Numerically, they are similar to the border-split cross-validation results, but again with a different ranking of the feature combinations.
the size of the test data set was the same as in time-split (below) but the size of the training data set was 5,868. r time-split validation: We chose the cut-off time t * so that 90% of the data was used as training (5,922 tweets) and 10% as validation (658 tweets).