Hate Explained: Evaluating NER -Enriched Text in Human and Machine Moderation of Hate Speech (2025)

Abstract: Hate speech detection is vital for creating safe online environments, as harmful content can drive social polarization. This study explores the impact of enriching text with intent and group tags on machine performance and human moderation workflows. For machine performance, we enriched text with intent and group tags to train hate speech classifiers. Intent tags were the most effective, achieving state-of-the-art F1-score improvements on the IHC, SBIC, and DH datasets, respectively. Cross-dataset evaluations further demonstrated the superior generalization of intent-tagged models compared to other pre-trained approaches. Then, through a user study (N=100), we evaluated seven moderation settings, including intent tags, group tags, model probabilities, and randomized counterparts. Intent annotations significantly improved the accuracy of the moderators, allowing them to outperform machine classifiers by 12.9%. Moderators also rated intent tags as the most useful explanation tool, with a 41% increase in perceived helpfulness over the control group. Our findings demonstrate that intent-based annotations enhance both machine classification performance and human moderation workflows.

## introduction warning: this paper contains content that may be offensive or upsetting. social media platforms face persistent challenges in moderating harmful content, such as hate speech, which violates community guidelines and poses significant risks to user safety. although automated systems are critical for detecting policy violations, ambiguous cases and flagged content often require human moderators to make final decisions @xcite . given the overwhelming volume of content that requires review, improving the efficacy of both human and machine moderation processes is essential @xcite . recent advances in hate speech detection have highlighted the potential of annotations to improve both performance and model explainability @xcite @xcite . annotations highlighting key elements of hate speech, such as intent and group mentions, offer a promising direction for improving detection systems. however, their specific impact on machine classifiers and human moderation workflows remains underexplored. addressing this gap is essential to developing tools that are not only effective but also interpretable for end-users. to address this gap, we semantically enrich the text with intent and group annotations to evaluate their impact on hate speech detection (see figure 1 ). using a cross-dataset evaluation, our findings show that intent tags produce the most significant improvement, achieving state-of-the-art f1 score gains on the ihc @xcite , sbic @xcite and dh @xcite datasets, respectively. these datasets, recognized benchmarks in the field, address implicit hate, social bias, and power implications in language and dynamically generated hate speech content. crossdataset evaluations further demonstrated the superior generalization of intent-tagged models compared to other pre-trained approaches. to further validate these findings, we conducted a user study comparing seven experimental settings, including configurations with intent tags, group tags, model uncertainty, and randomized counterparts. the results show that intent annotations significantly improve the accuracy of human moderators, allowing them to outperform machine classifiers by 12.9%. moderators also perceived intent tags as the most helpful source of explanation, with a 41% increase in perceived help compared to the control group. this work makes the following contributions. the remainder of this paper is organized as follows. section 2 reviews related work. section 3 describes the study design. section 4 describes the methodology of the study. section 5 presents the results. section 6 concludes with a summary of contributions and future research directions. ## related work the explainability to detect hate speech has been studied @xcite @xcite @xcite . kim et al. (2022a) introduced masked rationale prediction (mrp) to improve detection by predicting human rationales, improving bias mitigation. mittal and singh (2023) proposed explainable models such as ktrain to improve the interpretability of classifiers. calabrese et al. (2024) investigated the usage of tags for explainable ai (xai), focusing primarily on human-side evaluation with user validation (@xmath0. our study expands on this by incorporating a larger-scale assessment of human and machine moderators. open-source dataset initiatives such as plead @xcite have provided intent annotations. in contrast, toxygen @xcite addresses adversarial hate speech but lacks the intent and group-span annotations essential for subtle moderation. @xcite highlighted the risks of misinterpreting explanations in moderation, which we address by integrating specific ner tags for intents and groups. recent methods such as conprompt's contrastive learning approach @xcite ) and hare's llm-based reasoning @xcite achieve competitive results in automated hate speech detection. our work extends these approaches by enriching text with intent and group tags and further differentiates them by directly evaluating their effectiveness in improving human moderation. beyond hate speech, explainability has been explored in other domains. in healthcare, prior work applied explainable and active learning approaches for document screening and evidence-based text classification @xcite . in education, explainable nlp techniques have supported moral discourse analysis and peer influence modeling @xcite . in low-resource machine translation, explainability and data curation strategies were used to support indigenous language processing @xcite . additionally, recent work has proposed enriching hate speech classification using named entity tags for identity groups @xcite , and visualization strategies leveraging attention weights in transformers have also been explored for text classification transparency @xcite . ## study design this work aims to evaluate two key objectives. first, we examine whether enriching text with tags, such as intents or mentioned groups, improves the performance of hate speech classifiers. following @xcite , we work with intent tags that capture the motivations behind the statements, including derogation, threats, hate crimes, comparisons, and animosity. in contrast, group tags identify mentions of demographic or social groups, ranging from neutral descriptors to pejorative terms that denigrate or dehumanize foot_1 . second, we investigate whether these tags help human moderators identify hate speech. this involves assessing the usefulness of enriched text for distinguishing hate from non-hate and determining which types of tags most effectively support moderation along with other variables, such as the model's certainty. ## conclusions this study demonstrated that the use of intent tags significantly improves the detection of hate speech. the evaluation in benchmark data confirmed that intent tags boost classifier performance to the state of the art. a user study showed that these tags improve human accuracy and serve as valuable explanation tools for the moderation of hate speech. future research could explore the extension of these annotations to multilingual contexts to broaden their effectiveness. in addition, efforts to incorporate human curation of intent tags provided by llms are essential to clarify the differences between human and ai data annotations. evaluation benchmarks for spanish sentence representations @xcite , as well as lightweight spanish language models such as albeto and dis-tilbeto @xcite , provide a promising foundation for expanding this work beyond english-centric approaches. furthermore, integrating entity-enriched hate speech detection into social network analysis pipelines-such as those enabled by toolkits like tsundoku (graells-garrido et al., 2025)-could support broader applications in content moderation and network-level intervention strategies.