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Many extreme right groups have had an online presence for some time through the use of dedicated websites. This has been accompanied by increased activity in social media websites in recent years, which may enable the dissemination of extreme right content to a wider audience. In this paper, we present exploratory analysis of the activity of a selection of such groups on Twitter, using network representations based on reciprocal follower and mentions interactions. We find that stable communities of related users are present within individual country networks, where these communities are usually associated with variants of extreme right ideology. Furthermore, we also identify the presence of international relationships between certain groups across geopolitical boundaries.

Research into the international agreements that increase cooperation over cybersecurity challenges is severely lacking. This is a necessary next step for bridging diplomatic challenges over cybersecurity. This work aspires to be push the bounds of research into these agreements and offer a tool that future researchers can rely on. For this research I created, and made publicly available, the International Cybersecurity Cooperation Dataset (ICCD), which contains over 350 international cybersecurity agreements and pertinent metadata. Each agreement is marked per which subtopics within cybersecurity related agreements it covers. These typologies are:
• Discussion and Dialogue
• Research
• Confidence Building Measures
• Incident Response
• Crime
• Capacity Building
• Activity Limiting
• Defense
• Terrorism
Drawing on ICCD and R for summary statistics and significance tests, as well as some quantitative insights, this research explores the relationship between different agreements, organizations, and other possibly related factors. The most significant takeaways from this research are:
1. Governments view cybersecurity in terms of relative advantages and are hesitant to engage competitors with agreements over topics like incident response and capacity building.
2. Authoritarian governments are involved with agreements over controlling or projecting state power and government authority while democratic governments focus on resilience and defense.
3. There are two groupings of authoritarian governments, those with high technical capabilities and those without. Technically capable governments focus on agreements over terrorism, and they also often end up participating in activity limiting agreements. Those without are preoccupied with agreements over criminal activity.
4. Discussion and dialogue agreements tend to accompany agreements over additional topics about one fifth of the time. While policy-makers shouldn’t create a hard rule out of this statistic, it does possibly strengthen an optimistic hypothesis that dialogue consistently leads to agreements.
Hopefully this research invigorates researchers’ interest in studying and understanding when cooperation over cybersecurity is successful or not. Policy-makers will need this knowledge if they are to achieve their goals in an environment that is rapidly increasing in state actors and
complexity

The “Islamic State in Iraq and Syria” (ISIS) is the main source of instability, not only in Iraq and Syria, but also throughout the Middle East. The instability poses a danger for the other parts of the world because of the influx of foreign fighters to the region. Extremists have taken advantage of the continuing conflicts in Iraq and Syria, with Syria in particular serving as a magnet for thousands of foreign fighters from more than 90 countries. While most of these ISIS combatants are men, many women have left their countries behind to join the “caliphate” and support its cause. Social media have played a key role in luring women to join ISIS. This study therefore analyzed the ISIS organization’s social media propaganda and grass-roots recruitment activities aimed at women in Turkey. The results of the analysis provide important information about the strategies that ISIS uses to spread its ideology.

This article analyses Dabiq magazine to explore the strategic logic of Islamic State (IS) appeals to English-speaking Muslims. It offers the field a conceptual framework through which to analyse IS’s communications strategy and a top-down empirical study of Dabiq’s contents. This paper argues that Dabiq appeals to its audiences by strategically designing in-group identity, Other, solution and crisis constructs which it leverages via value-, crisis- and dichotomy-reinforcing narratives. By fusing identity- and rational-choice appeals, IS provides its audiences with a powerful ‘competitive system of meaning’ that is designed to shape its readership’s perceptions, polarise their support and drive their radicalisation.

The Online Social Network (OSN) has evolved as a popular platform enabling rich topic-centric interactions and serving as a medium to facilitate online radicalization (Behr et al. 2013). Keeping in view the growing need of uncovering online radicalization, we focus on the information network of Twitter and present an approach for identifying dynamic communities, which arise due to “radical” topic-centric user interactions. The approach at successive timestamps deploys evolving topic-entity maps along with evolving interaction graphs. We propose “Rate of Overlap (ROAct)” to determine the similarity among successive community timestamps. We further validate our approach using an open dataset of criminal offences in the city of Denver, Colorado. The approach presented is simple, fast, and effective for dynamic identification of topic-centric communities and, thus, will enable law enforcement agencies to identify hidden radicalization.

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.