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• Social media companies should recognise the political importance of counterterrorist financing (CTF) by explicitly reflecting the priorities of the UN Security Council and the Financial Action Task Force (FATF) in their policies, strategies and transparency reports.
• Furthermore, social media companies identified as being at high risk of exploitation should update their terms of service and community standards to explicitly reference and outlaw terrorist financing (consistent with universally applicable international law and standards such as those of the FATF) and actions that contravene related UN Security Council resolutions and sanctions.
• Social media companies should clearly demonstrate that they understand and apply appropriate sanctions designations; at the same time, policymakers should ensure that sanctions designations include, where possible, information such as email addresses, IP addresses and social media handles that can support sanctions implementation by social media companies. The more granular the information provided by governments on designated entities, the more efficiently the private sector can comply with sanctions designations.
• Social media companies should more tightly control functionality to ensure that raising terrorist funding through social media videos, such as big-brand advertising and Super Chat payments, is disabled.
• Researchers and policymakers should avoid generalisations and make a clear distinction between forms of social media and the various terrorist-financing vulnerabilities that they pose, recognising the different types of platforms available, and the varied ways in which terrorist financiers could abuse them.
• Policymakers should encourage both inter-agency and cross-border collaboration on the threat of using social media for terrorist financing, ensuring that agencies involved are equipped with necessary social media investigative capabilities.
• International law enforcement agencies such as Interpol and Europol should facilitate the development of new investigation and prosecution standard operating procedures for engaging with operators of servers and cloud services based in overseas jurisdictions to ensure that necessary evidence can be gathered in a timely fashion. This would also encourage an internationally harmonised approach to using social media as financial intelligence.
• Policymakers should encourage the building of new, and leveraging of existing, public–private partnerships to ensure social media company CTF efforts are informed and effective.

The proliferation of social media has created a terrorist finance vulnerability due to the ease with which propaganda can be spread, promoting fund-raising for a certain cause. Social media companies recognize the importance of preventing violent extremist and terrorist content, but less attention is paid to their fund-raising role. As well as presenting a threat, the movement of terrorist fund-raising activities online creates a disruption opportunity. This article argues that social media companies need to display greater awareness of their vulnerability to supporting terrorist financing and greater collaboration with law enforcement and financial institutions to strengthen the integrity of the system against abuse.

In the aftermath of the Boston Marathon bombings Bipartisan Policy Center Homeland Security Project co-chair and former Governor Tom Kean, a 9/11 Commission co-chair, talks to BBC World News about the dangers of online radicalisation. Originally published by the BBC on 26 April 2013

VOX-Pol Blog post.

Proliferation of social media platforms in recent past has resulted into upsurge in the number of users. Advent of these sites have paved way for the users to easily express share and communicate. In such a scenario, it is imperative to analyze the content and identify nasty content so as to avoid unpleasant situations. Machine learning techniques are extensively used for this purpose. In this paper, we propose a language model for the identification of hate speech in twitter data. Distil-BERT, a context aware embedding model along with Support Vector Machine (SVM) for the classification of hate speech has been used. SVM with a 10-fold cross validation and linear kernel has been found to provide better accuracy as compared to existing models. Results show that accuracy is improved with the use of context aware embedding model.

This article presents the results of a case study of the online community Russian Nationalism, one of the most popular Russian nationalists’ online communities on VKontakte in 2012–2016. The article aims to find out where network leaders and common members of nationalist online groups closed on the Russian Internet go and in which thematic communities they continue their activity. Russian Nationalism members’ personal profiles and friendly links on VKontakte were used as data sources. The metrics of social network analysis were used to study Russian Nationalism’s network structure. The study showed that the ban of a massive extreme right nationalist online group leads to an increase in the number of smaller shelter groups that retain their ideological basis, but abandon the clearly expressed hate speech; members of a banned nationalist group flow to groups whose content is both close (militaristic, cultural, and historical) and far (everyday) to the extreme right. The content of the latter can transform in line with the nationalist ideology. The case study of Russian Nationalism shows a high potential for diffusion of extreme right nationalist ideas in the Russian network space, which manifests in their ability to penetrate other thematic and ideological areas of public discourse.