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