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The internet has profoundly changed the way we communicate, including how jihadist
groups seek to reach Western audiences with their propaganda strategies. Cases of believed
online-radicalization call for a re-evaluation of radicalization processes, previously thought
to depend on face-to-face interactions. Based on the Hoffman-Sageman debate on whether
top-down or bottom-up processes drive terrorism, this essay explores both social movement
and organizational approaches to understand online-radicalization. Do jihadist
organizations such as Al-Qaeda and IS act as ‘fishermen’, actively engaging in the
radicalization processes of individual recruits, or is radicalization driven by social group
dynamics with little organizational involvement? Essentially, the larger question is: What
role do organizational structures play for radicalization in times of ‘virtual jihad’? Bottomup
radicalization processes are facilitated online, because the conditions for Sageman’s
‘bunch of guys’ are replicated by the characteristics of virtual communication: an echo
chamber effect causes frame-alignment through repetition and enables ‘digital natives’ to
communicate claims that resonate with other ‘digital natives’. Top-down structures are
influential, because organizations continue to employ sophisticated propaganda
development, preachers and special recruiters or ‘fishermen’. The article finds evidence for
both schools of thought and concludes that the internet facilitates both types of
radicalization mechanisms. Only a holistic strategy will be successful in battling onlineradicalization
and must include both targeting direct channels through which the
organizations execute control over recruits, and breaking the echo chamber created by
social movement dynamics in the virtual world. While countermeasures need to include the
provision of alternative social narratives and the utilization of ‘digital natives’ to make
counter-messages more effective, organizational structures need to be tackled
simultaneously, not only by identifying and arresting preachers and recruiters, but also
through stronger internet governance tools and collaboration with social media companies.

Since its early mainstream adoption in the 1990s, the Internet has been leveraged by white supremacist groups to recruit and radicalize individuals. Twenty years later, social media platforms, like YouTube, reddit, and Twitter, continue to further this practice. The attention of researchers has been primarily centered on white supremacist men, and this focus on white men erases white women’s roles as active agents in the spread of white supremacy, skewing our understanding of white supremacy as a whole. This study used digital ethnography and interviews to examine the ways white women are radicalized to organized white supremacy through popular social media platforms YouTube, reddit, and Twitter. The study found white women were radicalized by engaging with posts and joining communities focusing on beauty, anti-feminism or “The Red Pill,” traditionalist gender values or #TradLives, and alt-right politics. White supremacist recruiters leveraged gendered topics and weaponized platform features – likes, sharing, comments, recommendation algorithms, etc. – to cultivate a sense of community. Through involvement with these communities, women were introduced to racialized perspectives on each topic, usually after a catalytic pop culture or newsworthy event, and slowly radicalized to organized white supremacy.

Using a dataset of over 1.9 million messages posted on Twitter by about 25,000 ISIS members, we explore how ISIS makes use of social media to spread its propaganda and to recruit militants from the Arab world and across the globe. By distinguishing between violence-driven, theological, and sectarian content, we trace the connection between online rhetoric and key events on the ground. To the best of our knowledge, ours is one of the first studies to focus on Arabic content, while most literature focuses on English content. Our findings yield new important insights about how social media is used by radical militant groups to target the Arab-speaking world, and reveal important patterns in their propaganda efforts.

VOX-Pol Blog post.

With the ever-increasing cases of hate spread on social media platforms, it is critical to design abuse detection mechanisms to pro-actively avoid and control such incidents. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. Bias removal has been traditionally studied for structured datasets, but we aim at bias mitigation from unstructured text data. In this paper, we make two important contributions. First, we systematically design methods to quantify the bias for any model and propose algorithms for identifying the set of words which the model stereotypes. Second, we propose novel methods leveraging knowledge-based generalizations for bias-free learning. Knowledge-based generalization provides an effective way to encode knowledge because the abstraction they provide not only generalizes content but also facilitates retraction of information from the hate speech detection classifier, thereby reducing the imbalance. We experiment with multiple knowledge generalization policies and analyze their effect on general performance and in mitigating bias. Our experiments with two real-world datasets, a Wikipedia Talk Pages dataset (WikiDetox) of size ~ 96k and a Twitter dataset of size ~ 24k, show that the use of knowledge-based generalizations results in better performance by forcing the classifier to learn from generalized content. Our methods utilize existing knowledge-bases and can easily be extended to other tasks.

Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity. Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char/word n-gram methods by ~18 F1 points.