  There is an increase in the proliferation of online hate commensurate with
the rise in the usage of social media. In response, there is also a significant
advancement in the creation of automated tools aimed at identifying harmful
text content using approaches grounded in Natural Language Processing and Deep
Learning. Although it is known that training Deep Learning models require a
substantial amount of annotated data, recent line of work suggests that models
trained on specific subsets of the data still retain performance comparable to
the model that was trained on the full dataset. In this work, we show how we
can leverage influence scores to estimate the importance of a data point while
training a model and designing a pruning strategy applied to the case of sexism
detection. We evaluate the model performance trained on data pruned with
different pruning strategies on three out-of-domain datasets and find, that in
accordance with other work a large fraction of instances can be removed without
significant performance drop. However, we also discover that the strategies for
pruning data, previously successful in Natural Language Inference tasks, do not
readily apply to the detection of harmful content and instead amplify the
already prevalent class imbalance even more, leading in the worst-case to a
complete absence of the hateful class.
