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The present paper contributes to the research on the activities of far-right actors on social media by examining the interconnections between far-right actors and groups on Telegram platform using network analysis. The far-right network observed on Telegram is highly decentralized, similarly to the far-right networks found on other social media platforms. The network is divided mostly along the ideological and national lines, with the communities related to 4chan imageboard and Donald Trump’s supporters being the most influential. The analysis of the network evolution shows that the start of its explosive growth coincides in time with the mass bans of the far-right actors on mainstream social media platforms. The observed patterns of network evolution suggest that the simultaneous migration of these actors to Telegram has allowed them to swiftly recreate their connections and gain prominence in the network thus casting doubt on the effectiveness of deplatforming for curbing the influence of far-right and other extremist actors.

VOX-Pol Blog Post.

VOX-Pol Blog post.

In recent years, radicalization is being increasingly attempted on video-sharing platforms. Previous studies have been proposed to identify online radicalization using generic social context analysis, without taking into account comprehensive viewer traits and how those can affect viewers’ perception of radicalizing content. To address the challenge, we examine QAnon, a conspiracy-based radicalizing group, and have designed a comprehensive questionnaire aiming to understand viewers’ perceptions of QAnon videos. We outline the traits of viewers that QAnon videos are the most appealing to, and identify influential factors that impact viewers’ perception of the videos.

VOX-Pol Blog Post.

Over the past few years, a number of new “fringe” communities, like 4chan or certain subreddits, have gained traction on the Web at a rapid pace. However, more often than not, little is known about how they evolve or what kind of activities they attract, despite recent research has shown that they influence how false information reaches mainstream communities. This motivates the need to monitor these communities and analyze their impact on the Web’s information ecosystem. In August 2016, a new social network called Gab was created as an alternative to Twitter. It positions itself as putting “people and free speech first'”, welcoming users banned or suspended from other social networks. In this paper, we provide, to the best of our knowledge, the first characterization of Gab. We collect and analyze 22M posts produced by 336K users between August 2016 and January 2018, finding that Gab is predominantly used for the dissemination and discussion of news and world events, and that it attracts alt-right users, conspiracy theorists, and other trolls. We also measure the prevalence of hate speech on the platform, finding it to be much higher than Twitter, but lower than 4chan’s Politically Incorrect board.