and Multicultural Hate Speech

Detection with Vision-Language Models

 Minh Duc Bui\({}^{\heartsuit}\) Katharina von der Wense\({}^{\heartsuit\spadesuit}\) Anne Lauscher\({}^{\diamondsuit}\) Johannes Gutenberg University Mainz, Germany

University of Colorado Boulder, USA \({}^{\diamondsuit}\)University of Hamburg, Germany

{minhducbui, k.vonderwense}@uni-mainz.de

anne.lauscher@uni-hamburg.de

###### Abstract

**Warning: this paper contains content that may be offensive or upsetting**

Hate speech moderation on global platforms poses unique challenges due to the multimodal and multilingual nature of content, along with the varying cultural perceptions. How well do current vision-language models (VLMs) navigate these nuances? To investigate this, we create the first multimodal and multilingual parallel hate speech dataset, annotated by a multicultural set of annotators, called **Multi\({}^{3}\)Hate**. It contains 300 parallel meme samples across 5 languages: English, German, Spanish, Hindi, and Mandarin. We demonstrate that cultural background significantly affects multimodal hate speech annotation in our dataset. The average pairwise agreement among countries is just 74%, significantly lower than that of randomly selected annotator groups. Our qualitative analysis indicates that the lowest pairwise label agreement--only 67% between the USA and India--can be attributed to cultural factors. We then conduct experiments with 5 large VLMs in a zero-shot setting, finding that these models align more closely with annotations from the US than with those from other cultures, even when the memes and prompts are presented in the dominant language of the other culture. Code and dataset are available at [https://github.com/MinBucBui/Multi3Hate](https://github.com/MinBucBui/Multi3Hate).

## 1 Introduction

Our cultural backgrounds significantly shape our perceptions of the world. For instance, individuals raised in collectivist societies often emphasize group harmony, leading them to interpret events through a relational lens, whereas those from individualistic societies may prioritize personal achievements and autonomy, resulting in a perception that focuses on individual characteristics Triandis [28]. Consequently, identical content can be perceived vastly differently depending on cultural background, posing challenges for hate speech moderation models as they must balance diverse perspectives without marginalizing certain cultures while favoring others.

Towards incorporating this important goal, Lee et al. [9] released the first and only hate speech dataset, annotated by a multicultural set of annotators, revealing that large language models often exhibit bias toward Anglospheric cultures. However, their work leaves critical gaps unaddressed: (1) The dataset is limited to text-based content, excluding multimodal forms of hate; (2) It is restricted to English-language samples, overlooking non-English-speaking cultures. This narrow scope not only _hampers the cross-cultural evaluation of multimodal_ hate speech detection models, providing little guidance for practitioners, but also amplifies the _exclusion of non-English-speaking_ cultures from cross-cultural analysis.

Figure 1: Our dataset creation process is divided into three stages: 1. Crawling Stage; 2. Translation Stage; and 3. Cross-Cultural Hate Speech Annotation Stage. The two examples illustrate the varying ways in which memes are annotated across different cultures.



To close this gap, we are the first, to the best of our knowledge, to release a parallel **m**ultilingual and **m**ultimodal hate speech dataset. Additionally, the dataset is annotated by a **m**ulticultural set of annotators, as shown in Table 1. Our dataset, **Multi\({}^{3}\)Hate**, comprises a curated collection of 300 memes--images paired with embedded captions--a prevalent form of multimodal content, presented in five languages: English (_en_), German (_de_), Spanish (_es_), Hindi (_hi_), and Mandarin (_zh_). Each of the 1,500 memes (300\(\times\)5 languages) is annotated for hate speech in the respective target language by at least five native speakers from the same country. These countries were chosen based on the largest number of native speakers of each target language: USA (US), Germany (DE), Mexico (MX), India (IN), and China (CN) [26], World Population Review, 2024). As in prior research, we use the country of the annotators as a cultural proxy (EVS/WVS, 2022; Koto et al., 2023; Lee et al., 2024).

We demonstrate that cultural background significantly influences multimodal hate speech annotation in our dataset. The average pairwise agreement among countries is only 74%, significantly lower than that of randomly selected annotator groups. The lowest agreement, at just 67%, occurs between the USA and India. Through qualitative analysis involving multicultural annotators with ties to both countries, we demonstrate that these disagreements can be attributed to cultural factors, such as differing social norms. Consequently, **Multi\({}^{3}\)Hate enables the analysis of multimodal models for cross-cultural hate speech detection across a range of diverse speaking cultures**.

Furthermore, we conduct experiments using 5 large VLMs in a zero-shot setting. Our experiments with English prompts reveal that these models consistently align more closely with annotations from the US than with those from other cultures, independent of the meme language. Specifically, out of 50 combinations of models, languages, and input variations, 42 demonstrate the highest alignment with US labels. Even when we switch the prompt language to the dominant language of a specific culture, we still observe similarly high alignment to US annotators. We therefore demonstrate that **VLMs align more closely with hate speech annotations from the US** than with those from non-English-speaking cultures, **even when the memes and prompts are presented in the dominant language of the other culture**. This trend poses a risk of marginalizing certain cultures, despite VLMs being used in their native languages, while simultaneously privileging US cultural perspectives.

## 2 Related Work

Multingual Hate SpeechWhile several text-based hate speech datasets exist in various languages Jeong et al. (2022); Mubarak et al. (2022); Yadav et al. (2023); Demus et al. (2022), there has been limited focus on creating a parallel hate speech dataset. The only notable example is Glavas et al. (2020), which developed a parallel text dataset in six languages.

Moreover, most multimodal hate speech datasets are in English Suryawanshi et al. (2020); Hossain et al. (2022); Bhandari et al. (2023); Kiela et al. (2020); Gomez et al. (2020), with limited resources available for other languages. Notable exceptions include a Bengali dataset by Karim et al. (2022), an Italian dataset by Miliani et al. (2020), and a Tamil dataset by Suryawanshi et al. (2020). To our knowledge, no parallel multimodal hate speech datasets exist. 1

Footnote 1: Gold et al. (2021) translated the English captions of the word corpus.

 

Cross-cultural Hate SpeechLee et al. (2024) are the first to analyze how cultural background affects hate speech annotations, finding that annotators' nationality significantly influence their annotation. However, their study is limited to English-speaking cultures due to its exclusively English dataset. Expanding to include non-English-speaking cultures could provide valuable insights for a more inclusive moderation system.

Cross-cultural VLMsSeveral studies have established benchmarks to probe cultural awareness in VLMs. For instance, researchers have focused on creating culturally diverse image descriptions, visual grounding, and benchmarks for cultural visual question-answering Liu et al. (2021); Cao et al. (2024); Burda-Lassen et al. (2024); Ye et al. (2024); Karamolegkou et al. (2024); Nayak et al. (2024). However, there has been little to no attention given to cross-cultural multimodal hate speech detection.

## 3 Dataset Construction

We now describe the pipeline used to create Multi3Hate, as illustrated in Figure 1.

Footnote 3: [https://memegenerator.net](https://memegenerator.net) (Accessed: May, 2024)

### Crawling

Image Templates & User CaptionsTo effectively modify captions in memes, we select memes with a simple structure, featuring captions at the top and/or bottom. For this purpose, we crawl a website4 where users can submit captions based on meme image templates provided by other users, collecting both the templates and user-generated captions.

Footnote 4: [https://memegenerator.net](https://memegenerator.net) (Accessed: May, 2024)

Sociopolitical CategoriesTo ensure our samples are influenced by cultural perceptions, we curate a list of culturally relevant templates by filtering them according to _sociopolitical categories_. These categories were discussed and decided among the authors. Each category is further divided into specific topics based on established criteria, see Appendix A.1 for more details.

For every topic, we generate relevant keywords. As an example, for the topic "Germany", we create the keyword "german" and match meme templates to these keywords based on their template names. Subsequently, we select the top three meme templates with the highest number of user captions. For details, see Appendix A.2. The final list, which includes a total of 5 categories, 15 topics and 45 image meme templates, is presented in Table 2.

Pre-FilteringWe ensure high-quality captions after crawling by implementing three pre-filtering

\begin{table}
\begin{tabular}{p{86.7pt}|p{99.6pt}} \hline \hline
**Category** & **Topic** \\ \hline Religion & Christiany Islam Judaism \\ \hline Nationality & Germany (DE) United States (US) Mexico (M) China (N) India (IN) \\ \hline Ethnicity & Asian Black Middle Eastern White \\ \hline LGBTQ+ & Transgender \\ \hline Political Issues & Law Enforcement Feminism \\ \hline \hline \end{tabular}
\end{table}
Table 2: Final list of topics across our 5 sociopolitical categories, with each topic featuring 3 image templates. For a comprehensive overview of the topics, associated search keywords, and the final number of samples, please refer to Table 13 in the Appendix.

Figure 2: Example of a parallel meme. The original English meme reads: “just in time ¡sep¿ for new year in cologne”. Only in Germany is this meme perceived as hate speech.2



steps to verify that the captions are: (1) in English, (2) multimodal, and (3) free from wordplay. Memes that can be classified solely based on their captions may lead to underutilization of the images by VLMs. Furthermore, wordplay can introduce translation errors and distort the intended meaning. We provide a detailed description of the prefiltering implementation in Appendix A.3. After pre-filtering, we have a total of 450 captions distributed across 45 image templates.

### Translation

We start by utilizing the v3 Google Translate API4 to generate machine translations of our 450 captions into four target languages: German, Spanish, Hindi, and Mandarin.

Footnote 4: [https://cloud.google.com/translate/docs/reference/rest/v3/projects/translateText](https://cloud.google.com/translate/docs/reference/rest/v3/projects/translateText) (Accessed: June, 2024)

Following this, we conduct two rounds of validation with two native speakers of the target language who are also fluent in English. Their task is to verify the accuracy of the translations and make any necessary corrections. Each annotator is provided with a detailed annotation guide, which can be found in Appendix A.4. We then recreate each meme by overlaying the new captions onto the image templates using the Python Pillow package Clark (2015), see Figure 2 for one example.

### Cross-Cultural Annotation

Annotator RecruitmentWe recruit annotators through Prolific5, ensuring the following: (1) they are native speakers of the target language; (2) have spent most of their lives in the target country; (3) their nationality aligns with the target country; (4) they identify as monocultural in relation to the target country and (5) they currently reside in the target country.6 We hire 445 annotators across all countries, maintaining a balanced representation of gender. All annotators gave explicit consent, were informed of the risks, and received a fair wage compensation (see 6 Ethics Statement). For a detailed demographic distribution, see Table 9 in Appendix.

Footnote 5: [https://www.prolific.com](https://www.prolific.com)

Pre-AnnotationTo ensure our dataset is balanced, we implement a pre-annotation stage, in which the dataset is evenly divided among our five target countries and annotated twice. Subsequently, we adjust the samples of _hate speech_ and _non-hate speech_ based on the annotation results. For further details, please refer to Appendix A.5.

In total, our final dataset consists of 300 parallel memes across five languages distributed across 45 templates, resulting in 1,500 memes.

Annotation ProcessBefore the annotation process begins, annotators receive a definition of hate speech7 along with examples in their native language, see Figure 10 in the Appendix. Each annotator is provided with the survey and samples - also in their native language - and is asked to label each meme (combination of image and embed caption) as _hate speech_, _non-hate speech_, or _I don't know_. For every sample and language, we collect a minimum of five annotations. The final label is determined through majority voting; when there is a tie between _hate speech_ and _non-hate speech_, we gather additional annotations until a majority consensus is reached. A detailed description of the survey design and quality checks can be found in Appendix A.6.

Figure 3: We provide examples from each category with hate speech annotations, highlighting cultural variability in perceptions and challenges for annotators in identifying targeted groups and stereotypes.

 



## 4 Analysis of Annotations

### Dataset Overview

We present examples in Figure 3.

Distribution of Hate SpeechWe report the proportion of _hate speech_ and _non-hate speech_ for each culture in Table 3. A significant lower number of samples were classified as _hate speech_ by US respondents compared to other cultures. For instance, Chinese annotators labeled approximately 63% of instances as _hate speech_, while US annotators labeled only 51% as such.

Inter-Annotator Agreement (IAA)We measure the IAA across hate speech annotations for each cultural group using Krippendorff's \(\alpha\) coefficient [20]. The values obtained are as follows: for the US, \(\alpha=0.4686\); for DE, \(\alpha=0.4537\); for MX, \(\alpha=0.3895\); for IN, \(\alpha=0.4018\); and, for CN, \(\alpha=0.4322\). These values are higher than or comparable to those reported in previous hate speech research [19, 23], demonstrating that there is a consensus on hate speech within each culture and pointing to the general validity of our annotation setup.

### Significance of Culture

To demonstrate that cultural background significantly affects multimodal hate speech annotation in our dataset, we closely follow lee2024multi.

Overall SignificanceTo assess the significance of cultural differences, we apply a chi-squared test to the hate speech annotations. The results reveal significant disparities (\(p<0.05\)) across cultures.

Label Agreement Across CulturesWe report the average pairwise label agreement across countries in Figure 3(a). The highest agreement is observed between the US and Germany (78%), while the lowest occurs between the US and India (67%).

Additionally, we calculate the proportion of samples with complete or partial agreement across countries: Only 44% of samples show agreement across all countries, four countries agree for 30%, and, for 26%, only three countries agree.

Comparison with Random Annotator GroupsTo demonstrate that the label disparity between cultures is not due to random variations among annotators, we create random annotator groups and calculate their agreement. Specifically, for each sample, we randomly select five annotations from across all cultures to form two groups. We then calculate the label agreement between these two random groups, repeating this process \(3\times 10^{4}\) times.

Figure 4: (a) Pairwise label agreement for all countries, ranked by average agreement. (b) A comparison of the top two and bottom two country pairs’ pairwise label agreement, along with the overall average across all countries, against randomly selected annotator groups. The results indicate that the lowest agreement pairs and the overall average differ significantly from random groups

\begin{table}
\begin{tabular}{l|c c|c} \hline \hline
**Country** & **Hate Speech** & **Non-Hate Speech** & **Total** \\ \hline US & 51\% & 49\% & 300 \\ DE & 59\% & 41\% & 300 \\ MX & 55\% & 45\% & 300 \\ IN & 60\% & 40\% & 300 \\ CN & 63\% & 37\% & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Proportion of _hate speech_ and _non-hate speech_ for each country. Chinese annotators labeled the majority of samples as hate speech, whereas US annotators identified the fewest instances as such.



We plot the resulting agreement histogram in Figure 3(b). To assess significance, we first confirm that the random group distribution follows a normal distribution using the D'Agostino-Pearson normality test [17], with a mean of \(0.79\) and standard deviation (\(\sigma\)) of \(0.019\).

We observe that the pairs with the lowest agreement, "US - IN" and "DE - IN", show significant deviations from the random annotator groups, with differences of \(-5.97\sigma\) and \(-5.47\sigma\), respectively. Additionally, the overall country average of 74% is significantly lower, by \(-2.70\sigma\). Upon closer inspection, all country pairs--except for the top three ("DE - MX", "US - DE", and "DE - CN"),--exhibit significantly lower agreement compared to the random groups. This analysis demonstrates that an individual's cultural background significantly influences their perception of multimodal hate speech.

### Analysis of Label Disagreements

Label Agreement Across CategoriesTo further analyze the disagreement between cultures, we examine the sociopolitical categories. Table 4 presents the pairwise agreement across countries for each category. The highest label agreement is observed in the "Religion" category, with an average of 78%, while the "LGBTQ+" category shows the lowest agreement at only 61%, which reflects deeper cultural sensitivities and differing norms. Interestingly, the "US - DE" pair has the highest agreement for every category, while the "US - IN" and "US - CN" pairs exhibit the lowest.

Annotators' Disagreement AnalysisWe conduct a qualitative analysis to examine _why_ cultures differ in their hate speech annotations, focusing on the pair with the highest disagreement: the USA and India. We recruit 7 annotators who are bilingual in Hindi and English, born in one of the two countries, currently residing in the other, and self-identifying as multicultural with ties to both cultures. These annotators are shown memes where the two cultures' annotations diverge, and we ask them to explain the reasons for their disagreement in free-text form. Using an inductive "bottom-up" approach, one author extracts keywords from each response, summarizing the text, giving us an initial codebook of 37 codes. A hired annotator then independently reassigns these established codes to the samples. We then establish 6 major themes.

As shown in Figure 5, "Sensitivity Around Minority Groups" and "Social Norms & Cultural Values" account for 53.6%, while "Historical & Political Context" and "Non-Existing Stereotypes" contribute 31%. Together, these four themes, totaling 84.6%, likely _reflect cultural differences_. Ideally, we aim to minimize the proportion of "Language Error", which accounts for only 5.2%. However, 10.3% fall under "Annotation Ambiguity", which may stem from annotation noise or reflect individual annotators' personal preferences. In conclusion, our cross-cultural disagreements can largely be attributed to cultural differences.

Figure 5: Distribution of disagreements between the USA and India. See Table 14 in the Appendix for detailed information on each category along with examples.

\begin{table}
\begin{tabular}{c|r|l|l} \hline \hline
**Category** & **AVG** & **Highest** & **Lowest** \\ \hline Religion & **78\%** & US-DE: 83\% & US-IN: 74\% \\ Nationality & 69\% & US-DE: 72\% & US-IN: 62\% \\ Ethnicity & 77\% & US-DE: 81\% & US-IN: 70\% \\ LGBTQ+ & 61\% & US-DE: 73\% & US-CN: 42\% \\ Political Issues & 75\% & US-DE: 77\% & US-CN: 61\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: We present the culture average pairwise agreement for each sociopolitical category, highlighting the culture pairs with the highest and lowest agreement.

 



## 5 Experiments

### Experimental Setup

Zero-Shot SetupWe evaluate VLMs using a zero-shot approach to detect hate speech. The task is framed as a multiple-choice format, where the model must select between two answers: (a) _hate speech_ and (b) _non-hate speech_. We implement three different prompt variations, each altering the order of answers (a) and (b). In total, we generate six prompts, maintaining English as the prompts' language unless otherwise specified. Additionally, we experiment with two input variations: (1) using only the image (IMG) and (2) incorporating the image caption (+CAPT) into the prompt, see Appendix B.2 for detailed prompts.

EvaluationWe present the average accuracy across all prompt variations, along with the standard deviation. To determine whether the observed differences are statistically significant, we apply the Wilcoxon rank-sum test Wilcoxon (1945), a non-parametric test that assesses whether one distribution tends to have higher values than another, without assuming normality.

ModelsWe evaluate several models, including GPT-4o8 OpenAI et al. (2024), Gemini 1.5 Pro9Georgiev et al. (2024), Qwen2-VL Wang et al. (2024), LLaVA OneVision Li et al. (2024), and InternVL2 Chen et al. (2023, 2024). For more details, see Appendix B.1.

\begin{table}
\begin{tabular}{l|c c c c c} \hline \multicolumn{6}{c}{**ItemI:**} \\ \hline _en_: IMG /+CAPT & **75.8\({}^{*}_{\pm 2.1}\) / 75.5\({}^{*}_{\pm 1.2}\) & 72.2\({}_{\pm 2.6}\) / 71.7\({}_{\pm 1.6}\) & 69.2\({}_{\pm 3.2}\) / 69.3\({}_{\pm 2.5}\) & 63.1\({}_{\pm 2.2}\) / 63.2\({}_{\pm 1.6}\) & 68.7\({}_{\pm 2.8}\) / 67.6\({}_{\pm 2.9}\) \\ _de_: IMG /+CAPT & **73.8\({}^{*}_{\pm 1.0}\) / 74.2\({}^{*}_{\pm 0.5}\)** & 71.6\({}_{\pm 1.6}\) / 71.3\({}_{\pm 1.8}\) & 69.0\({}_{\pm 2.2}\) / 69.0\({}_{\pm 1.7}\) & 63.8\({}_{\pm 1.7}\) / 63.6\({}_{\pm 1.1}\) & 67.2\({}_{\pm 2.0}\) / 66.7\({}_{\pm 1.7}\) \\ _es_: IMG /+CAPT & **74.7\({}^{*}_{\pm 1.0}\) / 75.6\({}^{*}_{\pm 1.5}\)** & 72.0\({}_{\pm 1.2}\) / 72.5\({}_{\pm 2.0}\) & 70.7\({}_{\pm 2.3}\) / 70.5\({}_{\pm 2.7}\) & 63.7\({}_{\pm 2.1}\) / 63.3\({}_{\pm 2.8}\) & 65.6\({}_{\pm 2.4}\) / 65.4\({}_{\pm 3.2}\) \\ _hi_: IMG /+CAPT & **69.7\({}^{*}_{\pm 1.4}\) / 71.3\({}^{*}_{\pm 1.4}\)** & 68.1\({}_{\pm 1.7}\) / 68.1\({}_{\pm 1.9}\) & 68.7\({}_{\pm 2.8}\) / 68.3\({}_{\pm 2.3}\) & 65.4\({}_{\pm 2.5}\) / 63.9\({}_{\pm 3.3}\) & 68.2\({}_{\pm 3.6}\) / 66.5\({}_{\pm 2.9}\) \\ _zh_: IMG /+CAPT & **71.3\({}^{*}_{\pm 0.7}\)** / 77.2\({}^{*}_{\pm 1.4}\) & 66.1\({}_{\pm 2.0}\) / 67.1\({}_{\pm 2.6}\) & 68.6\({}_{\pm 1.2}\) / 68.6\({}_{\pm 1.4}\) & 63.0\({}_{\pm 2.5}\) / 63.9\({}_{\pm 2.5}\) & 68.1\({}_{\pm 1.3}\) / 69.5\({}_{\pm 2.6}\) \\ \hline \multicolumn{6}{c}{**Gemini 1.5 Pro**} \\ \hline _en_: IMG /+CAPT & **70.9\({}^{*}_{\pm 2.1}\)** / **70.9\({}^{*}_{\pm 2.1}\)** & 69.7\({}_{\pm 2.1}\) / 69.7\({}_{\pm 2.1}\) & 68.6\({}_{\pm 1.0}\) / 68.6\({}_{\pm 1.0}\) & 68.6\({}_{\pm 1.0}\) / 68.6\({}_{\pm 1.0}\) & 65.0\({}_{\pm 0.7}\) / 65.0\({}_{\pm 0.7}\) & 66.7\({}_{\pm 2.7}\) / 66.7\({}_{\pm 2.7}\) \\ _de_: IMG /+CAPT & 69.5\({}_{\pm 1.3}\) / 70.9\({}_{\pm 1.7}\) & **70.7\({}_{\pm 1.6}\)** / 70.9\({}_{\pm 2.1}\) & 68.1\({}_{\pm 1.2}\) / 68.2\({}_{\pm 2.3}\) & 67.1\({}_{\pm 2.2}\) / 66.8\({}_{\pm 3.3}\) & 70.1\({}_{\pm 3.9}\) / 68.1\({}_{\pm 1.4}\) \\ _es_: IMG /+CAPT & **69.5\({}^{*}_{\pm 2.0}\)** / **70.8\({}^{*}_{\pm 3.2}\)** & 69.2\({}_{\pm 1.7}\) / 68.8\({}_{\pm 3.3}\) & 68.7\({}_{\pm 1.5}\) / 66.7\({}_{\pm 1.3}\) & 65.4\({}_{\pm 1.6}\) / 63.7\({}_{\pm 2.8}\) & 69.0\({}_{\pm 2.8}\) / 65.9\({}_{\pm 5.0}\) \\ _hi_: IMG /+CAPT & 61.4\({}^{*}_{\pm 1.6}\) / 63.5\({}_{\pm 3.1}\) & 61.4\({}_{\pm 8.6}\) / 65.4\({}_{\pm 4.6}\) & 63.9\({}_{\pm 4.6}\) / 65.7\({}_{\pm 3.7}\) & 62.0\({}_{\pm 7.5}\) / 61.2\({}_{\pm 4.5}\) & 57.8\({}_{\pm 1.4}\) / _66.0\({}_{\pm 6.7}\) \\ _zh_: IMG /+CAPT & 60.8\({}_{\pm 2.5}\) / 63.7\({}_{\pm 4.6}\) & 62.6\({}_{\pm 2.6}\) / 63.4\({}_{\pm 6.0}\) & **66.0\({}_{\pm 2.8}\) / 65.2\({}_{\pm 5.4}\)** & 60.4\({}_{\pm 6.0}\) / 60.7\({}_{\pm 6.6}\) & 63.1\({}_{\pm 6.3}\) / 62.8\({}_{\pm 7.4}\) \\ \hline \multicolumn{6}{c}{**Owen2-VL 72B**} \\ \hline _en_: IMG /+CAPT & **71.5\({}^{*}_{\pm 3.0}\)** / **70.8\({}^{*}_{\pm 4.8}\)** & 62.3\({}_{\pm 3.5}\) / 62.4\({}_{\pm 3.9}\) & 65.5\({}_{\pm 3.7}\) / 65.4\({}_{\pm 3.9}\) & 59.1\({}_{\pm 3.9}\) / 58.0\({}_{\pm 4.1}\) & 58.9\({}_{\pm 4.4}\) / 58.2\({}_{\pm 7.7}\) \\ _de_: IMG /+CAPT & **68.7\({}^{*}_{\pm 0.8}\)** / **70.1\({}^{*}_{\pm 2.2}\)** & 64.2\({}_{\pm 2.2}\) / 65.3\({}_{\pm 2.6}\) & 66.6\({}_{\pm 1.8}\) / 66.4\({}_{\pm 2.4}\) & 60.1\({}_{\pm 1.4}\) / 59.2\({}_{\pm 2.2}\) & 61.4\({}_{\pm 2.4}\) / 61.3\({}_{\pm 2.5}\) \\ _es_: IMG /+CAPT & **70.8\({}^{*}_{\pm 2.1}\)** / **71.2\({}^{*}_{\pm 2.3}\)** & 62.5\



### Dataset Sanity Check

We start by demonstrating the desired multimodality and evaluating the impact of different model scaling on our dataset. The aggregated results are presented in Table 5, while detailed model performances in Table 10 in the Appendix.

MultimodalityTo demonstrate multimodality, we compare models that utilize images as input with those that rely solely on captions. We present the top-performing models for English input in both settings, based on average accuracy.

The top-performing multimodal model achieves an accuracy of 75.8% with US labels, compared to 65.4% for the best unimodal model. The significant higher accuracy of the multimodal models underscores the strength of our dataset in supporting multimodal analysis.

ScaleWe compare the average performance of models within the same family, contrasting those with fewer than 10B parameters against those with more than 70B. On average, larger models exhibit better performance across all cultural labels, with the greatest improvement of 5.5% seen on US labels. Our subsequent analysis focuses exclusively on large VLMs (models with over 70B parameters).

### Prompting in English

In this section, we report experiments with VLMs in a zero-shot setting, using English as the prompt language. The results are presented in Table 6.

Strong Alignment with US Culture LabelAcross input and language variations, we observe that nearly all models perform best on US labels: out of 50 variations, 42 achieve their highest performance on US labels. In 39 cases, the performance difference compared to the worst-performing cultural label is statistically significant, and in 18 cases, the difference is significant even compared to the second-highest performing label.

For example, GPT-4o consistently performs best with US labels across all languages and input variants, achieving the highest accuracy on our dataset at 75.8% for English. The model shows a significant difference from the second-highest cultural label in 8 out of 10 variations.

Low Alignment with Indian Culture LabelIn contrast, the alignment between the model and hate speech annotations from Indian annotators is notably low, ranking among the bottom in accuracy across 30 out of 50 variants. Similarly, annotations from Chinese annotators also show low alignment, with 19 variants reflecting the lowest accuracy.

Comparison: IMG vs. +CAPAdding captions into the prompt improves performance in all languages except English, suggesting weaker OCR capabilities in VLMs for non-English text. For instance, LLaVA OneVision's accuracy on Hindi with US labels rises from 58.2% to 64.5% with captions.

### Prompting in Native Language

We experiment with adjusting the prompt language to match the dominant language of the target culture, as shown in Figure 11 in the Appendix. Incorporating captions in the prompt (+CAPT) typically enhances performance, so we focus on this setting with the best-performing open-source and proprietary models. Results are presented in Table 7

The results show mixed effects: e.g., switching to German improves the performance of GPT-4o by 0.3, while for Qwen2, it decreases by 0.7. However, none of the observed changes are statistically significant. Therefore, we conclude that **altering the prompt language to match the dominant language of a specific culture does not have a meaningful impact on aligning models**.

\begin{table}
\begin{tabular}{l|c c c c c} \hline  & **US** & **DE** & **MX** & **IN** & **CN** \\ \hline \multicolumn{6}{c}{**Multilingual Prompts: GPT-4o**} \\ \hline _de_ & **75.0***\({}^{*}_{=1.0}\)** & 71.6 ±\({}_{=1.9}\) & 69.4±\({}_{=2.0}\) & 63.7±\({}_{=1.9}\) & 67.2±\({}_{=1.5}\) \\ - \(\Delta\) & +0.8 & **+0.3** & +0.4 & +0.1 & +0.5 \\ \hline _es_ & **75.0***\({}^{*}_{=1.3}\)** & 73.8±\({}_{=1.1}\) & 70.3±\({}_{=1.8}\) & 64.1±\({}_{=1.1}\) & 67.3±\({}_{=2.6}\) \\ - \(\Delta\) & -0.6 & +1.3 & **–0.2** & +0.4 & +1.9 \\ \hline _hi_ & **72.8***\({}^{*}_{=0.9}\)** & 70.0±\({}_{=1.0}\) & 71.2±\({}_{=1.4}\) & 64.9±\({}_{=1.6}\) & 67.4±\({}_{=1.5}\) \\ - \(\Delta\) & +1.5 & +1.9 & +2.9 & **+1.0** & +0.9 \\ \hline _zh_ & **72.4***\({}^{*}_{=1.3}\)** & 66.4±\({}_{=2.5}\) & 69.3±\({}_{=2.1}\) & 63.7±\({}_{=2.8}\) & 70.2±\({}_{=3.2}\) \\ - \(\Delta\) & -0.5 & -0.7 & +0.7 & -0.2 & **+0.7** \\ \hline \end{tabular}
\end{table}
Table 7: Evaluation of adjusting the prompt language to match the dominant language of the respective culture. \(\Delta\) shows the difference between the multilingual prompt (+CAPT) and English prompt (+CAPT). The asterisk (*) in the \(\Delta\) row shows significant difference.



Even when the prompt's language is altered, the model continues to show high alignment with US labels. All variations significantly outperform the lowest-performing cultural variant with US culture label, and, for GPT-4o, they even significantly surpass the second-highest. This reinforces the idea that the **models are more aligned with US cultural norms, even when prompted in the dominant language of another culture**.

### Adding Country Information

Building on Lee et al. (2024), we align VLMs with the target culture by adding country information to the prompt. We report results only for the +CAPT setup and best models, as shown in Table 8.

Injecting country information generally decreases performance across target cultures, with the exception of Qwen2 in Hindi, which shows no change. For instance, adding "Germany" to the prompt results in a 2.0 and 2.4-point accuracy drop for GPT-4o and Qwen2-VL, respectively, though these decreases are not statistically significant. Therefore, we conclude that **adding country information does not positively impact performance in the target culture**.

## 6 Conclusion

We present the first multimodal, parallel, multilingual hate speech dataset, annotated by a multicultural set of annotators. This dataset contains 300 parallel meme samples across five languages and has been annotated for hate speech across five cultures. We show that cultural factors significantly impact multimodal hate speech annotations in our dataset. Additionally, we use this dataset to highlight that VLMs exhibit a strong cultural bias towards the US, independent on the image and prompt language.

### Limitation

While our dataset contains 300 samples across 5 languages--amounting to 1500 memes in total--the relatively small size reflects the challenges of generating high-quality translations and culturally diverse annotations. Expanding such a dataset is resource-intensive, both in terms of cost and labor.

Additionally, the dataset was sourced from a single website, primarily in English, and does not specifically target content from various cultural contexts. Furthermore, by selecting annotators from Prolific and using a single language per country, we introduce a degree of selection bias, as this method may not fully represent the complex cultural landscapes within each country.

We also recognize that equating culture with country is a limitation, as countries are often multicultural and multiethnic. For example, India is home to thousands of ethnic and tribal groups Thapar et al. (2024), and our approach does not fully capture this diversity.

Finally, while our work highlights cross-cultural differences in the perception of hate speech, understanding the root causes of these disagreements remains an open challenge. Although we offer a qualitative analysis of annotator disagreements, a comprehensive theory-driven analysis is still lacking. Developing a robust theoretical framework to explain these cultural variations could ultimately help the alignment of VLMs with specific cultural nuances, leading to more accurate and culturally sensitive hate speech detection systems.

### Ethics Statement

The annotators recruited through Prolific were compensated at a rate of PS10.65 per hour, in alignment with the minimum wage in the authors' country,

\begin{table}
\begin{tabular}{l|c c c c c} \hline \hline  & **US** & **DE** & **MX** & **IN** & **CN** \\ \hline \multicolumn{7}{c}{**Country Information: GPT-4o**} \\ \hline _en_ & **74.3***\({}^{*}_{1.0}\) & 68.0±1.7 & 66.1±1.0 & 60.1±1.2 & 63.4±2.2 \\ \cline{2-7} \(\Delta\) & **-1.2** & -3.7* & -3.2* & -3.1* & -2* \\ \hline _de_ & **73.5***\({}^{*}_{1.7}\)** & 69.3±2.0 & 66.9±1.2 & 62.7±1.4 & 64.8±1.7 \\ \cline{2-7} \(\Delta\) & -0.7 & **-2.0** & -2.1* & -0.9 & -2.4 \\ \hline _es_ & **72.9***\({}^{*}_{1.4}\)** & 70.2±1.4 & 67.7±1.7 & 61.6±2.2 & 64.2±2.5 \\ \cline{2-7} \(\Delta\) & -2.7* & -2.3* & **-2.8** & -1.7 & -1.2 \\ \hline _hi_ & **67.8***\({}^{*}_{2.1}\)** & 65.3±2.9 & 65.9±2.2 & 61.2±2.2 & 62.7±2.6 \\ \cline{2-7} \(\Delta\) & -3.5* & -2.8 & -3.4 & **-2.7** & -3.8 \\ \hline _zh_ & **69.9***\({}^{*}_{2.1}\)** & 65.8±3.2 & 67.0±1.3 & 62.4±3.1 & 68.1±3.2 \\ \cline{2-7} \(\Delta\) & -3.0* & -1.3 & -1.6 & -1.5 & **-1.4** \\ \hline \end{tabular} 
\begin{tabular}{l|c c c c c} \hline \hline _en_ & **70.7***\({}^{*}_{4.3}\) & 61.6±3.6 & 64.1±2.6 & 57.8±4.4 & 58.4±3.3 \\ \cline{2-7} \(\Delta\) & **-0.1** & -0.8 & -1.3 & -0.2 & +0.2 \\ \hline _de_ & **68.5***\({}^{*}_{2.2}\)** & 62.9±2.4 & 64.8±2.0 & 56.8±2.0 & 59.3±2.4 \\ \cline{2-7} \(\Delta\) & -1.6 & **-2.4** & -1.6 & -2.4 & -2.0 \\ \hline _es_ & **69.9***\({}^{*}_{2.6}\)** & 62.4±3.9 & 64.3±2.3 & 58.3±3.0 & 57.6±3.6 \\ \cline{2-7} \(\Delta\) & -1.3 & -1.9 & **-1.8** & -1.0 & -0.9 \\ \hline _hi_ & **62.9***\({}^{*}_{2.3}\)** & 58.7±4.0 & 22.2±3.9 & 56.1±3.2 & 54.7±3.7 \\ \cline{2-7} \(\Delta\) & -1.6 & +0.3 & +0.4 & **+0.0** & -0.1 \\ \hline _sh_ & **63.9***\({}^{*}_{2.0}\)** & 58.1±2.7 & 61.9±2.4 & 54.3±3.1 & 57.2±3.4 \\ \cline{2-7} \(\Delta\) & -2.8 & -0.8 & -1.7 & -2.9 & **-2.7** \\ \hline \end{tabular}
\end{table}
Table 8: Evaluation of injecting the country information. \(\Delta\) represents the difference between the prompt with country information injection and those without it. The asterisk (*) in the \(\Delta\) row shows significant difference.

 

ensuring fair payment. Prior to the start of annotations, the project received ethical approval from the lead author's institution. All annotators were thoroughly informed about the nature of the project, including warnings regarding potentially harmful and offensive content. Each annotator provided explicit consent before beginning their work, ensuring they were fully aware of the content and the purpose of their involvement.

We also acknowledge the potential risks associated with distributing our dataset. To mitigate these risks, we will establish clear terms of use that strictly prohibit any form of malicious exploitation. Additionally, we release the dataset in an anonymized format, ensuring that all user IDs and any personally identifiable information are removed to protect individual privacy.

We use AI assistants, specifically GPT-4o, to help edit sentences in our paper writing. Multi\({}^{3}\)Hate is licensed under CC BY-NC-ND 4.0.

## Acknowledgement

The work of Minh Duc Bui and Katharina von der Wense is funded by the Carl Zeiss Foundation, grant number P2021-02-014 (TOPML project). The work of Anne Lauscher is funded under the Excellence Strategy of the German Federal Government and the Federal States. We thank Sukannya Purkayastha, Pranav A, Yujie Ren, Zhu Luan, Timm Dill, Carlos Galarza, and Delia Rieger for helping with translations and feedback on non-English text. We also thank Kyung Eun Park, Carolin Holtermann and Abteen Ebrahimi for their helpful feedback and discussions.

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classify each caption as either non-wordplay or wordplay. Only captions unanimously classified as non-wordplay are selected.

### Translation Stage Details

To translate the captions by the Google Translate API, each caption is inputted with a separator " // " to clearly distinguish between the top and bottom text.

Furthermore, each human translator is provided with a detailed annotation guide outlining the criteria for what constitutes a correct translation and how corrections should be made. The annotation guidelines are shown in Figure 7.

### Pre-Annotation

Before beginning the main annotation process, we conduct a pre-annotation stage to balance the dataset. For this phase, we create a parallel multilingual meme dataset consisting of 450 samples. The dataset is divided into five equal parts, with each part assigned to annotators from a different cultural background. Each sample is annotated twice, with a total of 50 annotators involved--10 from each cultural group.

To achieve balance, we adjust the dataset so that 40% of the samples are labeled as hate speech, 40% as non-hate speech, and 20% where there was a tie between annotators. This process results in the final set of 300 samples.

### Hate Speech Survey Design

We use Google Forms10 to design and distribute our surveys. To create surveys in each target language, we use the v3 Google Translate API to translate the

Figure 6: Prompt for classification of multimodal hate speech in the pre-filtering stage.

\begin{table}
\begin{tabular}{l|c c c c c} \hline \hline  & USA & Germany & Mexico & India & China \\ \hline
**No. of Annotators** & 105 & 103 & 101 & 66 & 70 \\ \hline
**Gender** (\%) & & & & & \\ mile & 53.33 & 49.51 & 52.48 & 53.08 & 48.57 \\ female & 46.67 & 50.49 & 47.52 & 46.97 & 51.43 \\ non-binary & – & – & – & – & – \\ \hline
**Ethnicity (Simplified)** (\%) & & & & & \\ Asian & 1.9 & – & – & 93.94 & 97.14 \\ Black & 15.24 & – & – & – & – \\ White & 82.86 & 100.0 & 28.71 & – & 1.43 \\ Mixed & – & – & 41.58 & – & 1.43 \\ Other & – & – & 29.7 & 6.06 & – \\ \hline
**Level of Education** (\%) & & & & & \\ Below High School & – & – & 0.99 & – & – \\ High School & 24.76 & 22.33 & 9.9 & 3.03 & – \\ College & 20.95 & 16.5 & 17.82 & 3.03 & 7.14 \\ Barbiero & 37.14 & 38.93 & 60.4 & 71.12 & 32.66 \\ Master’s Degree & 14.29 & 19.42 & 9.9 & 22.73 & 48.57 \\ Doctorine & 2.86 & 2.91 & 0.99 & – & 11.43 \\ \hline
**Age** (\%) & & & & & \\
18-19 & 1.9 & 4.85 & 1.98 & 9.09 & 1.43 \\
20-29 & 28.57 & 64.08 & 74.26 & 60.61 & 60.0 \\
30-39 & 35.24 & 21.36 & 22.77 & 16.67 & 30.0 \\
40-49 & 17.14 & 8.74 & – & 9.09 & 5.71 \\
50-59 & 13.33 & 9.97 & 9.99 & 4.55 & 1.43 \\
60-69 & 3.81 & – & – & - & 1.43 \\ T0-79 & – & – & – & – & – \\
80-89 & – & – & – & – & – \\ \hline
**Political Orientation** (\%) & & & & & \\ Liberal/Perspective & 35.24 & 37.86 & 34.65 & 25.76 & 8.57 \\ Moderate Internal & 20.95 & 32.04 & 26.73 & 18.18 & 22.86 \\ Independent & 22.86 & 15.53 & 13.86 & 43.94 & 37.14 \\ Modeme Conservative & 10.48 & 21.62 & 14.85 & 9.09 & 12.86 \\ Concurative & 8.57 & 0.97 & 3.96 & 1.82 & – \\ Other & 1.9 & 0.97 & 5.94 & 1.52 & 18.57 \\ \hline
**Religia** (\%) & & & & & \\ None & 42.86 & 70.87 & 42.57 & – & 74.29 \\ Christian & 46.67 & 28.16 & 37.62 & 6.06 & 5.71 \\ Buddism & – & – & – & 3.03 & 8.57 \\ Islam & 1.9 & – & – & 21.21 & – \\ Juddism & – & – & – & – & – \\ Hindianian & – & – & – & 60.61 & – \\ Incision & 2.86 & 0.97 & 8.91 & 1.52 & 2.86 \\ Other & 5.71 & – & 10.89 & 7.58 & 8.57 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Demographics of annotators during the hate speech annotation phase.

Figure 7: Annotation guidelines for translators.

 

surveys, which are then reviewed and corrected by native speakers for accuracy. We then create fixed random parallel batches, which are then assigned to each annotator.

Each batch includes four attention checks: one explicit check, where annotators are required to select a specific pre-defined answer (see Figure 8), and three implicit checks. These implicit checks consist of samples presented as examples at the beginning of the survey, accompanied by explanations of why the samples are classified as non-hate speech or hate speech based on the given definition.

We only retain annotations where the explicit attention check is answered correctly, and at least two out of the three implicit checks are passed. After collecting five annotations per sample, we review the results for any ties that need resolution and create new batches accordingly.

### Terms of Use

Our research is conducted in the public interest under the GDPR, fulfilling the conditions for substantial public interest as academic research. We were unable to locate any Terms of Service on [https://memegenerator.net](https://memegenerator.net), and the contact information provided on the website appears to be outdated and non-functional. To ensure we respect the platform's rights, we are publishing Multi3Hate under the CC BY-NC-ND 4.0 license.

### Time Required for Dataset Development

Estimating the effort required to create such a dataset is challenging due to the multiple, often unforeseen, refinement stages involved. For instance, unexpected challenges--such as translating wordplay--necessitated filtering them out and revisiting the translation process, significantly increasing the time and effort required. Overall, the entire dataset creation process took approximately four months from start to finish.

## Appendix B Experiments Details

### Model Details

Table 12 lists the models and their sizes, all run on three H100 GPUs. Each large VLM processes five languages in around 1.5 hours. To support better text extraction from memes, images are resized to 512x512 pixels [Beyer, 2024]. For all models, we generate deterministic outputs and limit generation to 40 new tokens. For the Gemini 1.5 Pro model,

Figure 8: An example of an explicit attention check used in our survey.

Figure 9: All three prompt variations: The order of options (a) and (b) is switched to create a total of six variations. Brackets are optional, allowing for insertion of the caption (+CAPT Setting) or country information as described in Section 5.5.

 

we disable all safety settings to minimize rejected responses.

To derive binary classifications from the answers, we implement a custom keyword extraction. We relax the constraints on possible answers significantly, moving beyond a binary choice of "a" or "b". For instance, "non-hate" or "hate-speech" is also recognized as a valid response in our analysis. However, answers that are nonsensical are counted as incorrect.

### Prompts

We design prompts similar to those in Lee et al. (2024) and present the various prompt formulations in Figure 9. Additionally, the multilingual version of these prompts is shown in Figure 11.

## Appendix C Supervised Baseline

We experiment with a small supervised VLM baseline, training the Qwen2-VL 7B separately on each cultural label (i.e., culture-specific models) on only one prompt variation, using a 3-fold cross-validation approach due to the relatively small dataset size. We fine-tune our model using LoRA Hu et al. (2021), modifying only the Query and Value matrices, with a rank of 8 and an alpha value of 16. We employ a learning rate of \(2e-4\) with a constant learning rate schedule, training for three epochs with a batch size of 16. Additionally, we refrain from hyperparameter tuning to avoid overfitting on our validation folds, as our limited data prevents the creation of a separate test set. The results are presented in Table 11.

Overall, we observe high improvements across all countries, with the most notable gain of 7.1 points in Indian annotations. Moreover, the highest level of agreement with each culture-specific annotation is achieved by the model fine-tuned on the respective cultural labels. This suggests that each country's cultural perception can be effectively improved through supervised fine-tuning. We leave a more thorough analysis of the results and the exploration of advanced fine-tuning approaches for future work.

 



\begin{table}
\begin{tabular}{c|l|l|l|l|l|l|l|l|l|l|l|l|l|l|l|l|l|l|l|l|l|l|l|l l|l|l|l}  & \multicolumn{12}{l|}{PREFIX} & \multicolumn{3}{l|}{PROMPT \#1} & \multicolumn{3}{l|}{PROMPT \#1} & \multicolumn{3}{l|}{PROMPT \#2} & \multicolumn{3}{l|}{PROMPT \#2} & \multicolumn{3}{l|}{PROMPT \#3} & \multicolumn{3}{l|}{PROMPT \#4} & \multicolumn{3}{l|}{POSTPK} \\ \hline _DE_ & & Definition & von Haverde: Jede Art & Beurswerte, obes dieme & Ashand cines Menes am & Wurder de dnesse Meme ah & Mene: demago- & Kemminationa(1.) & L.die eine abernemente & Hans oider rick ist, mit & Gasem lineuten, beursurentete ob & Hamster breachernetnet? Gebe & Bechstuffung & Hsuelebruchner? Gebe & Bedstuffung & Hsuelebruchner? Gebe & Bedstuffung & Hsuelebruchner? Gebe & Bedstuffung & Hsuelebruchner? Gebe & Bedstuffung & Hsuelebruchner? Gebe & Bedstuffung & Hsuelebruchner? Gebe \\  & & & der exingeen dener que Gruppe aufrem & energhebenne Annourgeichten a und b & der vorgegebennen Anworder nun & mogellichenache & Aumel hautch. Anworder Ste mit & Bouchnauern & Bechstännen & Bechstännen & Bechtünnen & Bechtünnen & Bechtünnen & Bechtünnen & Bechtünnen & Bechtünnen & Bechtünnen & Bechtünnen & Bechtünnen & Bechtünnen & Bechtünnen & Bechtünnen & Bechtünnen & Bechtünnen \\  & & & weder, emberne



\begin{table}
\begin{tabular}{c|c|c} \hline \hline
**Topic** & **Keywords** & **Count** \\ \hline Christiarity & christ, jesus, priest & 21 \\ Islam & muslim, islam & 22 \\ Hinduism & hindu, hinduism & – \\ Buddhism & buddha, buddhist & – \\ Folk Religion & folk religion & – \\ Judaism & jew, judaism & 18 \\ \hline Germany & germany, german & 18 \\ United States & america, usa, american & 21 \\ Mexico & mexico, mexican & 20 \\ China & china, chinese & 21 \\ India & india, indian & 15 \\ \hline Asian & asia, asien & 20 \\ Black & black & 23 \\ Latine & latino, latine & – \\ Middle Eastern & middle+eastern, arab & 19 \\ White & white & 19 \\ \hline Lesbian & lesbian & – \\ Gay & gay & – \\ Bisexual & bisexual & – \\ Transgender & trans, transgender & 19 \\ Queer & queer & – \\ \hline Law Enforcement & police & 23 \\ Feminism & feminist & 21 \\ Immigration & immigrants & – \\ Racial Diversity & (already included) & – \\ LGBTQ+ & (already included) & – \\ \hline \hline \end{tabular}
\end{table}
Table 13: We conduct a keyword search based on the identified topics and report the final sample count in Multi\({}^{3}\)Hate. A “–” indicates that the topic did not meet our requirements.





\begin{table}
\begin{tabular}{l l p{142.3pt}} \hline
**Category** & **Example** & **Keywords** \\ \hline Historical \& Political Context & _In the U.S. it is much more acceptable due to Cold War politics, for individuals to decry Chinese communism as completely evil. This legacy did not affect India [...]_ & Historical Context of Hindu-Muslim Conflicts Historical Context of Discrimination against Latinos Political Tensions \\ \hline Sensitivity Around Minority Groups & _From the US context, this can be seen as mocking an immigrant or a person of color. From the Indian standpoint, this is not considered mocking as brown people are not a marginalized group in India._ & Sensitivity towards Immigrants Women’s Right Sensitivity to Class Distinctions LGBTR Acceptance and Rights \\ \hline Social Norms \& Cultural Values & _In the US culture, parents are expected to follow the society’s code of conduct towards the kids. In the Indian context, the father is the patriarch and can discipline the kids. This statement is insulting to the father._ & Social Norms Around Natity Social Norms Around Transportation Social Norms Around Diet Social Norms around Patriarchal Family Structure Cultural Norms of Politeness Cultural Norms Around Nutity Cultural Perception of Governance Cultural Perception of Gun Laws Cultural Perception of Police Authority Cultural Perception of Democracy Cultural Perception of War Cultural Perception of Sexual Violence Cultural Perception of Hard Labor Cultural Perception of Freedom of Speech Cultural Sensitivity to Religion Cultural Context of Poverty \\ \hline Non-Existing Stereotypes & _This meme uses the Asian stereotype [...] and hence is offensive in the US. This stereotype is non-existent in India [...]_ & Non-Existing Stereotypes \\ \hline Annotation Ambiguity & _[...] interviewers are not a protected minority [...]. I would have voted non-hate speech for both cultures._ & Translation Error \\ \hline Language Error & _The meaning translated to Hmid feels like [...] and can be reinterpreted [...]._ & \\ \hline \end{tabular}
\end{table}
Table 14: We present the major themes of disagreement, along with their associated keywords and examples of annotators’ comments.