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VOX-Pol Blog post.

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 Internet and online spaces have come to play a significant role in discussions over both terrorism and methods to counter it. Recent events have highlighted the role that such online spaces and networks can play in irregular violence. The growth of the Islamic State in Iraq and Syria came with a canon of slick social media videos and online propaganda, aimed at encouraging individuals from throughout the world to pledge support to Da’esh, participate in the state-building process in as-Sham, or to “stay in place” and commit attacks in their home countries.

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.