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Hateful and antagonistic content published and propagated via the World Wide Web has the potential to cause harm and suffering on an individual basis, and lead to social tension and disorder beyond cyber space. Despite new legislation aimed at prosecuting those who misuse new forms of communication to post threatening, harassing, or grossly offensive language – or cyber hate – and the fact large social media companies have committed to protecting their users from harm, it goes largely unpunished due to difficulties in policing online public spaces. To support the automatic detection of cyber hate online, specifically on Twitter, we build multiple individual models to classify cyber hate for a range of protected characteristics including race, disability and sexual orientation. We use text parsing to extract typed dependencies, which represent syntactic and grammatical relationships between words, and are shown to capture ‘othering’ language – consistently improving machine classification for different types of cyber hate beyond the use of a Bag of Words and known hateful terms. Furthermore, we build a data-driven blended model of cyber hate to improve classification where more than one protected characteristic may be attacked (e.g. race and sexual orientation), contributing to the nascent study of intersectionality in hate crime.

US Department of Defense Anti-Terrorism Handbook 2004

Several alternative social media platforms have emerged in response to perceptions that mainstream platforms are censoring traditional conservative ideologies. However, many of these alternative social media platforms have evolved to be outlets for hate speech and violent extremism. This study examines hate-based channels on Telegram from a US perspective. While Telegram has often been studied in relation to ISIS, less is known about its usage by US extremist users and movements. The authors used OSINT and observational methods on a sample of 125 Telegram channels containing hate speech and violent extremist content from far-right and far-left perspectives. The authors hypothesized that there would be a greater and growing presence of far-right activity compared to farleft activity due to current migration trends away from mainstream social media by the far-right. The authors also sought to observe the presence of disinformation campaigns, conspiracy theories, and accelerationism on Telegram. This study had four major findings: (1) the findings supported the hypothesis that more channels were host to farright dialogues, yet there were several far-left channels present, (2) 64.8% of the channels grew in size over a oneweek period, (3) 47 of the 125 channels were connected to well-known violent extremist movements or hate groups, and (4) QAnon and the COVID-19 pandemic were the most prominent sources of disinformation and conspiracy theories on Telegram. The findings of this study highlight that alternative social media platforms are a growing environment for a range of hateful ideologies and are aiding the spread of disinformation campaigns. This study concludes with a discussion on future strategies to combat the influence of the Internet on radicalization outcomes.

The potential of terrorists and other enemies to use the Internet and/or other digital information technologies as transport vehicles of weapons of mass destruction (WMD) is becoming more evident. This threat may involve cyber-assisted biochemical transactions, which can be performed through olfactory information exchanges, to attack civilian and military targets anytime. In response, this research is the product of a four-year investigation wherein the researcher went through a rich archive of declassified U.S. military reports (covering biochemical warfare and techno-terrorism)and a selective list of patent applications revealing several concepts related to users’ adoption of emergent cyber-assisted olfaction and aroma-embedded information systems. And after a rigorous analysis of the findings, the researcher hypothesized that careless adoption of unsafe technologies could accelerate a cyber-WMD-nexus.

Thus, the study conducted in support of this research sought to examine and explain the adoption of emergent cyber-assisted olfactory information technologies. Cyber-assisted olfaction is an emergent technology that involves the replication of the human sense of smell by computer systems. It can be manifested in the stimulation of human olfactory receptors by computer-emitted biochemical or aromatic particles. Certain olfaction-based biosensors, known as electronic noses, mimic the sense of smell to detect chemical plumes (aroma or warfare agents) in a variety of settings. Notably, the literature revealed an innovative dimension of multimodal, affective human-computer interactions involving scented emails, aroma therapy, and olfaction-based marketing. Unfortunately, the lack of security and safety persists at a potentially catastrophic cost, which is at the root of the researcher’s stated research problem below.

Largely supported by Everett Rogers’ innovation diffusion theory (IDT) and Fred Davis’ technology acceptation model (TAM), the researcher went beyond the “smell-o-vision” aphorism to offer a fertile ground for a new, comprehensive, and flexible framework to understand various aspects of cyber-assisted olfactory information systems, which may be usefully applied to a wide range of auxiliary information systems (i.e., standard, micro, and nano devices). In relation to national security and defense, the proposed Model for Safer Cyber-Assisted Olfaction (MSCAO) can be applied in subway systems, aircrafts, cruise ships, and various mixing/distribution systems (i.e., water systems and chemical plants) to automatically detect and neutralize a biological or chemical threat. Many military applications are also anticipated for emerging urban warfare and emergency rescue operations using devices such as miniature robots and nano air vehicles (NAVs).

Consistent with the aim of this investigation, the researcher used a mixedmethods approach that combined (1) the content analysis of a series of declassified military reports on airborne warfare agents with that of (2) a patent search on aroma embedded information systems to examine, with the help of a carefully designed survey instrument, the perceptions of potential adopters of the proposed MSCAO. Further, in order to collect data and obtain statistical evidence linked to the selected variables (perceived credibility, ease-of-use, and usefulness), the researcher surveyed a diverse group of students from a major Engineering School in southeastern United States, which offers degree programs in Mechanical, Electrical, Chemical and Biomedical Engineering. Based on the analysis performed, credibility, ease-of-use, and usefulness were found to be significant enough to be considered as positively influencing the acceptability of the MSCAO. In essence, the respondents expressed a significantly high level of need for harm-reduction and cyber security measures against potential threats and considered the MSCAO as meeting their need.

Perceived usefulness (67%) was identified as the principal determinant of the respondents’ intention to adopt the proposed model. Though less substantial than perceived usefulness, perceived ease-of-use (56%) appeared to have a greater influence on the MSCAO than perceived credibility (54%). In sum, extrapolating from the findings reported herein, the researcher concluded that potential users are more likely to accept an emergent technology that is useful, easy-to-use, and that offers credible harm-reduction measures.

Recent terrorist attacks by first- and second-generation immigrants in the United States and Europe indicate that radicalization may result from the failure of ethnic integration—or the rise of intergroup prejudice in communities where “home-grown” extremists are raised. Yet, these community-level drivers are notoriously difficult to study because public opinion surveys provide biased measures of both prejudice and radicalization. We examine the relationship between anti-Muslim and pro-ISIS (Islamic State of Iraq and Syria) Internet searches in 3099 U.S. counties between 2014 and 2016 using instrumental variable models that control for various community-level factors associated with radicalization. We find that anti-Muslim searches are strongly associated with pro-ISIS searches—particularly in communities with high levels of poverty and ethnic homogeneity. Although more research is needed to verify the causal nature of this relationship, this finding suggests that minority groups may be more susceptible to radicalization if they experience discrimination in settings where they are isolated and therefore highly visible—or in communities where they compete with majority groups for limited financial resources. We evaluate the validity of our findings using several other data sources and discuss the implications of our findings for the study of terrorism and intergroup relations, as well as immigration and counterterrorism policies.

Twitter is the largest and most popular micro-blogging website on Internet. Due to low publication barrier, anonymity and wide penetration, Twitter has become an easy target or platform for extremists to disseminate their ideologies and opinions by posting hate and extremism promoting tweets. Millions of tweets are posted on Twitter everyday and it is practically impossible for Twitter moderators or an intelligence and security analyst to manually identify such tweets, users and communities. However, automatic classification of tweets into pre-defined categories is a non-trivial problem problem due to short text of the tweet (the maximum length of a tweet can be 140 characters) and noisy content (incorrect grammar, spelling mistakes, presence of standard and non-standard abbreviations and slang). We frame the problem of hate and extremism promoting tweet detection as a one-class or unary-class categorization problem by learning a statistical model from a training set containing only the objects of one class . We propose several linguistic features such as presence of war, religious, negative emotions and offensive terms to discriminate hate and extremism promoting tweets from other tweets. We employ a single-class SVM and KNN algorithm for one-class classification task. We conduct a case-study on Jihad, perform a characterization study of the tweets and measure the precision and recall of the machine-learning based classifier. Experimental results on large and real-world dataset demonstrate that the proposed approach is effective with F-score of 0.60 and 0.83 for the KNN and SVM classifier respectively.