DocumentCode :
133942
Title :
An integrated approach to spam classification on Twitter using URL analysis, natural language processing and machine learning techniques
Author :
Kandasamy, Kamalanathan ; Koroth, Preethi
Author_Institution :
Amrita Center for Cyber Security, Amrita Vishwa Vidyapeetham, Kollam, India
fYear :
2014
fDate :
1-2 March 2014
Firstpage :
1
Lastpage :
5
Abstract :
In the present day world, people are so much habituated to Social Networks. Because of this, it is very easy to spread spam contents through them. One can access the details of any person very easily through these sites. No one is safe inside the social media. In this paper we are proposing an application which uses an integrated approach to the spam classification in Twitter. The integrated approach comprises the use of URL analysis, natural language processing and supervised machine learning techniques. In short, this is a three step process.
Keywords :
classification; learning (artificial intelligence); natural language processing; social networking (online); unsolicited e-mail; Twitter; URL analysis; natural language processing; social media; social networks; spam classification; spam contents; supervised machine learning techniques; Accuracy; Machine learning algorithms; Natural language processing; Training; Twitter; Unsolicited electronic mail; URLs; machine learning; natural language processing; tweets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical, Electronics and Computer Science (SCEECS), 2014 IEEE Students' Conference on
Conference_Location :
Bhopal
Print_ISBN :
978-1-4799-2525-4
Type :
conf
DOI :
10.1109/SCEECS.2014.6804508
Filename :
6804508
Link To Document :
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