Extremist Content

The Internet’s relatively unfettered transmission of information risks exposing individuals to extremist content. Using online survey data (N = 768) of American youth and young adults, we examine factors that bring individuals into contact with online material advocating violence. Combining aspects of social structure-social learning theory with insights from routine activity theory, we find that ...

Moderators of online discussion forums often struggle with controlling extremist comments on their platforms. To help provide an efficient and accurate tool to detect online toxicity, we apply word2vec’s Skip-Gram embedding vectors, Recurrent Neural Network models like Bidirectional Long Short-term Memory to tackle a toxic comment classification problem with a labeled dataset from Wikipedia Talk ...

By exploring lessons learned from Ethiopia and Finland, this article challenges two assumptions about online hate speech research. First, it challenges the assumption that the best way to understand controversial concepts such as online hate speech is to determine how closely they represent or mirror some underlying set of facts or state of affairs online ...

Online disruption efforts generally aim to reduce the availability of jihadist content. Yet, the speed and agility of jihadist movements online – a multiplatform approach which a co-author of this paper has previously described as a ‘swarmcast’ – has allowed groups to evolve in response to disruption efforts and find new ways to distribute content. This ...