Eyes Don’t Lie: Subjective Hate Annotation and Detection with Gaze (2024)

Abstract: Hate speech is a complex and subjective phenomenon. In this paper, we present a dataset (GAZE4HATE) that provides gaze data collected in a hate speech annotation experiment. We study whether the gaze of an annotator provides predictors of their subjective hatefulness rating, and how gaze features can improve Hate Speech Detection (HSD). We conduct experiments on statistical modeling of subjective hate ratings and gaze and analyze to what extent rationales derived from hate speech models correspond to human gaze and explanations in our data. Finally, we introduce MEANION, a first gaze-integrated HSD model. Our experiments show that particular gaze features like dwell time or fixation counts systematically correlate with annotators’ subjective hate ratings and improve predictions of text-only hate speech models.

## introduction hate speech is a real threat that harms individuals, groups, and societies in a profound way. even though research in nlp has developed many different datasets and models for hsd @xcite , the accurate modeling of hate speech is far from being solved @xcite . one of the key challenges in this area is that the definition and annotation of hate speech are highly complex and subjective, depending on the topic and domain of hate as well as on the individual annotators' backgrounds and biases @xcite @xcite . this combines with the fact that state-of-the-art hsd models are typically designed as black-box neural models that are well-known to pick up superficial, dataset-dependent patterns rather than learning a generalizable model of the underlying task. therefore, it is still an open question of how to handle subjective variation in human annotations and detection of hate speech. this paper contributes a new dataset (gaze4hate) that provides gaze and annotations from hate speech annotators, illustrated in figure 1 . we recorded the eye movements of annotators while they read statements, which were carefully controlled and constructed. this was followed by the annotation of hatefulness. annotators' gaze provides us with an extremely rich signal of the subjective cognitive processes involved in human hate speech evaluation while reading. in this paper, we explore whether subjective hatefulness rating can be predicted by the gaze of an annotator, and whether gaze features can be used to evaluate and improve hsd models. generally, the nlp community has recently started to leverage eye-tracking data as a means of analyzing the internal mechanisms in transformer language models as elaborated on in section 2.1. to the best of our knowledge, however, there is no available dataset of human reading of hate speech. other work along these lines has adopted so-called rationale annotations, where annotators mark text spans that they consider indicative of their labeling decisions (e.g. @xcite ; @xcite ). these rationales can be used to measure the plausibility and explainability of model decisions, by testing whether model-internal weights and gradients correlate with or even predict these human rationales @xcite ). yet, to date, it is unclear how rational annotations compare to gaze signals recorded during plain reading for the task of hate speech classification. our gaze4hate data closes this gap, as our annotators did not only rate texts for hatefulness but also annotated token-level rationales for their ratings. figure 1 shows an example that illustrates human gaze and rationales aligned with a model's rationale. our analyses and experiments center around the following research questions: rq1 do gaze features provide robust predictors for subjective hate speech annotations? rq2 how do gaze features correlate with human and model rationales? rq3 are gaze features useful for enriching lms for hsd? we address the first question by conducting statistical modeling on our collected eye-tracking and annotation data @xcite ). to answer the second question, we evaluate a range of existing hsd models on our data, comparing models' and humans' rationales to human gaze (section 5). section 6 presents the meanion model, which integrates text-based hsd with gaze features. in sum, our experiments show that particular gaze features like dwell time or fixation counts systematically differ with respect to annotators' subjective hate ratings. models' rationales, however, correlate more with explicit, annotated rationales than with annotator gaze. finally, in some settings, adding gaze features improves predictions of text-only hate speech models more than human rationales do. ## gaze4hate dataset we collected a hate speech annotated dataset that provides information from three different sources: hatefulness ratings of text w.r.t. gender, eye movements during plain readings of the statements, and explicit rationales marked by annotators. in this section, we explain the design of the dataset. ## analysis of annotators' gaze we start with testing whether the gaze parameters show significant differences among the subjective hate categories. we use anova tests using the ols library in r on the continuous gaze features. on the categorical gaze features, we utilized chi-square tests. multiclass comparison is conducted among hate, neutral and positively rated statements. the binary classification (similar to many existing hate speech classifiers) involves hate and non-hate categories. the non-hate category consists of both neutral and positive statements. for each gaze feature, we checked whether there is a significant main effect of subjective hate categories on the gaze features. six out of 13 features consistently show significant differences with high f-score values between the subjective hate ratings for multiclass (hate, neutral, and positive) and for binary comparisons (hate and no hate): all features that are significant in the multiclass condition are also significant in the binary one, but not the other way around. this indicates that merging neutral and positive categories has a negative impact on the statistical difference. fixation-count, dwell-time and first-run-fixation-count are showing higher f-scores in the binary comparison. tukey's tests for pairwise comparisons indicate that the differences in the fixation and dwell time originate from the difference between the hate vs. neutral and hate vs. positive conditions, while there is no difference between neutral and positive conditions. on the other hand, differences in the pupil size related parameters originate from difference in neutral conditions to hate and positive conditions without showing a significant difference between the latter two. this also confirms the theory of pupil size being more sensitive to the magnitude of the emotion rather than its polarity @xcite . ## hsd models and rationales in this section, we evaluate several hate speech detection (hsd) models on our gaze4hate dataset to answer rq2, which are described in section 5.1. we not only evaluate classification performance (section 5.2), but also measure the plausibility and explainability of model decisions by looking into the model rationales and compare them with the human rationales and gaze features (section 5.3). ## meanion -a gaze-integrated baseline model in this section, we explore whether gaze features improve pretrained and finetuned models on classifying hate speech (rq 3). we introduce the first member of our new family of gaze-integrated hsd models (meanions). ## conclusion we introduce a rich dataset of human readings of hate speech. our gaze4hate dataset is enriched with gaze features and subjective hatefulness ratings collected from 43 participants on 90 sentences (3870 unique subjective annotation instances). we compare subjective human hate ratings, human gaze and human rationales with hate speech models rationales. by doing so, we also experiment with various model explanation methods and compare their performance in aligning with human behaviour. the human attention values (represented with a set of gaze features and rationales) are a highly valuable source not only for evaluating the models, but also for training them with cognitively guided attention mechanisms @xcite @xcite . in ad-dition, we also introduce the first gaze-integrated hate speech model (meanion), which successfully shows the contribution of gaze features on subjective hate speech classification.