An Unwanted Messages Filtering System from OSN User Walls using Blacklist Mechanism

نویسنده

  • G. Lavanya
چکیده

In This paper we proposes a content-based message filtering conceived and system enforcing machine learning as a key service for On-line Social Networks (OSNs). As we know, today everybody is using On-line Social Networks (OSNs) to communicate and share information. Then one important need in today On-line Social Networks (OSNs) is to give users the capability to control the messages posted on their own private space to avoid that unwanted content is displayed. OSNs provide little support to this requirement up to now. To provide this, we suggest a system allowing OSN users to have a direct control on the messages posted on their walls. This is accomplished through a flexible rule-based system, which allows users to customize the filtering criterion to be applied to their walls, and Machine Learning based soft classifier which automatically produces membership labels in support of content-based filtering. Finally OSN plays a vital role in day to day life. User can communicate with other user by sharing several types of contents like image, audio and video contents. Only the unwanted messages will be blocked not the user. To avoid this issue, BL (Black List) mechanism is proposed in this journal, which avoids undesired creators messages. KeywordsPattern matching, Information Filtering, On-line Social Networks, Text Classification, Policy-based Personalization and Blacklist ______________________________________________________________________________________________________

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تاریخ انتشار 2014