Modelling and Explaining Online News Preferences

We use Machine Learning techniques to model the reading preferences of audiences of 14 online news outlets. The models, describing the appeal of a given article to each audience, are formed by linear functions of word frequencies, and are obtained by comparing articles that became “Most Popular” on a given day in a given outlet with articles that did not. We make use of 2,432,148 such article pairs, collected over a period of over 1.5 years. Those models are shown to be predictive of user choices, and they are then used to compare both the audiences and the contents of various news outlets. In the first case, we find that there is a significant correlation between demographic profiles of audiences and their preferences. In the second case we find that content appeal is related both to writing style - with more sentimentally charged language being preferred, and to content with “Public Affairs” topics, such as “Finance” and “Politics”, being less preferred.