You can't formally prove this. However, intuitively, text representations are high dimensional (bag of words, n-grams, ...).

  The higher the dimensionality, the easier it is to linearly separate data, as the VC dimension of a linear classifier in $d$ dimensions is $d+1$ (e.g. see these slides).

Additionally, you should be aware that the linear kernel is equivalent to a degenerate RBF kernel, which means that with a properly tuned RBF kernel you should be getting at least the same accuracy as a linear kernel. That said, using an RBF kernel on such data is a waste of time and effort, because it typically offers very little improvement and tremendously increases computational cost.

You can't formally prove this, unless you happen to be able to fit a hard margin SVM on your data (unlikely). However, intuitively, text representations are high dimensional (bag of words, n-grams, ...). The higher the dimensionality, the easier it is to linearly separate data, as the VC dimension of a linear classifier in $d$ dimensions is $d+1$ (e.g. see these slides). The VC dimension is the largest amount of points that a classifier can shatter (separate).

Additionally, you should be aware that the linear kernel is equivalent to a degenerate RBF kernel, which means that with a properly tuned RBF kernel you should be getting at least the same accuracy as a linear kernel. That said, using an RBF kernel on such data is a waste of time and effort, because it typically offers very little improvement and tremendously increases computational cost.