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Because it mostly exists online, the Alt Right does not have the infrastructure needed to launch a guerrilla war (as Nazi/Klan forces did in the 1980s) or build pseudo-state institutions (as Patriot groups did in the 1990s and are attempting again now), but it is in a strong position to pursue a “metapolitical” transformation of the political culture and thereby lay the groundwork for structural change, centered on its vision of a White ethnostate.

During the 2016 election, a new term entered the mainstream American political lexicon: “alt-right,” short for “alternative right.” Despite the innocuous name, the alt-right is a white-nationalist movement. Yet it differs from earlier racist groups: it is youthful and tech savvy, obsessed with provocation and trolling, amorphous, predominantly online, and mostly anonymous. And it was energized by Donald Trump’s presidential campaign. In Making Sense of the Alt-Right, George Hawley provides an accessible introduction and gives vital perspective on the emergence of a group whose overt racism has confounded expectations for a more tolerant America.

To prevent and counter the spread of illegal hate speech online, in May 2016, the Commission agreed with Facebook, Microsoft, Twitter and YouTube a “Code of conduct on countering illegal hate speech online”. The implementation of the Code of Conduct is evaluated through a regular monitoring exercise set up in collaboration with a network of organisations located in the different EU countries. Using a commonly agreed methodology, these organisations test how the IT companies are implementing the commitments in the Code.

The Islamic State of Iraq and ash-Sham (ISIS) continues to use social media as an essential element of its campaign to motivate support. On Twitter, ISIS’ unique ability to leverage unaffiliated sympathizers that simply retweet propaganda has been identified as a primary mechanism in their success in motivating both recruitment and “lone wolf” attacks. The present work explores a large community of Twitter users whose activity supports ISIS propaganda diffusion in varying degrees. Within this ISIS supporting community, we observe a diverse range of actor types, including fighters, propagandists, recruiters, religious scholars, and unaffiliated sympathizers. The interaction between these users offers unique insight into the people and narratives critical to ISIS’ sustainment. In their entirety, we refer to this diverse set of users as an online extremist community or OEC. We present Iterative Vertex Clustering and Classification (IVCC), a scalable analytic approach for OEC detection in annotated heterogeneous networks, and provide an illustrative case study of an online community of over 22,000 Twitter users whose online behavior directly advocates support for ISIS or contibutes to the group’s propaganda dissemination through retweets.

As the body of research on abusive language detection and analysis grows, there is a need for critical consideration of the relationships between different subtasks that have been grouped under this label. Based on work on hate speech, cyberbullying, and online abuse we propose a typology that captures central similarities and differences between subtasks and we discuss its implications for data annotation and feature construction. We emphasize the practical actions that can be taken by researchers to best approach their abusive language detection subtask of interest.

Common approaches to text categorization essentially rely either on n-gram counts or on word embeddings. This presents important difficulties in highly dynamic or quickly-interacting environments, where the appearance of new words and/or varied misspellings is the norm. A paradigmatic example of this situation is abusive online behavior, with social networks and media platforms struggling to effectively combat uncommon or nonblacklisted hate words. To better deal with these issues in those fast-paced environments, we propose using the error signal
of class-based language models as input to text classification algorithms. In particular, we train a next-character prediction model for any given class, and then exploit the error of such class-based models.
to inform a neural network classifier. This
way, we shift from the ability to describe
seen documents to the ability to predict
unseen content. Preliminary studies using out-of-vocabulary splits from abusive
tweet data show promising results, outperforming competitive text categorization
strategies by 4–11%.