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The recent rise of the political extremism in Western countries has spurred renewed interest in the psychological and moral appeal of political extremism. Empirical support for the psychological explanation using surveys has been limited by lack of access to extremist groups, while field studies have missed psychological measures and failed to compare extremists with contrast groups. We revisit the debate over the psychological and moral appeal of extremism in the U.S. context by analyzing Twitter data of 10,000 political extremists and comparing their text-based psychological constructs with those of 5000 liberal and 5000 conservative users. The results reveal that extremists show a lower positive emotion and a higher negative emotion than partisan users, but their differences in certainty is not significant. In addition, while left-wing extremists express more language indicative of anxiety than liberals, right-wing extremists express lower anxiety than conservatives. Moreover, our results mostly lend support to Moral Foundations Theory for partisan users and extend it to the political extremists. With the exception of ingroup loyalty, we found evidences supporting the Moral Foundations Theory among left- and right-wing extremists. However, we found no evidence for elevated moral foundations among political extremists.

Identifying and profiling threat actors are high priority tasks for a number of governmental organizations. These threat actors may operate actively, using the Internet to promote propaganda, recruit new members, or exert command and control over their networks. Alternatively, threat actors may operate passively, demonstrating operational security awareness online while using their Internet presence to gather information they need to pose an offline physical threat. This paper presents a flexible new prototype system that allows analysts to automatically detect, monitor and characterize threat actors and their networks using publicly available information. The proposed prototype system fills a need in the intelligence community for a capability to automate manual construction and analysis of online threat networks. Leveraging graph sampling approaches, we perform targeted data collection of extremist social media accounts and their networks. We design and incorporate new algorithms for role classification and radicalization detection using insights from social science literature of extremism. Additionally, we develop and implement analytics to facilitate monitoring the dynamic social networks over time. The prototype also incorporates several novel machine learning algorithms for threat actor discovery and characterization, such as classification of user posts into discourse categories, user post summaries and gender prediction.

Objectives: In this systematic review and meta analysis we will collate and synthesize the evidence on media‐effects for radicalization, focusing on both cognitive
and behavioral outcomes. The goal is to identify the relative magnitudes of the effects for different mediums, types of content, and elements of human‐media
relationships.
Methodology: Random‐effects meta analysis will be used and the results will be rank‐ordered according to the size of the pooled estimates for the different factors.
Meta‐regressions, moderator analysis, and sub‐group analyses will be used to investigate sources of heterogeneity.
Implications: The results of this review will provide a better understanding of the relative magnitude of the effects of media‐related factors. This information should
help the development of more evidence‐based policies.

VOX-Pol Blog Post.

This paper collection entitled “Protecting the Homeland from International and Domestic Terrorism Threats: Current Multi-Disciplinary Perspectives on Root Causes, the Role of Ideology, and Programs for Counter-radicalization and Disengagement”, seeks to add insights without needlessly repeating what has been heard and read elsewhere. What separates this paper collection from the many others on this topic is the multiplicity of perspectives represented, both domestic and international, that span the spectrum of social sciences. To do this, over forty authors were asked to provide perspectives on various aspects of terrorism: root causes, dynamics of Violent Non-State Actors (VNSAs), the role of ideology in terrorism, and potential solutions for counter-radicalization, deradicalization, and disengagement from terrorism.

This paper offers a set of specific proposals for better describing harmful content online and for reducing the damage it causes, while protecting freedom of expression. The ideas are mainly meant for OSPs since they regulate the vast majority of online content; taken together they operate the largest system of censorship the world has ever known, controlling more human communication than any government. Governments, for their part, have tried to berate or force the companies into changing their policies, with limited and often repressive results. For these reasons, this paper focuses on what OSPs should do to diminish harmful content online. The proposals focus on the rules that form the basis of each regulation system, as well as on other crucial steps in the regulatory process, such as communicating rules to platform users, giving multiple stakeholders a role in regulation, and enforcement of the rules.