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I believe this could be interesting for many Facebook developers. Is there some reliable way of detecting fake profiles on Facebook? I am developing some games and applications for Facebook and have some virtual goods for sale. If player wants to play more he can create another profile or many others and play as much as he like. The idea here is to somehow detect this and stop them from doing so.

Best Regards!

amit
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Dusan Radojevic
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    This question doesn't belong here, since it's too broad and doesn't address a programming problem. But if there was a way to do this, facebook would delete those fake profiles. – Guntram Blohm Dec 25 '13 at 18:31
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    Did you read those tags before posting this question here on SO ? – P0W Dec 25 '13 at 18:32
  • @GuntramBlohm Why doesn't it address a programming problem? I can think of a few ways to solve this problem programmatically, and the question do ask how to do it as a part of a program. A programming question is not only "how to parse a string". Nothing is wrong in asking how to do something more complex, that can definetly be done programmatically. – amit Dec 25 '13 at 19:04

2 Answers2

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Put validation on no. of friends.. if no. of friends < A PARTICULAR THRESHOLD, disallow user, else continue. Well.. That's only an opinion, not a solution.. :)

Lavneet
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    Poor guy with no / few friends. Also, it's fairly easy to get around this by creating a bunch of profiles and having them friend each other, or just sending out random friend requests. – Bernhard Barker Dec 25 '13 at 23:30
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You can try using anomaly detection.

Make your 'features' number of likes/spam/friends/other relevant features you've found helpful, and use the algorithm to detect the anomalies.

Another approach could be supervised learning - but will require a labeled set of examples of "fake" and "real" users. The 'features' will be similar to these for anomaly detection.
Train your learning algorithm using the labeled set (usually referred as training set), and use the resulting classifier to decide if a new user is fake or not.

Some algorithms you can use are SVM, C4.5, KNN, Naive Bayes.

You can evaluate results for both methods using cross-validation (this requires a training set, of course)

If you want to learn more about machine learning approaches, I recommend taking the webcourse at coursera.

amit
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