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The Internet has become an important tool for modern terrorist groups as a means of spreading their propaganda messages and recruitment purposes. Previous studies have shown that the analysis of social signs can help in the analysis, detection, and prediction of radical users. In this work, we focus on the analysis of affect signs in social media and social networks, which has not been yet previously addressed. The article contributions are: (i) a novel dataset to be used in radicalization detection works, (ii) a method for utilizing an emotion lexicon for radicalization detection, and (iii) an application to the radical detection domain of an embedding-based semantic similarity model. Results show that emotion can be a reliable indicator of radicalization, as well as that the proposed feature extraction methods can yield high-performance scores.

The dramatic growth of the Web has motivated researchers to extract knowledge from enormous repositories and to exploit the knowledge in myriad applications. In this study, we focus on natural language processing (NLP) and, more concretely, the emerging field of affective computing to explore the automation of understanding human emotions from texts. This paper continues previous efforts to utilize and adapt affective techniques into different areas to gain new insights. This paper proposes two novel feature extraction methods that use the previous sentic computing resources AffectiveSpace and SenticNet. These methods are efficient approaches for extracting affect-aware representations from text. In addition, this paper presents a machine learning framework using an ensemble of different features to improve the overall classification performance. Following the description of this approach, we also study the effects of known feature extraction methods such as TF-IDF and SIMilarity-based sentiment projectiON (SIMON). We perform a thorough evaluation of the proposed features across five different datasets that cover radicalization and hate speech detection tasks. To compare the different approaches fairly, we conducted a statistical test that ranks the studied methods. The obtained results indicate that combining affect-aware features with the studied textual representations effectively improves performance. We also propose a criterion considering both classification performance and computational complexity to select among the different methods.

Hate speech may take different forms in online social media. Most of the investigations in the literature are focused on detecting abusive language in discussions about ethnicity, religion, gender identity and sexual orientation. In this paper, we address the problem of automatic detection and categorization of misogynous language in online social media. The main contribution of this paper is two-fold: (1) a corpus of misogynous tweets, labelled from different perspective and (2) an exploratory investigations on NLP features and ML models for detecting and classifying misogynistic language.

An analysis of Harakat al-Shabab’s multimedia releases and the rapid evolution in their production quality and design over a period of two to three years. Sound quality, animation, syncing sound with visuals, and narrative structures have all improved from the group’s multimedia releases from 2007 and 2008 when its videos were relatively simple, often just individuals sitting in front of a video camera, possibly just a camcorder, and grainy battle footage depicting fierce firefights between Harakat al-Shabab and the interim Somali government and its chief military backers, the African Union expeditionary force stationed inside the country.

The Somali jihadi-insurgent movement Al-Shabab has established itself, since emerging in 2007 after the overthrow of the Islamic Courts Union (ICU) umbrella in the wake of the December 2006 Ethiopian invasion and occupation of parts of Somalia, as one of the relatively few jihadi organizations to succeed in the capture, control, and governance of territory for a significant period of time. When Islamic State was masquerading as a «paper state» in 2008 and 2009, Al-Shabab’s leadership was busy constructing a bureaucracy of power, divided into regional and local nodes of authority, designed to implement and maintain insurgent rule over rapidly expanding territories. In establishing itself as a self-proclaimed and seemingly viable alternative governing authority, even if only in the short to medium term, Al Shabab continues to present a major challenge to the internationally recognized but weak and corrupt Somali Federal Government (SFG), the African Union Mission in Somalia (AMISOM), and the international community. The Somali insurgents, in successfully implementing a form of law and order, however harsh and philistine their interpretation of Islamic law and specifically their imposition of hudud («set») punishments for crimes such as highway robbery, banditry, theft, zina (various forms of fornication), and murder, provided other Sunni jihadi groups with an example of how jihadi-insurgent governance can be enacted in practice.

Over the course of the 2016 Presidential campaign, an execrable trend has emerged: reporters who voiced even slightly negative opinions about presidential candidate Donald Trump have been targeted relentlessly on social media by the candidate’s self-styled supporters; reporters who are Jewish (or are perceived to be Jewish) have borne the brunt of these attacks. There is evidence that Mr. Trump himself may have contributed to an environment in which reporters were targeted. Indeed, he repeatedly denounced reporters as “absolute scum,” and said of “most journalists” in December 2015, “I would never kill them, but I do hate them. And some of them are such lying, disgusting people. It’s true.” Accordingly, while we cannot (and do not) say that the candidate caused the targeting of reporters, we can say that he may have created an atmosphere in which
such targeting arose.

The social media attacks on journalists were brutal.