# 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!

• Can you clarify what you're asking? what have you tried and what is the result? what's the problem? Jul 15 '15 at 14:57
• There is no problem except this is my first task in machine learning. I just want to ensure that I have choosen right algorithm and, for example, should I normalize features? Also I am still confused whether it is correct to combine categorical and continuous features. Jul 15 '15 at 15:12