A Classification Mechanism To Avoid Useless Data From Osn Walls

The attempt of the present work is consequently to propose and experimentally estimate an automated system called Filtered Wall (FW) which is competent to filter unwanted messages from OSN user walls. One essential issue in today’s Online Social Networks (OSNs) is to give users the provision to control the messages posted on their own private space to shun that unwanted content is displayed. This is achieved through a flexible rule-based system that let users to adapt the filtering criterion to be applied to their walls and a Machine Learning-based soft classifier automatically labelling messages in support of content-based filtering. The unique set of description imitative from endogenous properties of short texts is distended here including exogenous knowledge connected to the context from which the messages create. As far as the learning model is apprehensive we confirm in the current paper the use of neural learning which is today documented as one of the well-organized solutions in text classification. In particular we base the overall short text classification strategy on Radial Basis Function Networks (RBFN) for their established potential in acting as soft classifiers in managing noisy data and essentially vague classes.