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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Paper Title: Master Follower Method For Controlling Based Real-Time Detection Of Drifted Twitter Spam Messages
Authors Name: U.Prathiba , S.Jayasribanu , TRP.Monisha
Unique Id: IJSDR1803001
Published In: Volume 3 Issue 3, March-2018
Abstract: Generally Machine Learning has been used for Twitter Spam Detection,which uses statistical features of Tweets.Sometimes,the properties of tweets vary over time and thus result in “Twitter Spam Detection” occurs. To solve this problem we use Lfun scheme which discover the “changed” tweets from unlabeled tweets. In this paper, We propose:1)To block the unwanted words from tweets 2)Unfollow the spam followers. The unwanted words are clustered and stored under Big Data. In this scheme, the unwanted words from that tweet will be blocked and remove the user from their follower’s list.
Keywords: spam Detection, Machine Learning, Lfun scheme.
Cite Article: "Master Follower Method For Controlling Based Real-Time Detection Of Drifted Twitter Spam Messages", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.3, Issue 3, page no.1 - 4, March-2018, Available :http://www.ijsdr.org/papers/IJSDR1803001.pdf
Downloads: 000223214
Publication Details: Published Paper ID: IJSDR1803001
Registration ID:180038
Published In: Volume 3 Issue 3, March-2018
DOI (Digital Object Identifier):
Page No: 1 - 4
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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