Since the beginning of her career Dr Olivia (Liv) Brown has been researching how to prevent – and improve the response to – terror attacks.

The focus of her work is how groups interact in various contexts. This has many uses: from helping emergency services communicate better, to deciding whether someone exhibiting worrying behaviour online should be more closely monitored.

In recent years, her work has become ever more relevant; it is now estimated that an alarming 40% of users encounter extremist content online every day. And in summer 2024, the UK was rocked by a nine day-long surge of violent far-right protests and riots.

Liv's recent work has focused on identifying those most likely to undergo 'mobilisation' – the process of transitioning from online discussion to offline action.

She led a study – funded by the UK's national Centre for Research and Evidence on Security Threats (CREST) – to develop a model that can predict which users are most likely to go on to commit violent offences, based on their online footprint.

In the study, Liv and her fellow researchers analysed data set of over 200,000 social media posts from both people convicted of far-right terrorism offences and those who expressed far-right views online but had no convictions.

The research involved a lot of investigative work – reading court records, news reports and scouring the internet – to find terrorists' online data. They then compared that to other hateful or extremist content online, to reveal what’s different about the way that actual offenders communicate. This meant the laborious, manual analysis of hundreds of thousands of online posts.

Liv says:

"By going through post by post you generate an understanding of what’s really important. You understand how and why people are using terms in different contexts, rather than just using a machine to track patterns."

The study determined a set of indicators from these posts that could identify whether the user had been convicted of real-world actions. Surprisingly, this didn't correlate with the hateful views expressed.

Instead, signs that a user was more likely to mobilise include:

• Discussion of logistics
• Operational planning
• Knowledge of weapons
• Talk of avoiding law enforcement
• Emphatic use of punctuation, such as repeated exclamation marks

These markers could be seen years before the user actually went on to commit offences.

The publication of Liv and her colleagues' paper, 'Online Signals of Extremist Mobilization' in Personality and Social Psychology Bulletin, coincided with the outbreak of far-right riots across the UK, fuelled by online mis- and disinformation.

As a result, Liv was invited to provide expert comment and interviews across a range of media outlets, including:

• BBC News
• The Financial Times
• LBC
• The Conversation

Liv's research has also been used to inform practitioner events and workshops, including for UK police forces and intelligence agencies.

An understanding of the real-world application of research is essential to Liv’s work on online extremism. It allows her to tailor what she’s doing to plug existing gaps in the way security services approach radicalisation.

"My main concern is to translate what we’re doing into workable tools or resources that the security services can use. Resources are finite, and they can’t monitor everyone. In fact, they shouldn’t, for ethical reasons. We want to help them establish clearer markers or risk signals for what might turn into genuine violence so that they can prioritise."

Liv stresses that that algorithms such as this should not act as the sole tool in security services' arsenals, but rather a component in a toolkit that helps the keep the public safe.

She explains:

“The sheer volume of extremist content online means that identifying people most likely to cause harm is like finding a needle in a haystack. We have pinpointed signals of risk to make the haystack smaller and the needle bigger, which can be used to prioritise monitoring resources on a smaller pool of people who we think are more likely to act.

Impact is central to Liv’s approach to research. She originally wanted to become a clinical psychologist. But a module on counter terrorism during her psychology degree changed everything. Her interest stems from the importance of the work, and how she can use her skills to make a difference.

"For me research has always been about doing something useful. While research for research’s sake is important, I’m personally more motivated by real-world outcomes. How can somebody use what I’ve done? How will that impact on tangible things in the real world?"

She was also heavily influenced by the ethos of CREST, who funded both her PhD and postdoctoral research.

"Working with CREST really affected how I approach my work, as they are so outcome focused. It makes me consider how this research can be useful to practitioners, and not just seeing impact as being published in a top journal."

Though Liv’s mixed methods approach to the project was invaluable in creating a more intelligent algorithm, it also meant that she was exposed to some of the internet’s most toxic and extreme content.

Liv found trawling through all the racism, homophobia and hate difficult. But she was able to stay strong by reminding herself of the value of the work.