Online Gender-Based Violence is an increasing problem, but existing datasets fail to capture the plurality of possible annotator perspectives or ensure representation of affected groups. In a pilot study, we revisit the annotation of a widely used dataset to investigate the relationship between annotator identities and underlying attitudes and the responses they give to a sexism labelling task. We collect demographic and attitudinal information about crowd-sourced annotators using two validated surveys from Social Psychology. While we do not find any correlation between underlying attitudes and annotation behaviour, ethnicity does appear to be related to annotator responses for this pool of crowd-workers. We also conduct initial classification experiments using Large Language Models, finding that a state-of-the-art model trained with human feedback benefits from our broad data collection to perform better on the new labels. This study represents the initial stages of a wider data collection project, in which we aim to develop a taxonomy of GBV in partnership with affected stakeholders.

There are two competing approaches for modelling annotator disagreement: distributional soft-labelling approaches (which aim to capture the level of disagreement) or modelling perspectives of individual annotators or groups thereof. We adapt a multi-task architecture which has previously shown success in modelling perspectives to evaluate its performance on the SEMEVAL Task 11. We do so by combining both approaches, i.e. predicting individual annotator perspectives as an interim step towards predicting annotator disagreement. Despite its previous success, we found that a multi-task approach performed poorly on datasets which contained distinct annotator opinions, suggesting that this approach may not always be suitable when modelling perspectives. Furthermore, our results explain that while strongly perspectivist approaches might not achieve state-of-the-art performance according to evaluation metrics used by distributional approaches, our approach allows for a more nuanced understanding of individual perspectives present in the data. We argue that perspectivist approaches are preferable because they enable decision makers to amplify minority views, and that it is important to re-evaluate metrics to reflect this goal.