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Research Paper | Computer Science & Engineering | India | Volume 4 Issue 7, July 2015
Filtering Unwanted Post from Online Social Networking (OSN) Sites
Sachin P. Vidhate [2] | Syed Akhter [3]
Abstract: Online Social Network (OSN) which plays a most important role in our day to day life, one user can communicate with one or more users by sharing several types of information through various medium like audio, video and image. Major issue in Online Social Network is to prevent user for posting unwanted messages such as abuse words etc. There is need to give users the ability to control the messages posted on their own private/public wall or in inbox to avoid that unwanted content to be displayed. In todays available OSN System unwanted post will be directly posted on the public wall, to fill this gap, in this paper, I propose an automated system that automatically filters the undesirable messages by allowing Online Social Network users to have a direct control on the content posted on their walls. This is done through a flexible rule-based system, that allows users to set the filtering criteria to be applied to their walls, and a Machine Learning technique based soft classifier algorithm automatically labeling messages in support of content-based filtering. To do this, Black List (BL) mechanism is proposed in my system, which avoid undesired creators messages. BL is used to determine which user should be inserted in Black List and decide when the retention of the user is finished. Machine Learning Text Categorization (MLTC) is also used to categorize the short text messages.
Keywords: Online Social Network OSN, machine learning techniques, Black List BL, Machine Learning Text Categorization MLTC
Edition: Volume 4 Issue 7, July 2015,
Pages: 988 - 991