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You can see the curve of false positives and false negatives in the paper. As one example, finding half of the extremist recruiters involves about labeling abou
by rspeer 11y ago
You can see the curve of false positives and false negatives in the paper. As one example, finding half of the extremist recruiters involves about labeling about 2% of benign posts as extremist recruiters. It's not promising if you think that they'll automatically suspect people of being terrorists based on this classifier, but it could be used responsibly as a filter for a human to follow up on.
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.)
- Zigurd 11y ago2% false positives? That's terrible, because it's not scaled to the ratio of benign posts to recruiting posts. That is, at a 2% rate, false positives could, perhaps vastly, outnumber accurate hits. 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.