A Language and an Inference Engine for Twitter Filtering Rules

We consider the problem of the filtering of Twitter posts, that is, the hiding of those posts which the user prefers not to visualize on his/her timeline. We define a language for specifying filtering policies suitable for Twitter posts. The language allows each user to decide which posts to filter out based on his/her sensibility and preferences. Since average users may not have the skills necessary to translate their filtering needs into a set of rules, we also propose a method for inferring a policy automatically, based solely on examples of the desired filtering behavior. The method is based on an evolutionary approach driven by a multi-objective optimization scheme. We assess our proposal experimentally on a real Twitter dataset and the results are highly promising.