A System to Filter Unwanted Messages on Social Networking Site

Computer Engineering

Today we mostly using on-line Social Networks (OSNs) for sending the messages to one another but there is no any limitations of any type of message flow. In this project we will give the users the ability to control the message posted on their own private space to avoid that unwanted contend is displayed. This will be achieved through a flexible rule-based system, that allows user to customize the filtering criteria which will be applied on their wall and machine based soft classifier automatically labeling messages in support of contend based filtering. If such kind of posting of unwanted messages on user wall is done many times then system will be put automatically that user in to blacklist. This is achieved through a flexible rule-based system, that allows users to customize the filtering criteria to be applied to their walls, and a Machine Learning based soft classifier automatically labeling messages in support of content-based filtering. In content-based filtering each user is assumed to operate independently. As a result, a content-based filtering system selects information items based on the correlation between the content of the items and the user preferences as opposed to a collaborative filtering system. 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 pre-classified examples. The core components of the proposed system are the Content-Based Messages Filtering (CBMF) and the Short Text Classifier (STC) modules. The latter component aims to classify messages according to a set of categories. The strategy underlying this module is described in Section IV. In contrast, the first component exploits the message categorization provided by the STC module to enforce the FRs specified by the user. BLs can also be used to enhance the filtering process.

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