Online Library

VOX-Pol Blog post.

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.

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.

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.

‘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.

This study involved the creation of factors and indicators that can detect radicalization in social media posts. A concurrent approach of an expert knowledge acquisition process (modified Delphi technique) and literature review was utilized. Seven Singapore subject-matter experts in the field of terrorism evaluated factors that were collated from six terrorism risk assessment tools (ERG 22+, IVP, TRAP-18, MLG, VERA-2, and Cyber-VERA). They identify those that are of most considerable relevance for detecting radicalization in social media posts. A parallel literature review on online radicalization was conducted to complement the findings from the expert panel. In doing so, 12 factors and their 42 observable indicators were derived. These factors and indicators have the potential to guide the development of cyber-focused screening tools to detect radicalization in social media posts.