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.

A significant gap remains in our understanding of the types of users who utilise online extremist platforms, as well as how their activity on these platforms influences the radicalisation of others and the dissemination of extremist content online. Our research addresses this gap by focusing on the Parler social network, one of the largest social media platforms used by the extreme far-right, boasting a reported 15 million total users as of January 2022. We present an exploration of the Parler social network, specifically reviewing the roles of users in the network and the types of activity and content shared on the platform. Our methodology provides a novel examination of Parler using tools that have previously been tested to understand other extremist groups.

There is an increasing demand for expert analysis of cybercriminal communities. Cybercrime is continually becoming more complex due to the rapid development of digital technologies, on the one hand, in new types of criminal activity, such as hacking, distributing malware and DDoS attacks, and on the other hand, in digitised forms of more traditional crimes, such as email scams, phishing, identity theft, and cryptographically secured black markets. Tackling this broad array of behaviour requires tool support for multi-disciplinary investigations, and a connecting framework that can adjust flexibly to changes in the populations being studied. In this work, we present AMoC, a multi-faceted machine learning toolkit that combines structured queries, anomaly detection, social network analysis, topic modelling and accounts recognition to enable comprehensive analysis of cybercriminal communities and users. The toolkit enables the extraction of findings regarding the motivations, behaviour and characteristics of offenders, and how cybercriminal communities react to interventions such as arrests and take-downs. In our demonstration, the toolkit is deployed to analyse over 150,000 accounts from 35 underground marketplaces.

Identity resolution capability for social networking profiles is important for a range of purposes, from open-source intelligence applications to forming semantic web connections. Yet replication of research in this area is hampered by the lack of access to ground-truth data linking the identities of profiles from different networks. Almost all data sources previously used by researchers are no longer available, and historic datasets are both of decreasing relevance to the modern social networking landscape and ethically troublesome regarding the preservation and publication of personal data. We present and evaluate a method which provides researchers in identity resolution with easy access to a realistically-challenging labelled dataset of online profiles, drawing on four of the currently largest and most influential online social networks. We validate the comparability of samples drawn through this method and discuss the implications of this mechanism for researchers as well as potential alternatives and extensions.