I have been reading a lot of literature regarding text classification and different approaches/models, especially using Python language, but probably I am still missing something on how to build the models and the steps involved.
I have multiple datasets, each of them on a particular topic. These datasets include news and fake news, manually flagged at the moment. I have collected texts from different sources about similar topics (using keywords) and now I would like to try to build a model that can allow me to classify a news as real or fake automatically.
I have thought that it could have been useful to study the frequency of words and punctuation, and also similarity, trying to group similar texts based on same conditions (for example, similarity between texts as in plagiarism). I have used similarity (Jaccard or cosine) for comparing texts rather than words, but I do not know if this is the right approach and how I should create a model based on that.
Probably the best method would be to build a logistic regression model using a binary variable, but I also read a lot of naive bayesian modes and nn models.
Do you have any example on how I should apply text similarity for classification or further relevant information about that?