Let me say upfront that this is the early stages of this project and there is no money involved. If this goes well I might write a paper about it (though I have no idea where I would submit it).
Here is the basic premise of the problem:
Lots of young girls are used as sex slaves/prostitutes in the US. If we examine the ad space of young prostitutes can we help them?
More details:
Their pimps advertise the girls in many cities all across the country. The pimps might have girls in several cities or move girls between several cities.
These ads appear online in a variety of publications. These ads often come with telephone numbers and photographs.
The question:
If we scrapped ads from these websites what sort of useful analysis could we do?
So far this is what I have thought of:
Do cluster analysis on the text of the ads to see which ads were posted by the same person. Essentially this is an unsupervised learning algorthm being applied to the data of the ads (photo, text, date/time of posting, location, phone number). It was suggested that I use an LDE. Additionally, there is no need to actually decode the meaning of the ads at this time, but it could be interesting later.
Photo analysis to see which girls are operating in which cities. This has several different possible levels of success: Finding the same picture attached to different ads. Finding similar pictures, i.e., pictures that might have been taken in the same room. Finding the same girls in distinctly different pictures.
What help I would like from r/machinelearning:
How have problems like this been solved in the past. I imagine someone has done analysis on craigslist ads to similar effect, i.e., to find out which ads were posted by the same person. Probably in the name of fraud detection. Can someone point me to some papers on the topic? I am looking myself, but I wouldn't mind the help.
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