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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.

Countering the militancy in Kashmir has become a highly challenging task due to the exploitation of new information and communication technology by insurgent groups. The battlefield is now a multidimensional one, encompassing both physical territory and cyberspace. The overall capabilities of insurgents have been enhanced by tools in cyberspace that are inexpensive, ever more sophisticated, rapidly proliferating, and easy to use. Militants are systematically exploiting the Internet to generate moral support, recruit personnel, and transmit propaganda, leading to the further militarisation of the Kashmiri youth. This paper examines the potentially disastrous consequences of the use of cyberspace by an already strong insurgency in Kashmir. The objective is to understand the most effective means to counter the cyber dimension of the Kashmir insurgency.

Radical ideologies and violent extremism continue to plague Kyrgyzstan and the Central Asia region, with approximately 2,000 Central Asians traveling to Syria and Iraq to join violent extremist groups. As fear of terrorism has grown in the region, the Kyrgyz government has increased scrutiny of Islam, with authorities conducting “appraisals” of clergy leaders, and violent clashes occurring between state police and suspected violent extremists. Despite these crackdowns, however, recruitment into religious-based violent extremist organizations continues both in-person and online. Social media plays a significant role in the dissemination and absorption of radical ideologies and narratives in Kyrgyzstan. Internet sites and mobile phone applications, including those that allow for messaging and content exchange, are actively used by extremist groups banned in Kyrgyzstan, such as the Islamic State (ISIS).

VOX-Pol Blog Post.