Online Library

Online discussion forums provide open workspace allowing users to share information, exchange ideas, address problems, and form groups. These forums feature multimodal posts and analyzing them requires a framework that can integrate heterogeneous information extracted from the posts, i.e. text, visual content and the information about user interactions with the online platform and each other. In this paper, we develop a generic framework that can be trained to identify communication behavior and patterns in relation to an entity of interest, be it user, image or text in internet forums. As the case study we use the analysis of violent online political extremism content, which has been a major challenge for domain experts. We demonstrate the generalizability and flexibility of our framework in predicting relational information between multimodal entities by conducting extensive experimentation around four practical use cases.

Operations involving the capture, processing, and transmission of terrorist events, campaigns, or images produce effects well beyond the representational/informational functions of media. This article examines several unspoken effects involved in the mediation of terrorism. We analyze the extent to which several mechanisms and operations of western media may be complicit in, if not fundamental to, the global production and administration of terror, particularly at the level of its image and what we call virality. We theorize the ways in which media not only “mediate” terror, but also function to regulate and/or administer it and, in particular, to exacerbate, amplify, and proliferate images and activities of Islamic State in Iraq and Syria (ISIS) across global networks of digital exchange. We argue that key to understanding the strategies and circulating effects of ISIS’s media involvement is the tendency of viral media operations to overproduce, overextend, and oversaturate. The condition of oversaturation denotes a hyperactive global media circuitry that is collapsing under its own weight. This condition reflects a strategic tendency of terror, which underlies all mediatic processing of images deployed by ISIS. It also reveals a vulnerability for terrorist strategy to exacerbate and exhaust the hyperactivity of media, and thus to accelerate the implosive collapse of the globally networked system. We theorize how implicit and unintended effects or outputs of the mediatic processing of terrorist meanings, images, and discourses may work to overstimulate the global system to the point of its reversal, exhaustion, or implosion.

This paper argues that companies’ efforts to deal with TVEC have been hampered at the outset by a tendency to define TVEC extremely narrowly. Still, only a tiny proportion of content that could reasonably be categorized as TVEC is included in most definitions. An outsized focus on pre-identified Islamic extremists and terrorist groups means that other types of violent extremists and terrorists (e.g., white supremacists, incels), and those unaffiliated with a group (e.g., lone-wolf actors) are overlooked. This paper also explores the idea of ethical obligations and norms as an alternative to a legally required definition.

This workbook teaches researchers, analysts and practitioners how different sorts of terrorist and violent extremist actors utilise financial technologies and cryptocurrencies to finance their operations. The process of terrorist adoption of financial technologies is spelled out for various organisations and can assist analysts to estimate whether and when a group or terrorist actor would embrace a financial technology or cryptocurrency. The workbook also includes terms that may be used to search information holdings for terrorist adoption of cryptocurrencies or financial technologies, offering early warning of terrorist adoption.

VOX-Pol Blog Post.

Social media messages often provide insights into offline behaviors. Although hate speech proliferates rapidly across social media platforms, it is rarely recognized as a cybercrime, even when it may be linked to offline hate crimes that typically involve physical violence. This paper aims to anticipate violent acts by analyzing online hate speech (hatred, toxicity, and sentiment) and comparing it to offline hate crime. The dataset for this preregistered study included social media posts from X (previously called Twitter) and Facebook and internal police records of hate crimes reported in Spain between 2016 and 2018. After conducting preliminary data analysis to check the moderate temporal correlation, we used time series analysis to develop computational models (VAR, GLMNet, and XGBTree) to predict four time periods of these rare events on a daily and weekly basis. Forty-eight models were run to forecast two types of offline hate crimes, those against migrants and those against the LGBT community. The best model for migrant crime achieved an R² of 64%, while that for LGBT crime reached 53%. According to the best ML models, the weekly aggregations outperformed the daily aggregations, the national models outperformed those geolocated in Madrid, and those about migration were more effective than those about LGBT people. Moreover, toxic language outperformed hatred and sentiment analysis, Facebook posts were better predictors than tweets, and in most cases, speech temporally preceded crime. Although we do not make any claims about causation, we conclude that online inflammatory language could be a leading indicator for detecting potential hate crimes acts and that these models can have practical applications for preventing these crimes.