State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism

Overview This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.  The paper is structured as follows: Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS). Part 2 provides an introduction to the key approaches of social media intelligence (henceforth ‘SOCMINT’) for counter-terrorism. Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored. Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work

Efficient Image Ranking in Heterogeneous Social Media Networks

Social media websites such as Flickr and Facebook are pervading our lives today. Thesefast-evolving Internet communities are characterized of the presence of large amounts ofimages and videos, which has opened up interesting research avenues within the multimediaand computer vision domains. Social media data is highly interconnected andheterogeneous and associated with a variety metadata (e.g. HTML tags). In this thesis,we investigate three important problems in efficient image ranking in the contextof heterogeneous social media networks. We first study the problem of image and tagco-ranking by utilizing graph structures in image collections and orderless tags. A prototypeof exploring mutually reinforcing relationships between image and tag graphs isdeveloped which is immediately applicable to image/tag ranking and significantly booststhe performance compared with previous work. In real-world image search engines, imagesfrom databases are returned ordered by their relevance to the issued query. There isoften significant redundancy in the top-matching images; it would be desirable to removethe redundancy and present a more diverse range of results, to better cover the searchtopic. To address the problem of diversifying image search results, we develop a novelyet efficient framework, based on non-uniform matroid constraints, to jointly capturethe relevance and diversity. Finally, we study the problem of landmark photo retrievalover social media networks. Observing that a landmark query issued by a specific usercannot generally display distinctive landmark features, we develop novel algorithms toexpand the unary query to be a multi-query set over which regular landmark featurescan be mined out. An effective landmark specific mid-level representation is presented to support retrieving relevant landmark photos in a scaled way