A Language and an Inference Engine for Twitter Filtering Rules
Alberto Bartoli, Barbara Carminati, Elena Ferrari,
IEEE/WIC/ACM International Conference on Web Intelligence (WI), held in Omaha (USA)
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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.