Automatic Detection of Cyber-Recruitment by Violent Extremists
security-informatics.com
security-informatics.com
One-dimensional "extremism." Can they detect "white power" recruitment? Environmental extremist recruitment? It seems too easy to rate "angry and in the Middle East" as "terrorist."
If it's too easy to score a hit, it may be too easy to avoid a false positive. What's the system's real capability of avoiding a false positive?
There is no comparison to current "best practices" such as they are. How does the FBI select extremists for surveillance? Does this do a better job?
I assess from the paper that they're specifically looking for Islamist extremist recruiters and not even considering any other type of extremists in this model.
Overfitting to suspect people with Arabic names would be a concern. (You know the classic 20 Newsgroups classification problem? Any algorithm that does particularly well at it is just overfitting to the names and signatures of people who posted in each newsgroup.)
One reason I mentioned other kinds of extremists is their method of using judges to manually rate posts. It could well be that Americans, in particular, have a media-formed consensus on what an islamist extremist is. Tree-sitters, antivivisectionists, anti-abortion, etc. "extremism" could be harder to judge, and that could jack up the error rate considerably.
Ideas are possibly interesting but this approach doesn't help to attract more readers.
http://www.security-informatics.com/content/pdf/s13388-014-0...