If i no longer have access to feature in a data set should i retrain my model?
Naive Bayes, as far as I know, does not have any internal method of dealing with missing data (which is what you have). Thus, there are really two options:

1) Retrain the model, discarding the variable that contains the is author known variable. This method will probably make your model worse if the is author known variable is important, but at least you won't have any missing data to deal with.

2) Impute the missing values using some sort of algorithm, like MICE, mode imputation, knn, random forest, etc. For this problem, just from a first glance there might be correlations between x1 and your other variables that you can exploit to make a good guess for what x1 would be, given the other known values. This introduces more uncertainty in your model and might even make it worse then simply dropping x1 outright (option 1), but it is worth a shot if x1 is an important predictor.