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

Toxicity in the Decentralized Web and the Potential for Model Sharing