Online Library

VOX-Pol is pleased to share the latest online workshop on “Borderline Online Content” which took place in June 2024. The workshop is hosted by Brigitte Naderer, Senior Researcher at the Medical University of Vienna, Heidi Schulze Research Associate at LMU Munich, and Diana Rieger, Professor at LMU Munich. The presenters will speak on topics around borderline, ‘hard to regulate’, content in the following four talks:

Heidi Schulze & Simon Greipl “A Little Less Hate, But a Lot More Harm – Fear Speech as Strategic Borderline Communication”

Hannah Rose: “Hybridised Online Hate and Extremism in the Israel/Gaza Conflict”

Ursula Schmid “Legitimizing hostility through humor: Perceptions and effects of humorous hate speech on social media”

Broderick McDonald “Lawful but Awful: Borderline Content and Human Rights”

Although video releases have been central to the Islamic State’s efforts to represent itself to its audiences, an extensive quantitative and qualitative study of these sources over a longer period of time is still lacking. This paper therefore provides an overview and analysis of the entire corpus of official videos released by the Islamic State between 1 July 2015 and 30 June 2018. It particularly focuses on how the Islamic State’s decline in Iraq and Syria during this period is reflected in its video output and how the group has responded to its setbacks. The paper demonstrates a strong correlation between the group’s mounting troubles and its video production: the numbers of videos decreased dramatically and their content reflects the Islamic State’s (re)transformation from a territory-based ‘state’ to an insurgent group relying on guerrilla tactics and terrorist attacks. Nevertheless, this paper argues that the Islamic State’s multi-faceted response to its setbacks might ensure the groups’ appeal to its target audience in the years to come.

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.