How to improve results in classification problems (SVM, Logistic Regression and MultiNaive Bayes)?
If you have a lot of data - down-sample your negative class to achieve 50/50 split on your fake news/real news classification. If you don't have much data - you can use techniques like SMOTE to up-sample the lesser class.
You seem to have better accuracy than randomly choosing fake/real which is a good sign. Your probability of a negative class based on your data split is 71.6% - and you are able to achieve 85.4% accuracy with LogReg. Don't get too down on that (especially if you are new to ML).
I would recommend checking out Gradient Boosting or Bagging algos if this is an NLP problem - these usually yield the best results for me when I'm encountered with sparse text data in classification.
As for the punctuation and stop words this is a common first step - however it's not good general advice for any problem. Do you think the presence of exclamation points might weed out some fake news in your data? If so I would include punctuation. If not - you're probably already on the right track. Removing stop words and punc only makes sense if the context of your specific problem calls for it.
More generally - your desire to reach 92% accuracy might not be possible given the difficulty of your problem. This is not to say it's not possible but keep in mind that the tutorials you may follow online are pre-determined to show that you can get good results. Some projects are simply harder than others (and some are not even possible given the context).
Good luck!