Detecting spam users automatically with a neural network
Website builders – online hosting services that provide tools for non-technical users to build their own websites – are frequently exploited by spammers looking for a convenient launching pad. Checking thousands, or sometimes millions, of web pages manually to look for evidence of a spammer is both tedious and inefficient.
In this article, I show how to build a suitable spam-searching neural network with help from Google's TensorFlow machine learning library [2] [3] and TFLearn [4], a library with a high-level API for TensorFlow. Even if you don't spend your days searching for spammers, the techniques described in this article will give you some insights on how to harness the power of neural networks for other complex problems.
Training Day
The neural network needs both positive and negative samples in order to learn. This solution starts with a manually compiled list of sample users divided into spammers and legitimate users, taking care to distribute both types in equal numbers. Alongside this classification (spammer or not spammer), the data set contained the user's name or the website that belongs to the user, the IP address with which the site is registered, and the language version associated with the site.
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