  Hate speech is regarded as one of the crucial issues plaguing the online
social media. The current literature on hate speech detection leverages
primarily the textual content to find hateful posts and subsequently identify
hateful users. However, this methodology disregards the social connections
between users. In this paper, we run a detailed exploration of the problem
space and investigate an array of models ranging from purely textual to graph
based to finally semi-supervised techniques using Graph Neural Networks (GNN)
that utilize both textual and graph-based features. We run exhaustive
experiments on two datasets -- Gab, which is loosely moderated and Twitter,
which is strictly moderated. Overall the AGNN model achieves 0.791 macro
F1-score on the Gab dataset and 0.780 macro F1-score on the Twitter dataset
using only 5% of the labeled instances, considerably outperforming all the
other models including the fully supervised ones. We perform detailed error
analysis on the best performing text and graph based models and observe that
hateful users have unique network neighborhood signatures and the AGNN model
benefits by paying attention to these signatures. This property, as we observe,
also allows the model to generalize well across platforms in a zero-shot
setting. Lastly, we utilize the best performing GNN model to analyze the
evolution of hateful users and their targets over time in Gab.
