  Online abuse directed towards women on the social media platform Twitter has
attracted considerable attention in recent years. An automated method to
effectively identify misogynistic abuse could improve our understanding of the
patterns, driving factors, and effectiveness of responses associated with
abusive tweets over a sustained time period. However, training a neural network
(NN) model with a small set of labelled data to detect misogynistic tweets is
difficult. This is partly due to the complex nature of tweets which contain
misogynistic content, and the vast number of parameters needed to be learned in
a NN model. We have conducted a series of experiments to investigate how to
train a NN model to detect misogynistic tweets effectively. In particular, we
have customised and regularised a Convolutional Neural Network (CNN)
architecture and shown that the word vectors pre-trained on a task-specific
domain can be used to train a CNN model effectively when a small set of
labelled data is available. A CNN model trained in this way yields an improved
accuracy over the state-of-the-art models.
