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This study seeks to understand the community’s perceptions towards detecting signs of online radicalisation and examine whether different community members would exhibit different levels of understanding. A 57-item survey was administered to 160 undergraduates from Nanyang Technological University (NTU) and 160 Amazon Mechanical Turk (MTurk) workers. Based on the ratings of the 42 online radicalisation indicators identified by Neo (2020), two-factor analyses were separately conducted using oblique rotation to undercover a four-factor structure for the NTU sample and a three-factor solution for the MTurk sample. The results revealed valuable insights into how community members would identify terrorist threats. Furthermore, the survey revealed differences in the participants’ views on the role of the internet in radicalisation pathways and their perceptions regarding various counter-terrorism strategies. Together, the findings would contribute to the discussion of how law enforcement could better engage and work together with the community members to detect terrorist threats.

‘Jihadism’ (also known as the jihadi movement) is a popular term that signifies the Islamic terror movement which thrives on extremist ideologies and violence. In addition to the conventional practices, the online medium is currently being employed for disseminating these extremist ideologies across the globe. Radicalisation and recruitment of geographically dispersed individuals as ‘jihadists’ for supporting Islamic terror activities tend to be the primary intent for using the digital platforms as the medium of communication in this context. One such initiative by the Lashkar-e-Taiba of Jammu and Kashmir was the release of the ‘Wyeth: The Resistance in Flow’, an e-magazine which was launched on April 2018. The first issue which was posted with an open access option was primarily designed to influence the Indian youth population through the radical interpretations of Islam. Hence, it is crucial to analyse and understand the jihadi discourse of the Wyeth magazine in order to curb and counter-attack such initiatives at its initial phase. For this purpose, the present study aims to examine the content of the Wyeth magazine and analyse the basic traits of the jihadi propaganda and its potential to aid in the self-radicalisation process.

In the domain of computer science, the last years have witnessed the improvement of social network analysis at scale. One of the most challenging aspects of social network analysis is community detection; analysts use a variety of tools to visualise the spontaneous group structure emerging from interactions and friendship relations in multi-million-user networks. This visualisation, combined with influencer detection and automated text analysis tools such as topic detection, enables the analyst to grasp most of the complexity of a social network. This computer-science-oriented study explores three lines of research concerning online extremism. First, about the emerging narratives and the topics that can be found on open platforms. We show that many actors actively use terror-group related terms; most cannot be directly tied to any specific organisation. A second axis concerns the connections between platforms: the information space has no central point as content is shared across platforms. However, the links reveal clusters of locations: we observe a group of Pakistan-India conflict mentions, and a cluster of US alt-right websites, transforming terrorism into a migration problem. The third axis relates to the social media landscape structure. We rely on a combination of document-level topic modelling and graph analysis to detect and explore the social data, visualising the types of groups that are active on the topic. Among the results, we found a small botnet circulating a pro-Daesh pamphlet and a set of grassroot reactions that managed to moderate a controversial pro-Jihadi post on Reddit.

The National Academy of Engineering called the grid the world’s largest integrated machine and a central part of the greatest engineering achievement of the 20th century—electrification of modern society. Reliable electricity service is essential to health, welfare, national security, communication, and commerce. Because of its scale, geographic reach, and complexity, however, the grid also poses many security challenges in maintaining reliable operation. Furthermore, more than 90 percent of the U.S. power grid is privately owned and regulated by the states, making it challenging for the federal government to address potential vulnerabilities to its operation, and perhaps especially its vulnerability to terrorist attack. This report examines those vulnerabilities.

Violence usually spread online, as it has spread in the past. With the increasing use of social media, the violence attributed to online hate speech has increased worldwide resulting rise in number of attacks on immigrants and other minorities. Analysis of such short text posts (e.g. tweets etc.) is valuable for identification of abusive language and hate speech. In this paper, we present Deep Context-Aware Embedding for the detection of Hate speech and abusive language on twitter. To improve the classification performance, we have enhanced the quality of the tweets by considering polsemy, syntax, semantic, OOV words as well as sentiment knowledge and concatenated to form input vector. We have used BiLSTM with attention modeling to identify tweet with hate speech. Experimental results showed significant improvement in the classification of tweets.

VOX-Pol Blog post.