Radical Right Extremism, Online Propaganda and Hybrid Human-Automated Content Removal

This PhD will examine radical right image data (including symbols, emblems, memes and use of colour) found on numerous social media platforms in order to aid criminal justice operatives in their preventative approach to extremism online. The key questions of the project are:

Knowledge Enhancing Questions:

How do radical right groups use images to spread propaganda online?

What are the key nuances of these messages?

And how do the groups perform othering?

Policy Relevant Questions:

How best can human and automated decision-making processes be integrated for the purpose of content removal processes by the criminal justice system?

How effective can a hybrid model be for aiding counter-officials in content removal and preventative tactics in combatting online extremism?

Methodology

Given the sheer volume of content posted on social media platforms, the use of technology is essential for effective removal terrorist content. At the same time, automated decision-making has its limitations. In particular, machines work with data and code; they do not attribute meaning (Hildebrandt 2018). These challenges are exacerbated in the context of the far-right. Unlike content associated with the so-called Islamic State, most radical right content is not branded. Moreover, there has been a shift in the Overton Window, such that some powerful actors - including heads of state, major political parties, some traditional media organisations and broad swathes of Western publics - identify with this content (Conway, 2020). The key to effectiveness therefore lies in the ability to improve hybrid human-automated decision-making (van der Vegt et al, 2019).

As well as effectiveness, it is also important to address the various ethical issues associated with technological management (Brownsword, 2016). These include: providing users with fair warning of what content is and is not permissible, so that they can make informed decisions about their use of the platform; where content is removed, ensuring that users are provided with sufficient information to be able to appeal against the decision should they wish to do so; and, auditing the outcomes of automated decision-making in order to check for possible algorithmic bias (Macdonald et al, 2019).

The data scraped for this project will be analysed using a content analysis methodology. Content analysis utilises both qualitative and quantitative methods to critically analyse audio and visual material (Finch and Fafinski, 2012). Specific ideas, concepts, terms, themes and other image characteristics will be identified, and comparisons made, to allow a detailed description, explanation and analysis of the material. These categories will be generated through a careful reading of the data, as opposed to being pre-defined, thus ensuring an inductive approach. The generation of coding categories from the content analysis will result in a coding manual: a document containing instructions for the coder, so that the process of data analysis is specific, consistent and repeatable. As the coding categories are applied to the data, a coding schedule will be created, i.e. a document containing all the findings related to each item within the sample. This will result in a quantitative dataset, from which conclusions will be drawn and presented using qualitative methods.

There are several benefits to using this approach. Content analysis is a methodology that can be applied to both small and large datasets, and to quantitative and qualitative research. The coding manual will ensure the repeatability of the research and that the findings are robust and verifiable. Finally, the data-driven approach ensures the holistic, inductive and dialogic nature of all findings and conclusions.

The Centre will nurture 55 new PhD researchers who will be highly sought after in technology companies and application sectors where data and intelligence based systems are being developed and deployed. We expect that our graduates will be nationally in demand for two reasons: firstly, their training occurs in a vibrant and unique environment exposing them to challenging domains and contexts (that provide stretch, ambition and adventure to their projects and capabilities); and, secondly, because of the particular emphasis the Centre will put on people-first approaches. As one of the Google AI leads, Fei-Fei Li, recently put it, "We also want to make technology that makes humans' lives better, our world safer, our lives more productive and better. All this requires a layer of human-level communication and collaboration" [1]. We also expect substantial and attractive opportunities for the CDT's graduates to establish their careers in the Internet Coast region (Swansea Bay City Deal) and Wales. This demand will dovetail well with the lifetime of the Centre and provide momentum for its continuation after the initial EPSRC investment.

With the skills being honed in the Centre, the UK will gain a important competitive advantage which will be a strong talent based-pull, drawing in industrial investment to the UK as the recognition of and demand for human-centred interactions and collaborations with data and intelligence multiplies. Further, those graduates who wish to develop their careers in the academy will be a distinct and needed complement to the likely increased UK community of researchers in AI and big data, bringing both an ability to lead insights and innovation in core computer science (e.g., in HCI or formal methods) allied to talents to shape and challenge their research agenda through a lens that is human-centred and that involves cross-disciplinarity and co-creation.

The PhD training will be the responsibility of a team which includes research leaders in the application of big data and AI in important UK growth sectors - from health and well being to smart manufacturing - that will help the nation achieve a positive and productive economy. Our graduates will tackle impactful challenges during their training and be ready to contribute to nationally important areas from the moment they begin the next steps of their careers. Impact will be further embedded in the training programme with cohorts involved in projects that directly involve communities and stakeholders within our rich innovation ecology in Swansea and the Bay region who will co-create research and participate in deployments, trials and evaluations.

The Centre will also impact by providing evidence of and methods for integrating human-centred approaches within areas of computational science and engineering that have yet to fully exploit their value: for example, while process modelling and verification might seem much removed from the human interface, we will adapt and apply methods from human-computer interaction, one of our Centre's strengths, to develop research questions, prototyping apparatus and evaluations for such specialisms. These valuable new methodologies, embodied in our graduates, will impact on the processes adopted by a wide range of organisations we engage with and who our graduates join.

Finally, as our work is fully focused on putting the human first in big data and intelligent systems contexts, we expect to make a positive contribution to society's understandings of and involvement with these keystone technologies. We hope to reassure, encourage and empower our fellow citizens, and those globally, that in a world of "smart" technology, the most important ingredient is the human experience in all its smartness, glory, despair, joy and even mundanity.

[1] https://www.technologyreview.com/s/609060/put-humans-at-the-center-of-ai/