Filtering spam from retrieved data
Spam filtering, especially in email, has been revolutionized by neural networks, here are a couple papers that provide good reading on the subject:

On Neural Networks And The Future Of Spam 
A. C. Cosoi, M. S. Vlad, V. Sgarciu 
http://ceai.srait.ro/index.php/ceai/article/viewFile/18/8

Intelligent Word-Based Spam Filter Detection Using 
Multi-Neural Networks 
Ann Nosseir, Khaled Nagati and Islam Taj-Eddin
http://www.ijcsi.org/papers/IJCSI-10-2-1-17-21.pdf

Spam Detection using Adaptive Neural Networks: Adaptive Resonance Theory 
David Ndumiyana, Richard Gotora, and Tarisai Mupamombe
http://onlineresearchjournals.org/JPESR/pdf/2013/apr/Ndumiyana%20et%20al.pdf

EDIT:
The basic intuition behind using a neural network to help with spam filtering is by providing a weight to terms based on how often they are associated with spam.

Neural networks can be trained most quickly in a supervised -- you explicitly provide the classification of the sentence in the training set -- environment.  Without going into the nitty gritty the basic idea can be illustrated with these sentences:

Text = "How is the loss of the Viagra patent going to affect Pfizer", Spam = false
Text = "Cheap Viagra Buy Now", Spam = true
Text = "Online pharmacy Viagra Cialis Lipitor", Spam = true

For a two stage neural network, the first stage will calculate the likelihood of spam based off of if the word exists in the sentence.  So from our example:

viagra => 66%
buy => 100%
Pfizer => 0%
etc..

Then for the second stage the results in the first stage are used as variables in the second stage:

viagra &amp; buy => 100%
Pfizer &amp; viagra=> 0%

This basic idea is run for many of the permutations of the all the words in your training data.  The end results once trained is basically just an equation that based of the context of the words in the sentence can assign a probability of being spam.  Set spamminess threshold, and filter out any data higher then said threshold.