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VOX-Pol Blog post.

VOX-Pol blog post

It is only a matter of time before terrorists begin routinely using Twitter, Instagram, and other services in ongoing operations. We have already seen this in a limited manner from al-Shabaab, which tweets its #JihadDispatches on recent battles. But those delivery mechanisms are unlikely to replace the forums as the main environment for conversation and information distribution among jihadis. Twitter and the like provide a more public platform than a password-protected forum, but one critical utility of forums for jihadis is the ability to have relatively private conversations.

This article seeks to examine, quantitatively and qualitatively, one week of official media releases of the Islamic State (IS). Due to the breadth of IS official media releases, this provides a snapshot upon which to better understand the different styles and messaging streams IS releases on a weekly basis. The article shows that IS produces much more material, and on a broader range of topics, than what gets reported in the mainstream media. Execution videos make up just a fraction of the overall output and are dwarfed by the number of IS productions on military affairs, governance, preaching, moral policing, and other themes. The analysis also shows that IS relies very heavily on visual as opposed to text-based propaganda, and that most of its military activities take place in Iraq, not Syria.

In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, and empirical research. Despite a large number of emerging scientific studies to address the problem, a major limitation of existing work is the lack of comparative evaluations, which makes it difficult to assess the contribution of individual works. This paper introduces a new method based on a deep neural network combining convolutional and gated recurrent networks. We conduct an extensive evaluation of the method against several baselines and state of the art on the largest collection of publicly available Twitter datasets to date, and show that compared to previously reported results on these datasets, our proposed method is able to capture both word sequence and order information in short texts, and it sets new benchmark by outperforming on 6 out of 7 datasets by between 1 and 13% in F1. We also extend the existing dataset collection on this task by creating a new dataset covering different topics.

Content regulation and censorship of social media platforms is increasingly discussed by governments and the platforms themselves. To date, there has been little data-driven analysis of the effects of regulated content deemed inappropriate on online user behavior. We therefore compared Twitter — a popular social media platform that occasionally removes content in violation of its Terms of Service — to Gab — a platform that markets itself as completely unregulated. Launched in mid 2016, Gab is, in practice, dominated by individuals who associate with the “alt right” political movement in the United States. Despite its billing as “The Free Speech Social Network,” Gab users display more extreme social hierarchy and elitism when compared to Twitter. Although the framing of the site welcomes all people, Gab users’ content is more homogeneous, preferentially sharing material from sites traditionally associated with the extremes of American political discourse, especially the far right. Furthermore, many of these sites are associated with state-sponsored propaganda from foreign governments. Finally, we discovered a significant presence of German language posts on Gab, with several topics focusing on German domestic politics, yet sharing significant amounts of content from U.S. and Russian sources. These results indicate possible emergent linkages between domestic politics in European and American far right political movements. Implications for regulation of social media platforms are discussed.