Leading the Mastodon Herd: Analysing the Traits of Influential Leaders on a Decentralised Social Media Platform

With the development and growing use of social media platforms in the last two decades, platform architectures have driven how we notice, consume and share information. Whilst centralised social networks and the use of recommendation algorithms are a prominently used architecture, in recent years an alternative and novel framework has emerged aiming to offer users a non-commercial decentralised platform to distribute content. Run by users of the platform, Mastodon offers many of the benefits of traditional centralised approaches, however, with the absence of recommendation algorithms there is risk that these architectures could instead promote echo-chambers and the growth of disinformation. With this in mind, we collect a new large Mastodon dataset, consisting of three million connections between over a hundred thousand users. Modelling content using 68 conversational features, and measuring influence using twelve different metrics, we analyse the most common topics being discussed between influential users, the conversational features present in influential content, and the relationships between influence measurements. Our analysis finds a strong correlation between influence and negative traits at every network resolution, with positive and neutral traits in some cases being negatively correlated with influence. Our analysis also shows that influential users have a strong relationship with social/political commentary.