Online Library

Since the declaration of the Islamic State (IS) in 2014, men and women have been recruited to join the Caliphate in numbers surpassing those recruited by al Qaida. This variance in recruitment volume is likely attributable to the online propaganda campaign, Brand Caliphate. This thesis looks at the recruitment of women and asks if Brand Caliphate specifically targets females with its messaging, and if so, is the messaging effective? Based on a textual analysis of Brand Caliphate’s propaganda, it appears IS tried to deliver messaging targeted toward females. However, six case studies of radicalized females suggest the recruitment of these women does not appear to be directly attributable to the targeted messaging. There is, however, evidence that most of the female recruitment studied linked to online radicalization and Brand Caliphate’s broader messaging. All of the women studied did initially look online for information regarding IS. This initial outreach served to identify them as targets for radicalization by IS recruiters, who continued to persuade the females through direct online communication. Ultimately, a sense of belonging to a community, even if it exists online, served as a more powerful draw to potential recruits than the targeted messaging of Brand Caliphate.

This study evaluates female roles in pro-jihadist terrorism by examining online content. Data was collected from 36 Twitter accounts of women associated with al-Qaeda (AQ) affiliated groups for a period of six months. The purpose for collecting this data was to: 1) compare how traditional female roles, as constructed within a jihadi-Salafist ideology, are reproduced and challenged on social media; 2) and determine the extent that AQ-affiliated women conform to roles outlined in Huey’s classification of females in pro-Islamic State (IS) Twitter networks. The results of this study reveal that women’s traditional roles in pro-jihadist activities are reproduced on Twitter. Although the women appear to be empowered by the anonymity that Twitter provides, their roles remain largely constrained to those in supportive positions. AQ women mainly use Twitter to share the ideological beliefs of AQ and provide emotional support for fellow AQ members. In comparison with IS, AQ females subscribe to only a portion of the roles outlined in Huey’s classification.

Since the outbreak of the Syrian civil war in 2011 close to 30 000 foreign recruits from more than 100countries have migrated to the area of Iraq and Syria in support of the terrorist organization this thesis will refer to as ISIS. Among those traveling is a historically unprecedented number of women. Why women are drawn to violent Islamic extremist groups is rather unexplored. Through a qualitative text analysis of official ISIS-propaganda, this thesis investigates what promises the organization makes to women, examining pull-factors derived from social media studies of female migration to ISIS-held territories. The thesis concludes that women are promised the possibility to fulfill their religious duty, become important state builders, experience deep and meaningful belonging and sisterhood, to live an exciting adventure and find true romance, as well as being increasingly influential is also promised. Official propaganda does not make explicit promises to women of exerting violence. A secondary purpose of the thesis is to assess the potential risk that ISIS-affiliated women returning to the West, pose to society. This thesis further concludes that women who gain limited knowledge of handling weapons and explosives in ISIS-territory are not probable participants in armed terrorist attacks directed towards the West. However, through increased social networks acquired while in Syria or Iraq, women may play an important supporting role in the process of planning, crowdfunding and executing attacks. Based on these findings the thesis provides some gender-specific policy proposals intended to counter the recruitment of women to ISIS.

This study considers the ways that overt hate speech and covert discriminatory practices circulate on Facebook despite its official policy that prohibits hate speech. We argue that hate speech and discriminatory practices are not only explained by users’ motivations and actions, but are also formed by a network of ties between the platform’s policy, its technological affordances, and the communicative acts of its users. Our argument is supported with longitudinal multimodal content and network analyses of data extracted from official Facebook pages of seven extreme-right political parties in Spain between 2009 and 2013. We found that the Spanish extreme-right political parties primarily implicate discrimination, which is then taken up by their followers who use overt hate speech in the comment space.

Who is likely to view materials online maligning groups based on race, nationality, ethnicity, sexual orientation, gender, political views, immigration status, or religion? We use an online survey (N = 1034) of youth and young adults recruited from a demographically balanced sample of Americans to address this question. By studying demographic characteristics and online habits of individuals who are exposed to online extremist groups and their messaging, this study serves as a precursor to a larger research endeavor examining the online contexts of extremism. Descriptive results indicate that a sizable majority of respondents were exposed to negative materials online. The materials were most commonly used to stereotype groups. Nearly half of negative material centered on race or ethnicity, and respondents were likely to encounter such material on social media sites. Regression results demonstrate African-Americans and foreign-born respondents were significantly less likely to be exposed to negative material online, as are younger respondents. Additionally, individuals expressing greater levels of trust in the federal government report significantly less exposure to such materials. Higher levels of education result in increased exposure to negative materials, as does a proclivity towards risk-taking.

We present a machine learning framework that leverages a mixture of metadata, network, and temporal features to detect extremist users, and predict content adopters and interaction reciprocity in social media. We exploit a unique dataset containing millions of tweets generated by more than 25 thousand users who have been manually identified, reported, and suspended by Twitter due to their involvement with extremist campaigns. We also leverage millions of tweets generated by a random sample of 25 thousand regular users who were exposed to, or consumed, extremist content. We carry out three forecasting tasks, (i) to detect extremist users, (ii) to estimate whether regular users will adopt extremist content, and finally (iii) to predict whether users will reciprocate contacts initiated by extremists. All forecasting tasks are set up in two scenarios: a post hoc (time independent) prediction task on aggregated data, and a simulated real-time prediction task. The performance of our framework is extremely promising, yielding in the different forecasting scenarios up to 93 % AUC for extremist user detection, up to 80 % AUC for content adoption prediction, and finally up to 72 % AUC for interaction reciprocity forecasting. We conclude by providing a thorough feature analysis that helps determine which are the emerging signals that provide predictive power in different scenarios.