The Macerata Shooting: Digital Movements of Opinion in the Hybrid Media System

The role of Twitter in the organisation of political action – either by supporting existing street-level protests or native digital mobilizations – has attracted a great deal of attention. However, the wider media, political, and cultural context in which mobilizations take place is often overlooked. In this article, we analyse the trajectory of a digital movement of opinion that reacted to the shooting of black people by a right-wing militant in the Italian town of Macerata in 2018. Using a dataset of 571,996 tweets captured over 31 days, and employing a mix of machine learning, network analysis and qualitative investigation, we study how factors both external and internal to the platform sealed the fate of that movement. We maintain that the permeability of Twitter to outer divided arenas and its re-intermediation by political leaders are key to the transformation of protest movements into polarised crowds

The Macerata Shooting: Digital Movements of Opinion in the Hybrid Media System

The role of Twitter in the organisation of political action – either by supporting existing street-level protests or native digital mobilizations – has attracted a great deal of attention. However, the wider media, political, and cultural context in which mobilizations take place is often overlooked. In this article, we analyse the trajectory of a digital movement of opinion that reacted to the shooting of black people by a right-wing militant in the Italian town of Macerata in 2018. Using a dataset of 571,996 tweets captured over 31 days, and employing a mix of machine learning, network analysis and qualitative investigation, we study how factors both external and internal to the platform sealed the fate of that movement. We maintain that the permeability of Twitter to outer divided arenas and its re-intermediation by political leaders are key to the transformation of protest movements into polarised crowds

State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism

Overview This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.  The paper is structured as follows: Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS). Part 2 provides an introduction to the key approaches of social media intelligence (henceforth ‘SOCMINT’) for counter-terrorism. Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored. Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work

Escaping from American intelligence : culture, ethnocentrism and the Anglosphere

The United States and its closest allies now spend over $100 billion a year on intelligence. Ten years after 9/11, the intelligence machine is certainly bigger - but not necessarily better. American intelligence continues to privilege old-fashioned strategic analysis for policy-makers and exhibits a technocratic approach to asymmetric security threats, epitomized by the accelerated use of drone strikes and data-mining. Distinguished commentators have focused on the panacea of top-down reform, while politicians and practitioners have created entire new agencies. However these prescriptions for change remain conceptually limited because of underlying Anglo-Saxon presumptions about what intelligence is. Although intelligence is a global business, when we talk about intelligence we tend to use a vocabulary that is narrowly derived from the experiences of America and its English-speaking nebula. This article deploys the notion of strategic culture to explain this why this is. It then explores the cases of China and South Africa to suggest how we might begin to rethink our intelligence communities and their tasks. It argues that the road to success is about individuals, attitudes and cultures rather than organizations. Future improvement will depend on our ability to recognize the changing nature of the security environment and to practice the art of ‘intelligence among the people’. While the United States remains the world’s most significant military power, its strategic culture is unsuited to this new terrain and arguably other countries do these things rather better

What are the roles of the Internet in terrorism? Measuring online behaviours of convicted UK terrorists

Using a unique dataset of 227 convicted UK-based terrorists, this report fills a large gap in the existing literature. Using descriptive statistics, we first outline the degree to which various online activities related to radicalisation were present within the sample. The results illustrate the variance in behaviours often attributed to ‘online radicalisation’. Second, we conducted a smallest-space analysis to illustrate two clusters of commonly co-occurring behaviours that delineate behaviours from those directly associated with attack planning. Third, we conduct a series of bivariate and multivariate analyses to question whether those who interact virtually with like-minded individuals or learn online, exhibit markedly different experiences (e.g. radicalisation, event preparation, attack outcomes) than those who do not

Graph-based, systems approach for detecting violent extremist radicalization trajectories and other latent behaviors, A

2017 Summer.Includes bibliographical references.The number and lethality of violent extremist plots motivated by the Salafi-jihadist ideology have been growing for nearly the last decade in both the U.S and Western Europe. While detecting the radicalization of violent extremists is a key component in preventing future terrorist attacks, it remains a significant challenge to law enforcement due to the issues of both scale and dynamics. Recent terrorist attack successes highlight the real possibility of missed signals from, or continued radicalization by, individuals whom the authorities had formerly investigated and even interviewed. Additionally, beyond considering just the behavioral dynamics of a person of interest is the need for investigators to consider the behaviors and activities of social ties vis-à-vis the person of interest. We undertake a fundamentally systems approach in addressing these challenges by investigating the need and feasibility of a radicalization detection system, a risk assessment assistance technology for law enforcement and intelligence agencies. The proposed system first mines public data and government databases for individuals who exhibit risk indicators for extremist violence, and then enables law enforcement to monitor those individuals at the scope and scale that is lawful, and account for the dynamic indicative behaviors of the individuals and their associates rigorously and automatically. In this thesis, we first identify the operational deficiencies of current law enforcement and intelligence agency efforts, investigate the environmental conditions and stakeholders most salient to the development and operation of the proposed system, and address both programmatic and technical risks with several initial mitigating strategies. We codify this large effort into a radicalization detection system framework. The main thrust of this effort is the investigation of the technological opportunities for the identification of individuals matching a radicalization pattern of behaviors in the proposed radicalization detection system. We frame our technical approach as a unique dynamic graph pattern matching problem, and develop a technology called INSiGHT (Investigative Search for Graph Trajectories) to help identify individuals or small groups with conforming subgraphs to a radicalization query pattern, and follow the match trajectories over time. INSiGHT is aimed at assisting law enforcement and intelligence agencies in monitoring and screening for those individuals whose behaviors indicate a significant risk for violence, and allow for the better prioritization of limited investigative resources. We demonstrated the performance of INSiGHT on a variety of datasets, to include small synthetic radicalization-specific data sets, a real behavioral dataset of time-stamped radicalization indicators of recent U.S. violent extremists, and a large, real-world BlogCatalog dataset serving as a proxy for the type of intelligence or law enforcement data networks that could be utilized to track the radicalization of violent extremists. We also extended INSiGHT by developing a non-combinatorial neighbor matching technique to enable analysts to maintain visibility of potential collective threats and conspiracies and account for the role close social ties have in an individual's radicalization. This enhancement was validated on small, synthetic radicalization-specific datasets as well as the large BlogCatalog dataset with real social network connections and tagging behaviors for over 80K accounts. The results showed that our algorithm returned whole and partial subgraph matches that enabled analysts to gain and maintain visibility on neighbors' activities. Overall, INSiGHT led to consistent, informed, and reliable assessments about those who pose a significant risk for some latent behavior in a variety of settings. Based upon these results, we maintain that INSiGHT is a feasible and useful supporting technology with the potential to optimize law enforcement investigative efforts and ultimately enable the prevention of individuals from carrying out extremist violence. Although the prime motivation of this research is the detection of violent extremist radicalization, we found that INSiGHT is applicable in detecting latent behaviors in other domains such as on-line student assessment and consumer analytics. This utility was demonstrated through experiments with real data. For on-line student assessment, we tested INSiGHT on a MOOC dataset of students and time-stamped on-line course activities to predict those students who persisted in the course. For consumer analytics, we tested the performance on a real, large proprietary consumer activities dataset from a home improvement retailer. Lastly, motivated by the desire to validate INSiGHT as a screening technology when ground truth is known, we developed a synthetic data generator of large population, time-stamped, individual-level consumer activities data consistent with an a priori project set designation (latent behavior). This contribution also sets the stage for future work in developing an analogous synthetic data generator for radicalization indicators to serve as a testbed for INSiGHT and other data mining algorithms