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“Twister,” developed by Miguel Freitas, is a social network platform centered around micro-blogging, much like Twitter. However, rather than relying on centralized servers owned and maintained by a single firm, Twister users operate a block chain combined with distributed hash table (DHT)–like and BitTorrent-like protocols to both make posts and send private messages, and also to receive entries from other users. Twister’s raison d’etre is that it offers a social networking platform that cannot be censored and cannot itself censor. The software does not record the Internet Protocol addresses users use to access the service, nor does it notify other users of an account’s online/offline status. Growing adoption of block chain services means that it is possible that the concept of decentralized social networks could become a norm. It is suggested in this article that block chain-based peer-to-peer social networks present challenges to the current counterextremist practices for content removal and censorship. While there are methods to disrupt usage of blockchainbased peer-to-peer services, these approaches may have the net harm of curtailing bona fide use of legal and novel technologies. Given this opportunity cost, non-transitory online violent extremist content may need to be tolerated.

This policy brief explores new and innovative communications approaches to reduce the threat from all forms of violent extremism in Europe today based on a precise analysis of the strategic problem and corresponding strategic communications solutions deployable in response. In the context of an ever-evolving violent extremist landscape, including new and emerging forms of violent extremism, unfolding events in Israel and Gaza, and an accelerating digital environment – this policy brief looks at how governments can take a more strategic approach to preventing and countering violent extremism (P/CVE). Based on a strategic analysis of current events and an informed analysis of previous policy responses, this policy brief offers a new and practical approach to the use of strategic communications aimed at safeguarding all communities from all forms of extremist and violent threats, to turn back the tide of extremist influence for good.

This paper focuses on the attempts by Daesh (also known as the Islamic State of Iraq and Syria, ISIS) to use Twitter to disseminate its online magazine, Rumiyah. It examines a data set of 11,520 tweets mentioning Rumiyah that contained an out link, to evaluate the success of Daesh’s attempts to use Twitter as a gateway to issues of its magazine.

The scientific study of hate speech, from a computer science point of view, is recent. This survey organizes and describes the current state of the field, providing a structured overview of previous approaches, including core algorithms, methods, and main features used. This work also discusses the complexity of the concept of hate speech, defined in many platforms and contexts, and provides a unifying definition. This area has an unquestionable potential for societal impact, particularly in online communities and digital media platforms. The development and systematization of shared resources, such as guidelines, annotated datasets in multiple languages, and algorithms, is a crucial step in advancing the automatic detection of hate speech.

Extremism has grown as a global problem for society in recent years, especially after the apparition of movements such as jihadism. This and other extremist groups have taken advantage of different approaches, such as the use of Social Media, to spread their ideology, promote their acts and recruit followers. The extremist discourse, therefore, is reflected on the language used by these groups. Natural language processing (NLP) provides a way of detecting this type of content, and several authors make use of it to describe and discriminate the discourse held by these groups, with the final objective of detecting and preventing its spread. Following this approach, this survey aims to review the contributions of NLP to the field of extremism research, providing the reader with a comprehensive picture of the state of the art of this research area. The content includes a first conceptualization of the term extremism, the elements that compose an extremist discourse and the differences with other terms. After that, a review description and comparison of the frequently used NLP techniques is presented, including how they were applied, the insights they provided, the most frequently used NLP software tools, descriptive and classification applications, and the availability of datasets and data sources for research. Finally, research questions are approached and answered with highlights from the review, while future trends, challenges and directions derived from these highlights are suggested towards stimulating further research in this exciting research area.

This paper presents a survey on hate speech detection. Given the steadily growing body of social media content, the amount of online hate speech is also increasing. Due to the massive scale of the web, methods that automatically detect hate speech are required. Our survey describes key areas that have been explored to automatically recognize these types of utterances using natural language processing. We also discuss limits of those approaches.