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The notion of women in terrorism pushed its way to the forefront of the American mindset on December 2, 2015, when Tashfeen Malik and her husband, Syed Farook, opened fire at the Inland Regional Center in San Bernardino, California. After the couple killed 14 and injured 22, the growing threat posed by female jihadists in America became immediately apparent to policymakers, law enforcement officials, and the public. Some reports, citing law enforcement officials, claim that Malik pledged allegiance to Islamic State (IS) leader Abu Bakr al-Baghdadi on Facebook the day of the attack.2 IS later praised the couple’s actions in Dabiq, its official English-language magazine, affiliating themselves with the duo.3 Despite these assertions, the FBI’s most recent report has not yet determined a direct link to IS.4 Details about the couple’s path to violence remain buried in an ongoing investigation that may take years to reach the public. In spite of this obstacle, Malik’s case offers exceptional insight into the complex, morphing ventures of jihadist women in America. It is difficult to discern the exact rate at which women participate in jihadist movements in the United States, but the surge in relevant legal cases suggests this figure is on the rise. In the decade following 9/11, only a handful of prominent cases, like that of Aafia Siddiqui5 and Colleen LaRose,6 have shown the threat female jihadists could pose to national security. In recent years, instances of terrorism-related activity perpetrated by women have increased in number. Since 2011, at least 25 known cases of jihadi women with connections to the U.S. have emerged, shedding light on the myriad roles adopted by female jihadists. While few follow in Tashfeen Malik’s footsteps and pursue violent plots, many disseminate propaganda or donate resources to show their support. In some instances, women travel abroad to make direct contributions to a particular group. This report uses a wealth of primary and secondary data to examine the efforts of 25 American jihadi women since 2011.7 The cases offer a tremendous diversity of demographic data, suggesting that an overarching profile of the female jihadist is indiscernible. Moreover, within the dataset, women align themselves with a range of organizations including, but not limited to, IS, al-Shabaab, the Taliban, and al-Qaeda.

Because it mostly exists online, the Alt Right does not have the infrastructure needed to launch a guerrilla war (as Nazi/Klan forces did in the 1980s) or build pseudo-state institutions (as Patriot groups did in the 1990s and are attempting again now), but it is in a strong position to pursue a “metapolitical” transformation of the political culture and thereby lay the groundwork for structural change, centered on its vision of a White ethnostate.

Terrorism researchers have long sought to identify methods for challenging terrorist ideologies. The construction and dissemination of counternarratives has begun to receive substantial attention as a means of doing so. However, the effectiveness of this approach is contingent on message targets’ trust in the counternarrative’s content and source. This article draws from literatures on trust and online source credibility to offer preliminary guidelines for cultivating trust in counternarratives and their sources. By encouraging trust in this manner, practitioners can reduce the likelihood that their counternarratives will be dismissed by their intended audiences—a perpetual challenge to strategic messaging geared towards countering violent extremism.

Jihadis have long shared military-related information and expertise on top jihadi forums. Recently, much of that information has been circulated on the various social media outlets, as well. The shared information varies, from original content to translations, summaries, and recommended reading of non-Arabic material. Contrary to the common notion that jihadis are suspicious of, and avoiding circulating, any material originating from “enemy” sources, jihadis visiting military and technology sections on top jihadi forums in fact thrive on a broad base of information, especially information originating with “enemy” sources. This report provides an overview of military and technology-related subsections on the main jihadi forums.

A revealing and gripping investigation into how social media platforms police what we post online—and the large societal impact of these decisions. Most users want their Twitter feed, Facebook page, and YouTube comments to be free of harassment and porn. Whether faced with “fake news” or livestreamed violence, “content moderators”—who censor or promote user-posted content—have never been more important. This is especially true when the tools that social media platforms use to curb trolling, ban hate speech, and censor pornography can also silence the speech you need to hear.

In this revealing and nuanced exploration, award-winning sociologist and cultural observer Tarleton Gillespie provides an overview of current social media practices and explains the underlying rationales for how, when, and why these policies are enforced. In doing so, Gillespie highlights that content moderation receives too little public scrutiny even as it is shapes social norms and creates consequences for public discourse, cultural production, and the fabric of society. Based on interviews with content moderators, creators, and consumers, this accessible, timely book is a must-read for anyone who’s ever clicked “like” or “retweet.”

The use of “Big Data” in policy and decision making is a current topic of debate. The 2013 murder of Drummer Lee Rigby in Woolwich, London, UK led to an extensive public reaction on social media, providing the opportunity to study the spread of online hate speech (cyber hate) on Twitter. Human annotated Twitter data was collected in the immediate aftermath of Rigby’s murder to train and test a supervised machine learning text classifier that distinguishes between hateful and/or antagonistic responses with a focus on race, ethnicity, or religion; and more general responses. Classification features were derived from the content of each tweet, including grammatical dependencies between words to recognize “othering” phrases, incitement to respond with antagonistic action, and claims of well-founded or justified discrimination against social groups. The results of the classifier were optimal using a combination of probabilistic, rule-based, and spatial-based classifiers with a voted ensemble meta-classifier. We demonstrate how the results of the classifier can be robustly utilized in a statistical model used to forecast the likely spread of cyber hate in a sample of Twitter data. The applications to policy and decision making are discussed.