  The "Decentralised Web" (DW) is an evolving concept, which encompasses
technologies aimed at providing greater transparency and openness on the web.
The DW relies on independent servers (aka instances) that mesh together in a
peer-to-peer fashion to deliver a range of services (e.g. micro-blogs, image
sharing, video streaming). However, toxic content moderation in this
decentralised context is challenging. This is because there is no central
entity that can define toxicity, nor a large central pool of data that can be
used to build universal classifiers. It is therefore unsurprising that there
have been several high-profile cases of the DW being misused to coordinate and
disseminate harmful material. Using a dataset of 9.9M posts from 117K users on
Pleroma (a popular DW microblogging service), we quantify the presence of toxic
content. We find that toxic content is prevalent and spreads rapidly between
instances. We show that automating per-instance content moderation is
challenging due to the lack of sufficient training data available and the
effort required in labelling. We therefore propose and evaluate ModPair, a
model sharing system that effectively detects toxic content, gaining an average
per-instance macro-F1 score 0.89.
