Our work proposes the use of topic taxonomies as part of a filtering language. Given a taxonomy, we train classifiers for every topic of it. The user is able to formulate logical rules combining the available topics, e.g., (Topic1 AND Topic2) OR Topic3, in order to filter related documents in a stream of documents. Using the classifiers, every document in the stream is assigned a belief value of belonging to the topics of the filter. These belief values are then aggregated using logical operators to yield the belief to the filter. In that framework, we are concerned with the operators that provide the best filtering performance for the user. In our study, Support Vector Machines (SVMs) and Naïve Bayes (NB) classifiers were used to provide topic probabilities. Fuzzy aggregation operators were tested on the Reuters text corpus and showed better results than their Boolean counterparts. Moreover, the application of Ordered Weighted Averaging (OWA) operators considerably improved the performance of fuzzy aggregation, especially in the case of NB classifiers. Finally, we describe a filtering system to exemplify the use of fuzzy filtering.