cs.CLMar 24, 2026
SaveBeyond Hate: Differentiating Uncivil and Intolerant Speech in Multimodal Content Moderation
Organizations: Technical University of Munich
Abstract
Current multimodal toxicity benchmarks typically use a single binary hatefulness label. This coarse approach conflates two fundamentally different characteristics of expression: tone and content. Drawing on communication science theory, we introduce a fine-grained annotation scheme that distinguishes two separable dimensions: incivility (rude or dismissive tone) and intolerance (content that attacks pluralism and targets groups or identities) and apply it to 2,030 memes from the Hateful Memes dataset. We evaluate different vision-language models under coarse-label training, transfer learning across label schemes and a joint learning approach that combines the coarse hatefulness label with our fine-grained annotations. Our results show that fine-grained annotations complement existing coarse labels and, when used jointly, improve overall model performance. Moreover, models trained with the fine-grained scheme exhibit more balanced moderation-relevant error profiles and are less prone to under-detection of harmful content than models trained on hatefulness labels alone (FNR-FPR, the difference between false negative and false positive rates: 0.74 to 0.42 for LLaVA-1.6-Mistral-7B; 0.54 to 0.28 for Qwen2.5-VL-7B). This work contributes to data-centric approaches in content moderation by improving the reliability and accuracy of moderation systems through enhanced data quality. Overall, combining both coarse and fine-grained labels provides a practical route to more reliable multimodal moderation.
Figures & tables
Figure 1: Example memes from the hateful meme dataset that illustrate the concept of ’benign confounders’. Image above is a compilation of assets, including ©Getty Images.
| Annotation type | Labels | Count | % of original dataset |
| Coarse single-label | Hateful vs. Neutral | 9664 | 100% |
| Fine-grained multi-label | Intolerant vs. Tolerant Uncivil vs. Civil | 2030 | 21% |
Table 1: Two annotation schemes used in this study, with their respective coverage in the dataset.
| Incivility | Intolerance | |
| Sr. 1 vs. Sr. 2 | 0.88 (0.77) | 0.90 (0.78) |
| Sr. 1 vs. Jr. 1 | 0.71 (0.39) | 0.67 (0.37) |
| Sr. 2 vs. Jr. 1 | 0.69 (0.38) | 0.69 (0.42) |
Table 2: Agreement between senior annotators (Sr. 1 and Sr. 2) and junior annotator (Jr. 1). Table shows share of labels that agree between annotators with Cohen’s in brackets.
| Hateful | Intolerant | Uncivil | |
| Original (coarse) | 0.37 | - | - |
| Our (fine-grained) | 0.35 | 0.37 | 0.44 |
Table 3: Marginal distributions in the original dataset and our fine-grained subset.
| Tolerant | Intolerant | |
| Civil | 0.50 | 0.06 |
| Uncivil | 0.13 | 0.31 |
Table 4: Joint distribution of incivility and intolerance labels in our fine-grained subset.
Figure 2: Evaluation pipeline. A meme–text pair is passed to the VLM (e.g. LLaVA-1.6) along with a prompt asking whether the content is “Hateful” or “Neutral”. The model generates a binary classification based on both visual and textual input. Image above is a compilation of assets, including ©Getty Images.
| LLaVA-1.6-Mistral-7B | Qwen2.5-VL-7B | GPT-5.1 | ||||||
| Setup | Finetune Split | Evaluation Split | Acc. | F1 | Acc. | F1 | Acc. | F1 |
| Zero-shot | - | Hateful | 0.68 | 0.68 | 0.68 | 0.68 | 0.80 | 0.80 |
| - | Uncivil | 0.61 | 0.60 | 0.56 | 0.51 | 0.74 | 0.74 | |
| - | Intolerant | 0.49 | 0.46 | 0.58 | 0.56 | 0.81 | 0.81 | |
| Baseline | Hateful | Hateful | 0.73 | 0.67 | 0.77 | 0.74 | - | - |
| Uncivil/Intolerant | Uncivil | 0.78 | 0.77 | 0.77 | 0.76 | - | - | |
Table 5: Multi-model comparison for accuracy (Acc.) and weighted F1 score (F1) across all learning setups. Best results per model and evaluation split are highlighted in bold.
| Model | Task | FPR | FNR | FNR-FPR |
| GPT-5.1 | Hateful | 0.22 | 0.17 | -0.05 |
| GPT-5.1 | Intolerance | 0.21 | 0.16 | -0.05 |
| LLaVA-1.6-Mistral-7B | Hateful | 0.01 | 0.75 | 0.74 |
| LLaVA-1.6-Mistral-7B | Intolerance | 0.09 | 0.52 | 0.42 |
| Qwen2.5-VL-7B | Hateful | 0.04 | 0.58 | 0.54 |
| Qwen2.5-VL-7B | Intolerance | 0.09 | 0.36 | 0.28 |
Table 6: False positive rate (FPR) and false negative rate (FNR) across models and label granularity when detecting harmful content. Error asymmetry (FNR-FPR) is smaller with the fine-grained annotation scheme.
| Operationalization | Match rate | Correlation |
| Hateful = Intolerant | 0.89 | 0.76 |
| Hateful = Intolerant AND Uncivil | 0.88 | 0.73 |
| Hateful = Intolerant OR Uncivil | 0.81 | 0.65 |
| Hateful = Uncivil | 0.80 | 0.59 |
Table 7: Comparison of alternative operationalizations relating hatefulness to incivility and intolerance. Match rate indicates the proportion of cases where the label implied by a given operationalization matches the original hatefulness label. Correlation is the phi coefficient (equivalent to Pearson’s for binary variables). All associations are statistically significant according to a chi-squared test of independence at .
| Category | n | P(hateful) | |
| Incivility | |||
| Attacks | 106 | 0.91 | 0.27 |
| Vulgar | 537 | 0.60 | 0.31 |
| Aspersions | 45 | 0.56 | 0.06 |
| Civil | 1140 | 0.10 | -0.59 |
| Intolerance | |||
Table 8: Empirical hatefulness probability conditioned on fine-grained categories and coefficient (majority vote aggregation).
Appendix figures & tables10 assets
Supplementary material from the paper’s appendix.
Appendix
| Nr. | Form of Incivility | Text | Visual |
| 0 | Civil | Absence of incivility. | Absence of offensive, hostile, or degrading imagery. |
| 1 | Profane or vulgar | Use of explicit profanities or vulgarities, regardless of whether they are directed at a person or entity. | Images containing profanity, obscene gestures, or crude/vulgar symbolism. Sexual or violent images aimed at intimidating, shocking, or degrading public discourse. |
| 2 | Attacks | Derogatory or pejorative language directed at specific individuals or groups. These can focus on personal characteristics (e.g., appearance), traits, character, or choices. | Images targeting identifiable individuals or demographic groups with pejorative, mocking, or dehumanising intent. |
| 3 | Aspersions | Attacks targeting groups, organizations, or institutions aiming to undermine credibility, legitimacy, or moral standing. | Images delegitimising institutions, policies, or parties through ridicule, slurs, or hostile visual metaphors. |
Table 9: Subtypes of Incivility in Text and Visual Content. Used in the annotation scheme.
| Nr. | Form of Intolerance | Description |
| 0 | Tolerant / Neutral | Absence of expressions of intolerance. |
| 1 | Threats to Individual Rights | Claims or images where certain groups do not have equal civil, political, or human rights. |
| 2 | Intolerance Toward Political Positions | Delegitimizing opposing political views or calling for elimination of political actors. |
| 3 | Racism | Discriminatory, stereotypical, hateful, or prejudicial speech toward racial minorities. |
| 4 | Social/Economic Intolerance | Discriminatory, stereotypical, hateful, or prejudicial speech toward others based on education level, social status, or income. |
| 5 | Gender and Sexual Intolerance | Discriminatory, stereotypical, hateful, or prejudicial speech toward women and/or LGBTQ+ individuals based on gender status or sexual orientation. |
Table 10: Subtypes of Intolerance in Text Content. Used in the annotation scheme.
Figure 3: Validation accuracy for hatefulness prediction across different shares of annotated training data.
Figure 4: Frequency of fine-grained incivility (left) and intolerance (right) labels across all annotations before aggregation. A single meme may receive multiple labels.
| Setup | Model | LoRA r | LoRA | Dropout | Learning Rate | Weight Decay | Epochs |
| Coarse | Qwen2.5-VL-7B | 64 | 128 | 0.0 | 2e-3 | 0.05 | 2 |
| LLaVA-v1.6-Mistral-7B | 64 | 128 | 0.05 | 2e-4 | 0.05 | 2 | |
| Fine | Qwen2.5-VL-7B | 128 | 256 | 0.1 | 2e-3 | 0.05 | 2 |
| LLaVA-v1.6-Mistral-7B | 64 | 128 | 0.05 | 5e-4 | 0.0 | 2 | |
| Joint | Qwen2.5-VL-7B | 64 | 128 | 0.0 | 2e-3 | 0.05 | 3 |
| LLaVA-v1.6-Mistral-7B | 128 | 256 | 0.05 | 5e-4 | 0.1 | 2 |
Table 11: Hyperparameters for all models across training setups.
| LLaVA-1.6-Mistral-7B | Qwen2.5-VL-7B | |||||
| Setup | Finetune Split | Evaluation Split | Acc. | IPR | Acc. | IPR |
| Coarse | Hateful | Hateful | 0.64 ( 0.05) | 0.00 ( 0.00) | 0.65 ( 0.15) | 0.15 ( 0.02) |
| Fine | Uncivil/Intolerant | Uncivil | 0.75 ( 0.02) | 0.00 ( 0.00) | 0.81 ( 0.03) | 0.05 ( 0.07) |
| Intolerant | 0.77 ( 0.02) | 0.76 ( 0.08) | ||||
| Joint | Uncivil/Intolerant/Hateful | Hateful | 0.73 ( 0.03) | 0.01 ( 0.01) | 0.68 ( 0.09) | 0.12 ( 0.01) |
| Uncivil | 0.75 ( 0.04) | 0.77 ( 0.02) | ||||
Table 12: Average validation accuracy (Acc.) and invalid prediction rate (IPR) across models and training setups using 5 different seeds. Standard deviation reported in brackets.
| Category | Count | Share |
| Civil | 3552 | 0.58 |
| Vulgar | 1623 | 0.27 |
| Attacks | 603 | 0.10 |
| Aspersions | 323 | 0.05 |
| Total | 6101 | 1.00 |
Table 13: Distribution of incivility categories in the dataset.
| Category | Count | Share |
| Tolerant | 3463 | 0.52 |
| Threats to Rights | 56 | 0.01 |
| Political Intolerance | 255 | 0.04 |
| Racism | 815 | 0.12 |
| Social Intolerance | 25 | 0.00 |
| Gender Intolerance | 615 | 0.09 |
Table 14: Distribution of intolerance categories in the dataset.
Figure 5: Hatefulness empirical probability conditioned on each category. Error bars represent the Wilson Score Interval at 0.05 significance level.
Figure 6: Odds ratios from a logistic regression of the hateful label on fine-grained incivility and intolerance category probabilities. Points to the right of the dashed line ( ) indicate a positive association with hatefulness. Error bars denote 95% confidence intervals.