Special Issue Article
An Analysis of Unwanted Messages Filtering Methods from OSN User Walls
One major problem in today’s Online Social Networks (OSNs) is to give users skill to regulate the messages posted on their own personal space to avoid that unauthorized data is displayed. OSNs give small support to these needs. In this thesis, i propose a system permit OSN users to have a straight control on the messages posted on their walls. it is achieved through a flexible rule-based system, that permit users to customize the filtering criteria to be put to their walls, and a Machine Learning-based soft classifier automatically labelling messages in endure of contentbased filtering. first conduct a set of large-scale measurements with a collection of accounts observe the difference among human, bot, and cyborg in terms of tweeting behavior, tweet content, and account properties. Our experimental evaluation demonstrates the efficacy of the proposed classification system and also we use pattern matching and text classification algorithm for accurate results. In computer science, pattern matching is the act of checking a perceived sequence of tokens for the presence of the constituents of some pattern. The patterns generally have the form of either sequences or tree structures. Uses of pattern matching include outputting the locations of a pattern within a token sequence, to output some component of the matched pattern, and to substitute the matching pattern with some other token sequence.