By Alex Fradera
By the time a terrorist attack has begun, the security services have already failed. But the challenge they face in detecting potential attacks is substantial, especially since the tactic of terrorism has increasingly been taken up by individual attackers inspired by, but not directly beholden to, formal movements. Spotting a lone wolf among the flock is no easy task, especially when it relies on a bottleneck of human analysis. A new paper in the journal Aggression and Violent Behavior uses a test case of a real lone wolf attack to explore ways we may be able to deal with this in the future. Using online language analysis tools, it hunts within blocks of text for the warning signs we might otherwise miss, with the hope of helping us to more effectively detect the predator.
Giti Zahedzadeh of the Centre for Neuroeconomic Studies at Claremont Graduate University in California wanted to see what insights could be gleaned about violent tendencies by analysing text using the open-source web application Voyant. The application has a number of features such as creating word clouds, word frequency lists, and word association chains to pull out patterns from blocks of text.
The raw data Zahedzadeh used were writings by Nidal Hasan, the US military psychiatrist who turned traitor and went on an ideologically motivated mass shooting in 2009 in Fort Hood, Texas, killing 13 and injuring many more. Hasan, who is now incarcerated awaiting execution, received some attention from authorities before his crime but he was never classified as “being involved in terrorist activities,” leaving him a free hand to commit his attack. Were warning signs missed?
The Voyant programme was first applied on a powerpoint presentation Hasan gave two years before the attack, as part of the final stage of his medical training. He chose the topic “The Qur’anic World View As It Relates to Muslims in the U.S. Military.” Its purpose was ostensibly to explore the topic to inform policy and treatment, but with the benefit of hindsight, one can see an individual working out in public his own misgivings and sounding out justifications for a course of action. Using Voyant, Zahedzadeh was able to visually zoom through key words and explore their associations, such as “fight”, “cause”, and “important”, and generate a word cloud highlighting the prominence of the words “god” and “submission” in one of the sections.
Perhaps more interesting is the email exchange between Hasan and a radical cleric, Anwar al-Awlaki (who was later killed by a US drone strike in Yemen in 2011). Al-Awlaki presided over the funeral of Hasan’s mother some years prior to Hasan’s atrocity, but had since moved to Yemen to continue his activities with less interference. The exchange shows Hasan testing out ideas and probing whether certain violent actions could be consistent with Islamic law. These messages were picked up at the time by the Joint Terrorism Task Force but ultimately not acted on. However, the Voyant analysis pulled out a number of red flags, such as a high coincidence of the term “permissible” in the context of the word “suicide”, and the pairing of “intention”, “kill,” and “soldiers” – the latter precisely reflecting the act Hasan was to go on to conduct.
Again, reading the text with the benefit of hindsight, the warning signs feel like they jump out at you without the need to collate word patterns, but of course, it’s easy to spot signs of something once you know about it. The purpose of using a technology approach is to foreground patterns that may seem otherwise innocuous, objectively presenting frequencies and interconnections in black and white. Moreover, gathering this kind of data into a larger corpus, based upon many cases, could help us understand “the process in which an individual moves from radical opinion to violent action,” Zahedzadeh said.