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Since the 2016 US Presidential Election, extreme right-wing communities have gained extensive popularity on YouTube, spreading discourses of white supremacy and conspiracy. This paper focuses on how methods drawn from Systemic Functional Linguistics (SFL) can be used to analyze this communication and contribute to research interests within the field of media and communication studies. SFL is a social semiotic model of language concerned with systematic analysis of language choices in terms of their social context. More specifically, this paper draws upon the Appraisal and Affiliation frameworks developed within SFL, in order to understand how patterns of evaluation are expressed in language and how these function in terms of aligning ambient audiences with particular values. YouTube videos and comments about the Notre Dame Cathedral Fire are used as a case study. The aim is to illustrate how this approach can offer an additional perspective on the issues of information disorder and hate speech that does not attempt to homogenize the multiple reasons why people engage in such hateful behavior.

The design of the Internet has made it an especially useful tool to terrorist groups. Using the Internet, terrorist groups have been especially successful in recruiting new members and exciting them into action. Previous works have provided comprehensive accounts of ways that terrorists use the Internet. This article expands on the understanding of this phenomenon by framing the previous works in a systematic model of terrorist’s use of the Internet through social learning theory. The article also offers counterterrorist strategies in accordance with the components of social learning.

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.

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.

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.

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.