Spam detection in social media I want to train a binary classification algorithm for spam detection using labeled data set of mentions from social media. These mentions have the following features:


  URL, text message, posting date, is deleted, author


where the author has its own fields:


  URL, nick, type (person or community), number of subscribers, registration date, last activity date


I have $\approx 13.5k$ observations with $\approx 450$ spam messages among them. 

My suggestions: 


transform data features in real numbers
binarize URLs by media sources (e.g. facebook, instagram, etc.)
use SVM with Gaussian kernel for all data


Questions: Am I on the right way? What else can I do? How can I process text data?

Thanks in advance for any suggestions!
The approach seems correct, don't worry. But I'd suggest to use Naive Bayes Classifier instead of SVM - it perfectly works for this task (and actually popular email services use this algorithm for spam detection).