ONLINE Social Networks (OSNs) are today one of the most popular interactive medium to communicate, share, and disseminate a considerable amount of human life information. The huge and dynamic character of these data creates the premise for the employment of web content mining strategies aimed to automatically discover useful information dormant within the data. A system allowing OSN users to have a direct control on the messages posted on their walls. A flexible rule-based system, that allows users to customize the filtering criteria to be applied to their walls. Machine Learning-based soft classifier automatically labeling messages in support of content-based filtering. The original set of features, derived from endogenous properties of short texts, is enlarged here including exogenous knowledge related to the context from which the messages originate. More complex filtering systems include multilabel text categorization automatically labeling messages into partial thematic categories. The extraction of an appropriate set of features by which representing the text of a given document is a crucial task strongly affecting the performance of the overall classification strategy. Content-based filtering is mainly based on the use of the ML paradigm according to which a classifier is automatically induced by learning from a set of preclassified examples. This work is the first step of a wider project. The early encouraging results we have obtained on the classification procedure prompt us to continue with other work that will aim to improve the quality of classification. In particular, future plans contemplate a deeper investigation on two interdependent tasks. The first concerns the extraction and/ or selection of contextual features that have been shown to have a high discriminative power. The second task involves the learning phase. Since the underlying domain is dynamically changing, the collection of preclassified data may not be representative in the longer term.