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A Real Time Spam Classification of Twitter Data with Comparative Analysis of Classifiers


Author(s):

Sandeep Kumar Rawat , Sri Sai University; Saurabh Sharma, Sri Sai University

Keywords:

Twitter Spam Detection, API streaming, Text mining, Pre- processing, Classification, machine learning approaches BPNN classifier, Naive baye’s classifier

Abstract:

In today’s scenario, online networking is fast becoming popular among Internet users. The rise in the use of social networking sites such as twitter are gaining much importance because it plays a double role of online social networking and micro blogging but these sites have a constraint to them i.e. the spammers. Twitter is becoming a popular site in the face of micro-blogging service for the users to share short messages, called Tweet. The spamming fact widely spread-out in the tweets is now affecting micro blogs and exploits specific mechanisms of the messaging process. Spammers try to attack the trending topics over the twitter to spoil the useful content. Social spamming is more successful as compared to the email spamming as it uses social relationship between the users. Spam detection in real time is very important because Twitter is largely used for the commercial advertisements. The spammers attack the private information of the user. Spammers can be detected by using the content and user based attributes. In this paper we will try to classify the spam from real time twitter data using Twitter Streaming API, text mining and use the classifiers for spam classification.


Other Details:

Manuscript Id :IJSTEV2I12019
Published in :Volume : 2, Issue : 12
Publication Date: 01/07/2016
Page(s): 79-84
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