This article presents methods for identifying the recruitment activities of violent groups within extremist social media websites.
This article deals with a supervised machine learning text classifier, trained and tested to distinguish between hateful and/or antagonistic response with a focus on race, ethnicity or religion; and more general responses.
This article addresses the degree to which geolocation prediction is vital to geospatial applications like localised search and local event detection.
This paper concerns the creation of a prototype for sentiment analysis, capable of discerning key aspects of an entity under review, and the type of polarity in the response associated with it.
In this paper, the authors argue that despite the widespread use of social media in various domains (e.g.
This paper describes the methodology that the authors have developed for the collection and sampling of conversational threads, as well as the tools they have developed to identify rumour-based threads.
This paper focuses on social network analysis (SNA) for the purposes of community detection using a hybrid graph-based tag clustering scheme (HGC). The authors present a novel scheme for graph-based clustering with the goal of identifying groups of related tags in folksonomies.
This article outlines a semi-automated approach for analyzing the content and structure of online hate groups active on blogging platforms.
This article focuses on the problem of identifying influential Twitter users using Machine Learning (ML) techniques and Natural Language Processing (NLP).
This article focuses on the use of linguistic “weak signals”—digital traces of intent—in social media as a tool of counterterrorism aimed at preventing lone-wolf attacks.
This portal gathers an annotated collection of recent research on the ways in which social media and new technologies may be leveraged in the fight against violent extremism