Statistical comparison of model performance when training and validation data is always the same
If your model is deterministic (no randomness), then repeating the training/testing on the exact same set of data is pointless - you will get the exact same answer every time. The benefit of cross-validation is that it provides an unbiased estimate of your model performance, and does so by using different perturbations of the train/test data. You can still do something similar, selecting 80% of your training data and testing on some subset of the test data, and repeatedly doing a resampling. There's a slight difference from traditional CV, where your train/test set are mutually exclusive and essentially define one another, whereas in this case, your training and test datasets can be defined totally independently (but that shouldn't be a problem).

Incidentally, what model are you using that you expect can accurately predict data that's completely unlike what it's been trained on? Usually the point of the training data is to provide the model with examples of the data you expect to see, along with the correct output. It's not clear to me why you'd want to train a model to classify tweets in English, but then test how it performs at classifying Dutch tweets - the model has never seen Dutch, so I don't expect it would perform well. It seems this evaluation would test how similar English and Dutch are, rather than testing how good your classification model is.