Algorithms

This article presents methods for identifying the recruitment activities of violent groups within extremist social media websites.

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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.

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This article addresses the degree to which geolocation prediction is vital to geospatial applications like localised search and local event detection.

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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.

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In this paper, the authors argue that despite the widespread use of social media in various domains (e.g.

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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.

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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.

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This article outlines a semi-automated approach for analyzing the content and structure of online hate groups active on blogging platforms.

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This article focuses on the problem of identifying influential Twitter users using Machine Learning (ML) techniques and Natural Language Processing (NLP).

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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.

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