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Analysing violent extremist communications and their uptake in the media

Research team: Prof. Kay O’Halloran, Dr. Sabine Tan and Dr. Peter Wignell
CIC specialists: Dr. Rebecca Lange and Dr. Kevin Chai

In the Multimodal Analysis Group within the Faculty of Humanities, Prof. Kay O’Halloran, Dr Sabine Tan and Dr Peter Wignell analyse ‘multimodal discourse’ – how the combination of language and images function within a specific context to create nuanced meaning.  A current study analyses how violent extremist groups such as Islamic State (ISIS) use images and text to legitimise their views, incite violence and influence potential recruits and supporters in online propaganda materials.

The task of analysing electronic media has now moved beyond the human scale, however.  Classifying and analysing the various dimensions of a piece of human communication is a painstaking manual effort: it cannot be easily scaled up to track the patterns of communication that precede and develop from it over time across different groups.

The group is now collaborating with Dr Rebecca Lange and Dr Kevin Chai in the Curtin Institute for Computation to apply automated natural language processing, computer vision and machine learning to this problem.  The aim is to automate the contextual classification of key features in text and key objects in images as much as possible within a multimodal discourse framework, to track how extremist communications are spread and re-interpreted by different audiences over time.

In a pilot study, the multidisciplinary team investigated the nature of images that appear in ISIS’s official propaganda magazines Dabiq and Rumiyah, and how this source material is then reused and recontextualised across public online media sites such as news websites, blogs, and social media.

From three years’-worth of propaganda content, 537 articles were manually categorised using multi-modal discourse analysis into 20 different article types, with 1,575 embedded images assigned to 8 major and 69 sub-categories.  The best commercially-available computing tools were then put to work.  Image analysis software was used to conduct a ‘reverse image’ search of a selected subset of 26 propaganda images across the public web, leading to the identification of 8,832 websites that republished the content in some form.  Only selecting downloadable pages in English reduced the sample set to 3,840 websites.  Natural language processing models then automatically categorised the text into a hierarchy of categories covering both content (war, sport, humour, politics…) and tone (formality of language, positive or negative associations).

CIC expertise was also used to analyse and visualise this network of connections to identify patterns developing between the original ISIS propaganda and its repurposing across the web over time.

Using these tools, the team has been able to study how extremist communications work: what types of images are effective, how different groups react to them, and how they re-use and re-publish them, giving them new meanings over time.

Among English-language websites, ISIS propaganda images recirculated most frequently on Western news and politics websites and official webpages and blogs, in predominantly formal contexts.  However the images, when considered in relation to the accompanying texts, can have the effect of inadvertently legitimising ISIS’s values and agenda.  Images often have more impact than text – a very positively coded image, for example ISIS militants all celebrating, wearing army fatigues and carrying flags, can strengthen their iconographic status, and counteract the text accompanying it that may denounce their actions.

This pilot study is a first step in understanding the communication strategies employed by violent extremists, and the patterns of recontextualisation in the spread of their images in online media, with the aim of developing effective strategies for countering them.  This work also complements existing efforts to track and remove online violent and extremist content.

Modern communications have revolutionised how fast information and ideas can spread and change.  But improvements in harnessing natural language processing, computer vision and machine learning within a multimodal discourse analysis framework can improve our understanding of human issues and how they evolve in any domain – from violent extremists to politics, advertising or the latest pop-culture memes.