Expand or compact features?
The way I see it is that your 10 sources of data, they all refer to the same set of people. Depending on the attributes, some can be expanded, some can be merged ...

Attributes such as age should be unique, so it doesn't make sense to expand it to Profile 1 age, profile 2 age ... One simple way is merge them is by using the average or use max. Expanding age only add redundant data to your feature matrix, and increase its dimensionality, in most cases, this doesn't help generalization performance of your model. 

On the other hand, number of followers can be expanded. Depending on the data source, a guy has 10 followers on Twitter but 1000 followers on Google+ might simply mean that he barely uses Twitter. 

That being said, the way you pick your features or engineer new features should increase your model performance, so if expanding number of followers actually decrease Cross Validation or Test performance, compared to the one using sum of followers then you can simply use sum of followers.