Just using date of publication or last date of modification does not avoid the issue I described. In my brief reading I couldn't find a link or reference for your data beyond it coming from Kaggle somehow (might have missed more exact reference), but your sample is definitely not random (as you describe it has exactly 2000 real and 2000 fake examples, while a representative random sample would not be balanced). If the 2000 fake ones have 2016 publication dates and the 2000 real ones have 2017 dates, you haven't found a new optimal detection method nor that every article ever published in 2016 was fake, you've found some artifact of the dataset. Still an important finding, especially if other people are using that data and might be drawing wrong conclusions from it, but not a new misinformation detection method.

Of course, it's probably not such an extreme case like that (although something nearly that extreme has occurred in some widely used datasets, as explained in paper I linked). But here's a more subtle thought experiment: suppose fake articles were collected randomly from a fact-checking website (a not uncommon practice). Further, maybe that fact-checking website expanded its staff near the 2016 US election, say in October, because there was a lot of interest in and public need for misinformation detection at that time. More staff -> more articles checked -> more fake news detected -> a random sample of fake news from the website will contain more examples from October (when there was more staff) than September. So in the data then the month is predictive, but that will not generalize to other data.

A machine learning paper, whatever the audience, requires some guarantee of generalization. Since the metadata features used in your paper are known to be problematic in some datasets, and the paper only reports results on one dataset, in my opinion it cannot give confidence in generalization without some explanation "why."